fornix stimulation, effects on hippocampal oscillations
TRANSCRIPT
Fornix stimulation, effects on hippocampal oscillations and memory in an AD mouse model
Eva Vico Varela
Integrated Program in Neuroscience
McGill University
Montreal, Quebec, Canada
April 2020
A thesis submitted to McGill University in partial fulfillment of the requirements of the
degree of Doctor of Philosophy, Ph.D. © Eva Vico Varela 2020
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Abstract
Episodic memory depends on the integrity of the hippocampus and structures with
which it connects. Episodic memory is known to decline with age, but even more so in
progressive dementias such as Alzheimer’s disease (AD). Problems with episodic
memory generally follow the course of damage to the memory circuit in AD, with early
changes appearing in the hippocampus, a structure that is common across species.
Despite all efforts, there are currently no treatments that stop or reverse disease
progression in AD patients. Stimulation of the fornix, a major pathway connecting the
hippocampus with other essential memory areas, such as the medial septum, has the
potential of modulating hippocampal activity and enhance memory in disease. In AD
patients, the effect of fornix stimulation in cognitive measures has been variable, and
little is known about its mechanism of action. The work included in this thesis
investigates early oscillatory alterations in the hippocampus of freely-moving J20+ mice
(an amyloid precursor protein-overexpressing mouse model of AD), and how fornix
stimulation may modulate existing abnormal hippocampal activity and hippocampal-
dependent memory impairments. We focus our analysis on prominent hippocampal
oscillations, such as theta (4-10 Hz) and its coupling to gamma (30-120 Hz), which have
been linked to memory processes and occur during specific brain states.
In chapter 2, we examine early hippocampal rhythmic differences between non-
transgenic littermates and AD mice in wake and sleep periods. Following this, we
explore hippocampal hyperactivity during sleep in AD, and its correlation with memory
deficits. We demonstrate that during wake and rapid eye movement sleep, the peak of
theta frequency is lower in J20+, while theta- low gamma phase- amplitude coupling is
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reduced in wake periods only. Moreover, hyperactive events in the form of spike-wave
discharges are present only in AD mice, predominate during sleep and are particularly
prominent in rapid eye movement sleep. Lastly, the incidence of these aberrant
synchronous events correlates with memory performance in two hippocampal-
dependent tasks, novel place object recognition and passive avoidance. Thus, we
demonstrate that memory-associated early alterations can be detected in the
hippocampal activity of AD mice in a state-dependent manner. In chapter 3, we
investigate the effect of stimulating septo-hippocampal projections in these oscillatory
signatures using optogenetics. While activating septal fibers at the level of the fornix,
changes in hippocampal rhythms and hyperactivity are studied during wake and sleep,
together with the effect of stimulation on memory. The results reported here suggest
that GABAergic projections from the septum entrain hippocampal rhythms, lower
aberrant hyperactivity and improve memory performance in deficient mice. Finally, in
chapter 4, we show that electrical deep brain stimulation, approved for clinical use in AD
patients, is also able to enhance memory in J20+ when low, but not high, frequency
stimulation is delivered to the fornix. Our findings highlight differences between
stimulation paradigms for the modulation of memory.
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Résumé
La mémoire épisodique est dépendante de l'intégrité de l'hippocampe ainsi que de ses
structures connexes. Il est connu que ce type de mémoire se détériore avec l'âge, mais
d'autant plus dans le cas de démences progressives telles que la maladie d'Alzheimer
(MA). Dans la MA, les déficits de la mémoire épisodique sont généralement
temporellement corrélés aux dommages des circuits mnésique, avec les changements
les plus précoces apparaissant dans l'hippocampe, une structure commune à de
nombreuses espèces. Malgré de nombreux efforts, il n'existe à l'heure actuelle aucun
traitement pour arrêter ou améliorer la progression de la maladie chez les patients de la
MA. Le fornix est une voie majeure de connection entre l'hippocampe et d'autres
régions essentielles au traitement mnésique, telles que le septum médian, et la
stimulation de cette voie a le potentiel de pouvoir moduler l'activité de l'hippocampe et
d'améliorer la mémoire. Dans les patients atteints de la MA, les effets de la stimulation
du fornix sur les performances cognitives sont variables, et les mécanismes d'action
restent inconnus. Ce travail de thèse examine les altérations oscillatoires dans
l'hippocampe de souris J20+ (un modèle murin qui surexprime l'APP) libres de
mouvement, et la manière dont les stimulations du fornix pourraient moduler l'activité
hippocampique anormale ainsi que les troubles mnésiques hippocampes-dépendants.
Nous avons concentré notre analyse sur les principales oscillations de l'hippocampe,
telles que le thêta (4-10 Hz) et sont couplage au gamma (30-120 Hz), qui ont été liées
aux processus mnésiques et surviennent dans des états cérébraux spécifiques.
Dans le chapitre 2, nous examinons dans le sommeil et l'éveil les différences précoces
de rythmes hippocampiques entre souris MA et leurs contrôles non-transgéniques.
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Ensuite, nous explorons l'hyperactivité hippocampique pendant le sommeil dans la MA,
ainsi que sa corrélation avec les troubles de la mémoire. Nous démontrons que pendant
l'éveil et durant le sommeil paradoxal, la fréquence de pointe du thêta est diminuée
dans les souris J20+, alors que le couplage du gamma bas au thêta est seulement
réduit dans les périodes d'éveil. De plus, les événements d'hyperactivité qui prennent la
forme de décharges 'pointe-onde' sont seulement présents dans les souris MA, et
prédominent le sommeil, avec une prédominance pour le sommeil paradoxal. Enfin,
l'incidence de ces événements anormalement synchrones est corrélée avec la
performance mnésique dans deux tâches hippocampe-dépendantes: la reconnaissance
d'objet spatiale et la tâche d'évitement passif. En conséquence, nous démontrons que
les déficits précoces de la mémoire peuvent être détectés dans l'activité hippocampique
des souris MA en fonction de l'état cérébral. Dans le chapitre 3, nous avons examiné
l'effet des stimulations septo-hippocampiques sur ces événements oscillatoires en
utilisant l'optogénétique. Pendant l'activation des fibres septales au niveau du fornix,
des changements au niveau des rythmes hippocampiques et de l'hyperactivité sont
étudiés pendant l'éveil et le sommeil, ensemble avec les effets des stimulations sur la
mémoire. Les résultats rapportés ici suggèrent que les projections GABAergiques du
septum entraînent les rythmes hippocampiques, diminuent l'hyperactivité aberrante, et
améliorent la mémoire dans les souris déficientes. Enfin, dans le chapitre 4, nous
montrons que les stimulations cérébrales profondes électriques, qui ont été approuvées
pour usage clinique chez les patients atteints de la MA, sont également capables
d'améliorer la mémoire chez les souris J20+ seulement lorsque des motifs de
stimulation à basse fréquence, mais pas à haute fréquence, sont appliqués au fornix.
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Nos résultats soulignent l'importance du choix du motif de stimulation dans la
modulation de la mémoire.
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Acknowledgements
Many have accompanied me throughout the years and their help, support and
continued presence have allowed me to complete this thesis work. First and foremost is
my supervisor Sylvain Williams (PhD.) who gave me the opportunity to learn and grow
as a scientist in the work environment he created. In his laboratory I have enjoyed
scientific freedom and been exposed to a range of exciting techniques that have
considerably expanded my scientific horizons. I specially appreciate the opportunity to
take on new experimental and analytical challenges, which has been incredibly
stimulating and fulfilling. Sylvain has assembled a very talented group of people with
whom working with has been a privilege, and their perspective, invaluable. Thank you to
Richard Boyce (PhD.), Jennifer Robinson (PhD.), Ning Gu (PhD.), Bénédicte Amilhon
(PhD.), Guillaume Ducharme (PhD.), Frédéric Manseau (PhD.), Siddhartha Mondragon
(PhD.), Heather Nichol (MSc.), Jean-Bastien Bott (PhD.), Guillaume Etter (PhD.), Amy
Chee (PhD.), Jun Kang (PhD.), Lorène Penazzi (PhD.), Ingrid Inema (MSc.), Suzanne
van der Veldt (MSc.), Coralie-Ann Mosser (PhD.), Jisoo Choi (MSc.) and Ke Cui (BSc.).
A special thank you to Richard Boyce, Jennifer Robinson, Guillaume Etter, and Jean-
Bastien Bott for their mentorship and support during these years.
Thank you to my committee members, Mallar Chakravarty (PhD.) and Mark Brandon
(PhD.), and to my IPN mentor Erik Cook (PhD.) who have taken their time to offer me
guidance and counsel. Exchanging thoughts and ideas during my committee meetings
has been extremely useful and has contributed greatly to this work. Lastly thank you to
my proposal and thesis examiners for your time, and thoughtful comments and
questions.
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Contributions
Eva Vico Varela performed and analyzed all immunohistochemistry and in vivo
experiments described in this thesis, with the following exceptions: tetrode implants and
tetrode cluster cutting was performed by Johnson Ying, and the 16ch probe implant was
performed by Guillaume Etter. In vitro patch clamp data acquisition and analysis was
performed by Frederic Manseau. Writing of the thesis, including figure construction
(unless stated otherwise), was completed by Eva Vico Varela. GE and Sylvain Williams
contributed to and edited section 1.4 in chapter 1, which was published with some
modifications in Neurobiology of Disease (Vico Varela et al., 2019). GE translated the
thesis abstract to French. Amy Chee and SW provided many helpful revisions and
comments on the entirety of this work.
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Table of Contents
Abstract ............................................................................................................................ii
Résumé ...........................................................................................................................iv
Acknowledgements ........................................................................................................ vii
Contributions ................................................................................................................. viii
Table of Contents ............................................................................................................ix
List of figures .................................................................................................................. xii
Foreword: General overview of thesis ........................................................................... xiv
Chapter 1: Introduction .................................................................................................... 1
1.1. Hippocampal anatomy and circuit elements .......................................................... 1
1.1.2. Hippocampal projections................................................................................. 4
1.2. Major Hippocampal activity patterns ..................................................................... 6
1.2.1. Theta oscillations ............................................................................................ 7
1.2.2. Gamma oscillations ...................................................................................... 11
1.2.3 Sharp-wave ripple complexes ........................................................................ 12
1.3. Sleep and memory consolidation ........................................................................ 14
1.3.1 Differences between REM and NREM sleep ................................................. 15
1.3.2. REM sleep and memory ............................................................................... 17
1.4. Alterations in hippocampal activity and Alzheimer’s Disease* ............................ 19
1.4.1. Introduction ................................................................................................... 19
1.4.2. Abnormal rhythmic activity in AD .................................................................. 21
1.4.3. Abnormal network activity in AD promotes epileptiform activity .................... 23
1.4.4. Excitatory Disruption and Hyperactivity ........................................................ 27
1.4.5. Inhibitory Disruption ...................................................................................... 33
1.4.6. Targeting cognitive dysfunction by circuit modulation ................................... 38
1.4.7. Conclusions and Outlook .............................................................................. 43
1.5. Hypothesis and Aims .......................................................................................... 45
Chapter 2: Characterization of early hippocampal dysfunction and spatial memory in
Alzheimer’s Disease ...................................................................................................... 46
2.1. Introduction ......................................................................................................... 46
2.2. Methods .............................................................................................................. 48
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2.2.1. Animals ......................................................................................................... 48
2.2.2. Electrode implantation for in vivo experiments .............................................. 48
2.2.3. Post-surgery habituation and electrophysiological recordings ...................... 49
2.2.4. Novel object place recognition procedure and behavioral analysis ............... 50
2.2.5. Passive avoidance procedure and behavioral analysis ................................ 51
2.2.6. Vigilance state architecture analysis ............................................................. 52
2.2.7. Electrophysiological analysis of in vivo CA1 LFP and unit data .................... 53
2.2.8. SWD state-specific analysis of CA1 LFP ...................................................... 54
2.2.9. Ripple analysis .............................................................................................. 54
2.2.10. Phase-amplitude coupling........................................................................... 55
2.2.11. Statistical analysis ...................................................................................... 55
2.2.12. Histological confirmation of electrode/optic fiber placement ....................... 56
2.3. Results ................................................................................................................ 56
2.3.1. Time spent in REM, wake, and NREM does not differ in J20+ transgenic AD
mice compared to non-transgenic littermate control animals .................................. 56
2.3.2. Shift in hippocampal theta peak frequency in AD ......................................... 57
2.3.3. Hippocampal hyperactivity during sleep in AD .............................................. 64
2.3.4. Performance impairments during behavior in AD ......................................... 74
2.4. Discussion ........................................................................................................... 77
2.4.1. Deficits in theta frequency and theta-gamma coupling ................................. 78
2.4.2. Hyperactivity during sleep and its link to memory ......................................... 79
Chapter 3: Effects of optogenetic activation of septo-hippocampal fibers in AD. .......... 84
3.1. Introduction ......................................................................................................... 84
3.2. Additional Methods ............................................................................................. 85
3.2.1. Virus-mediated targeting of opsin and eYFP expression .............................. 85
3.3.2. Optic fiber implantation for in vivo experiments ............................................ 86
3.3.3. Light delivery to the fornix ............................................................................. 87
3.2.4. Immunohistochemistry and construct expression confirmation ..................... 87
3.3.5. In vitro patch-clamp electrophysiology. ......................................................... 88
3.3. Results ................................................................................................................ 90
3.3.1. Optogenetic targeting of medial septum neurons ......................................... 90
3.3.2 Characterizing the effect of theta stimulation of MS projections in hippocampal
rhythms ................................................................................................................... 93
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3.3.3. Modulation of SWDs by fornix optogenetic activation ................................... 99
3.3.4. Modulation of hippocampal memory by fornix optogenetic activation ........ 107
3.4. Discussion ......................................................................................................... 110
3.4.1. Stimulation of septo-hippocampal projections modulates hippocampal
rhythms ................................................................................................................. 110
3.4.2 Stimulation of septo-hippocampal projections decreases SWDs in a state-
dependent manner ................................................................................................ 112
3.4.3 Post-training stimulation improves spatial memory in AD ............................ 114
3.5 Supplementary Data .......................................................................................... 117
Chapter 4: Fornix deep brain theta-burst stimulation improves memory performance in
an Alzheimer’s disease mouse model ......................................................................... 122
4.1. Introduction ....................................................................................................... 122
4.2. Methods ............................................................................................................ 124
4.2.1. Surgical coordinates ................................................................................... 124
4.2.3 Post-training stimulation paradigm ............................................................... 124
4.2.4. Passive avoidance procedure. .................................................................... 124
4.2.5. Novel object place recognition procedure (NOPR) procedure. ................... 125
4.3. Results .............................................................................................................. 125
4.3.1. Passive avoidance memory is improved after fornix theta-burst stimulation in
J20+ mice. ............................................................................................................ 125
4.3.2. Novel object place recognition memory improves after fTBS in J20+ mice. 126
4.4. Discussion ......................................................................................................... 130
4.4.1. Possible therapeutic mechanisms of DBS in memory circuitry ................... 131
4.5. Supplementary Information ............................................................................... 135
Chapter 5: Concluding remarks ................................................................................... 138
References .................................................................................................................. 144
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List of figures
Chapter 1:
Fig. 1.1: The hippocampus.. ............................................................................................ 4
Fig. 1.2: The fornix. ......................................................................................................... 5
Fig. 1.3: Neural activity patterns associated with wake and sleep behavior in the human
and rodent brain. ........................................................................................................... 15
Fig. 1.4: Earliest recorded Spike Wave Discharges (SWDs) in AD models ................... 27
Fig. 1.5: Schematic of neural network in normal and AD conditions. ............................ 32
Chapter 2:
Fig. 2.1: Sleep Architecture in controls and J20+ mice. ................................................ 57
Fig. 2.2: Theta frequency decreased in J20+ during REM. ........................................... 61
Fig. 2 3: Theta frequency decreased in J20+ during wake and impaired theta-low
gamma coupling.. .......................................................................................................... 62
Fig. 2.4: Spike-wave discharges are specific to J20+ mice. .......................................... 66
Fig. 2.5: Spike-wave discharge characterization in sleep. ............................................. 68
Fig. 2.6: Unit activity in J20+ mice. ................................................................................ 70
Fig. 2.7: Example PSTHs.. ............................................................................................ 71
Fig. 2.8: PSTHs of all units classified by SWD response .............................................. 74
Fig. 2.9: Spatial memory impairments in J20+ mice ...................................................... 77
Table 2.1: Analysis of CA1 cell layer LFP ripple characteristics during NREM.. ........... 63
Supplemental Fig. 2.1: .................................................................................................. 83
Chapter 3:
Fig. 3.2: Optogenetics and MS neurons.. ...................................................................... 91
Fig. 3.1: In vitro characterization of AAV2-ChETA in medial septum neurons.. ............. 93
Fig. 3.3: Optogenetic driving of theta frequency in the hippocampus of J20+ during REM
sleep. ............................................................................................................................. 97
Fig. 3.4: Optogenetic driving of theta frequency in the hippocampus of J20+ mice during
wake.. ............................................................................................................................ 98
Fig. 3.5: Stimulation of fornix fibers does not alter sleep architecture in J20+.. ............. 99
Fig. 3.6: Effect of fornix stimulation in spike-wave discharge numbers ....................... 103
Fig. 3. 7: Effect of fornix stimulation in spike-wave discharge characteristics.. ........... 105
Fig. 3.8: Effect of fornix stimulation on hippocampal single unit activity. ..................... 107
Fig. 3. 9: Spatial memory impairments in J20+ mice ................................................... 109
Table 3. 1: Analysis of CA1 cell layer LFP ripple characteristics during NREM. ........... 97
Table 3.2: REM length -SWDs .................................................................................... 117
Table 3.3: REM segment -SWDs ................................................................................ 117
Table 3.4: NREM length -SWDs .................................................................................. 118
Table 3.5: NREM segment -SWDs .............................................................................. 119
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Table 3.6: Control behavioural measures .................................................................... 119
Table 3 7: Control behavioural measures .................................................................... 120
Table 3 8: Control behavioural measures .................................................................... 121
Chapter 4:
Fig. 4.1: Memory improves following fTBS in J20 mice ............................................... 129
Fig. 4.2: Novel object place recognition (NOPR) control measures. ........................... 130
Table 4.1: Passive Avoidance additional statistics. ..................................................... 135
Table 4. 2: Corresponds to data shown in Fig.4-1 F. .................................................. 136
Table 4. 3: Novel object place recognition (NOPR) additional statistics. ..................... 137
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Foreword: General overview of thesis
This thesis describes original research work carried out by the author which
investigate whether stimulation of the fornix modulates hippocampal activity, decreases
hippocampal hyperactivity, as well as restores memory performance in a rodent model
of Alzheimer’s disease. Accordingly, the first chapter contains an overview of
background literature integral to this thesis. First, a description of the basic
neuroanatomy of the hippocampus and its extensive connections through the fornix is
included. A more detailed presentation of major hippocampal electrophysiological
oscillations and their implication for memory is also provided, as these are a significant
focus of the current thesis. Next, since the majority of analyses performed in this thesis
are state-dependent, a general summary of wake and sleep behaviors and the latter’s
relevance for memory consolidation is included. The last sections of the introduction
review the role of aberrant hyperactivity in the cognitive deficits characteristic of
Alzheimer’s disease, and explores the mechanisms contributing to this altered
excitatory-inhibitory balance early in the disease progression. Although already
published (Vico Varela et al., 2019), these last sections have been modified here mainly
to include relevant literature pertaining to changes in rhythm dynamics in Alzheimer’s
disease. The chapter concludes with a statement of the rationale and specific
hypotheses of the thesis work.
In the second chapter, I describe early state-dependent alterations found in
hippocampal oscillatory patterns of freely-moving Alzheimer’s disease mice. First,
hippocampal rhythmic differences are assessed in Alzheimer’s disease and age-
matched controls, followed by a characterization of hippocampal hyperactivity in sleep,
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and a final assessment of spatial-memory consisting of the spatial novel object place
recognition and passive avoidance tasks. The results presented here serve as an
essential foundation in understanding how early existing deficits in our AD model
contribute to memory deficits.
In the third chapter, I present the effects of optogenetic stimulation of septo-
hippocampal forniceal projections on the altered hippocampal measures described in
our AD model in the previous chapter. The first section shows the specificity and
efficacy of optogenetic stimulation of neurons within the medial septum. The following
sections are laid out as in chapter 2, where the effects of optogenetic fornix stimulation
on hippocampal rhythms are assessed during different in vivo states. Finally, I present
data on how optogenetic fornix stimulation modifies network hyperactivity in the
hippocampus as well as performance in two spatial memory tasks.
Lastly, the fourth chapter explores the effect of non-specific fornix deep brain
stimulation, which is considered as a potential therapeutic treatment in AD patients. I
assess hippocampal-dependent memory after deep brain stimulation with two distinct
stimulation paradigms and find that stimulation patterns that more closely resemble
physiological frequencies have more significant enhancing effects on memory
performance. Finally, a general discussion is provided in chapter 5.
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Chapter 1: Introduction
1.1. Hippocampal anatomy and circuit elements
As one of the most studied areas in the brain, the hippocampus is anatomically
and functionally conserved across mammalian species and plays a critical role in
memory. Three distinct subregions can be distinguished: the dentate gyrus (DG), the
hippocampus proper (consisting of CA3, CA2 and CA1) and the subiculum (Fig. 1.1 A-
B). Within the CA regions, the cortex forming the hippocampus can be subdivided into
three main layers. The deep layer, or the stratum oriens, is comprised of the basal
dendrites of pyramidal cells and a mixture of afferent and efferent fibres. Superficial to
the stratum oriens is the cell layer, or stratum pyramidale, which is composed of the
somas of principal cells. The most superficial layer is subdivided into several sublayers,
mainly comprising the apical dendrites from the pyramidal layer. In CA3, three sublayers
are distinguished: the stratum lucidum, a thins stratum where Mossy fiber inputs from
the dentate gyrus terminate, the stratum radiatum and, most superficially, the stratum
lacunosum-moleculare. The lamination in CA2 and CA1 is similar, with the exception
that the stratum lucidum is missing (Andersen et al., 2007). Although pyramidal cell
somas are located solely in the stratum pyramidale, interneurons can be found in any of
the layers, wherein they finely modulate the output of principal neurons (Klausberger
and Somogyi, 2008).
The hippocampal circuit is most often described as a trisynaptic loop, where
information flows serially and unidirectionally (Fig. 1.1 C). In this classical scheme, the
neocortex projects to the parahippocampal region, consisting of the rhinal cortices,
which in turn provides the primary source of input to the hippocampus. One of these
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rhinal regions, the entorhinal cortex, is the source of the perforant pathway, which
projects to all hippocampal subregions. Specifically, layer II of the entorhinal cortex
projects to the DG and CA3, whereas layer III projects to CA1 and the subiculum,
synapsing at the level of the stratum lacunosum. The DG granule cells themselves give
rise to the mossy fiber pathway, which targets the stratum lucidum of CA3. Next, the
CA3 area gives rise to the Schaffer collaterals which project to CA1 across the stratum
radiatum and then CA1, in turn, projects to the subiculum. Finally, output from the
hippocampus arises in CA1 and the subiculum and is directed to the parahippocampal
region, in particular to the deep layers of the EC (Van Strien et al., 2009).
The trisynaptic circuit is an oversimplification of connectivity in the hippocampus,
and in contrast to what is depicted in the classical model, projections are neither serial
nor restricted to specific layers. In fact, several backprojections exist in the
hippocampus. For example, pyramidal cells in CA3 project back to the DG (Buckmaster
et al., 1993; Laurberg, 1979; Wittner et al., 2007, 2006), interneurons from CA1 oriens
and radiatum project to the same layers in CA3 (Cenquizca and Swanson, 2007;
Laurberg, 1979; Swanson et al., 1981) and the subiculum also backprojects to all layers
of CA1 and CA3 (Finch et al., 1983; Jackson et al., 2014; Köhler, 1985). Moreover,
recurrent collaterals which are generally viewed as a characteristic of CA3 (Buckmaster
et al., 1993; Laurberg, 1979; Sik et al., 1993) have also been described in other
hippocampal subregions. Although less extensive than in CA3, recurrent collaterals
have also been reported in the DG (Laurberg and Sørensen, 1981; van Groen and
Wyss, 1988), CA1 (Amaral et al., 1991; Cenquizca and Swanson, 2007; Van Groen and
Wyss, 1990). and the subiculum (Finch et al., 1983; Harris et al., 2001; Köhler, 1985).
3
Finally, principal cells and interneurons also innervate other hippocampal areas
contralaterally through the hippocampal commissure (also called the commissure of the
fornix), and these projections have been described as originating from the dentate, CA3,
CA2 and CA1 (Eyre and Bartos, 2019; Shinohara et al., 2012; Sun et al., 2014; Zhou et
al., 2017).
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1.1.2. Hippocampal projections
The hippocampus not only sends and receives projections through the angular
bundle to the entorhinal cortex and other parahippocampal regions, but also to a myriad
of subcortical structures, primarily through the fornix (Andersen et al., 2007). Since the
work presented here is mostly concerned with projections passing though the fornix, the
following discussion will give an overview of fornix connectivity.
The fornix is the most extensive single pathway linking the hippocampus with
distal brain sites (Fig. 1.2). This fiber system is given different names at different points
in its trajectory from or towards anterior and posterior areas of the brain (Andersen et
al., 2007; Mark et al., 1993). Starting from the hippocampus, the alveus, which covers
the deep surface of the hippocampus, is a thin sheet of intrahippocampal as well as
afferent and efferent fibers covering the deep surface of the hippocampus. These
afferent and efferent projections collect laterally in a bundle named the fimbria, which
contains around 900.000 axons in the rodent (Wyss et al., 1980). The fimbria then
Fig. 1.1: The hippocampus. Nissl stained coronal views of A) mouse and B) human
brain sections from the Allen Brain Institute. Insets shows zoomed view of right
hippocampus. HPC: hippocampus; ff: fimbria-fornix; fim: fimbria alv: alveus; DG: dentate
gyrus; or: oriend; pyr: pyramidale; lu: lucidum; rad: radiatum; lm: lacunosum. C.
Schematic of the classical trysinaptic hippocampal circuit (entorhinal cortex (EC)–
dentate gyrus–CA3–CA1–EC) is depicted by solid arrows. MPP: medial perforin-path;
LPP: lateral performant path. Adapted from (Deng et al., 2010).
5
proceeds to gives rise to the fornix. To emphasize the continuity of fibers in these
bundles and since the exact transition between fimbria and fornix is challenging to
define, the term fimbria-fornix is usually employed. The dorsal part of the fornix lies just
below the corpus callosum, very close to the midline. As the fibers of the fimbria leave
the hippocampus and rostrally descend into the forebrain at the level of the anterior
commissure, they are referred to as the columns of the fornix. At this level, it divides into
the precommissural and postcommisural branches to interconnect the hippocampus
with an array of subcortical sites. The more rostral precommisural fornix innervates the
Fig. 1.2: The fornix. Diagram of the fornix and the major structures it connects to the
hippocampus. The hippocampi illustrate the location of the fornix in the primate and
rodent brain. Pre. Com: pre-commisural fornix; Post. Com: post-commisural fornix; AC:
anterior commissure; N.ACC: nucleus accumbens; preoptic area; RE: nucleus reuniens;
ATN: anterior thalamic nuclei. MB: mamillary bodies.
6
septal and preoptic nuclei, and nucleus accumbens (Kelley and Domesick, 1982;
Mathiasen et al., 2019; Saunders and Aggleton, 2007; Swanson and Cowan, 1979).
The caudally directed postcommisural fornix extend towards the diencephalon and
connects to the anterior nucleus of the thalamus, nucleus reuniens, locus coeruleus,
raphe nuclei, as well as to hypothalamic areas such as the mamillary nuclei (Bokor et
al., 2002; Bubb et al., 2017; Dillingham et al., 2015; Pasquier and Reinoso-Suarez,
1978; Saunders and Aggleton, 2007; Swanson and Cowan, 1975). Although many of
the projections mentioned above are bidirectional, those coming from the brain stem
(Mokler et al., 1998; Pasquier and Reinoso-Suarez, 1978) or going towards the nucleus
accumbens (Kelley and Domesick, 1982) appear unidirectional.
1.2. Major Hippocampal activity patterns
Brain rhythms are periodically fluctuating waves reflecting the electrical activity
produced by the synchronous fluctuations of large groups of neurons. These neural
oscillations can be measured using electroencephalogram (EEG) and local field
potential (LFP) recording techniques and can be used to detect general arousal patterns
in mammals, such as fundamental sleep and waking behaviors. In addition, local
rhythmic oscillations have been linked to many functions within the brain (Kahana,
2006; MacKay, 1997; Schnitzler and Gross, 2005). Of relevance to the present work,
these synchronous neural events are a critical component in current theories of learning
and memory which will be discussed in subsequent sections. One of the most
prominent features of the hippocampus is the presence of several patterns of oscillatory
activity related to cognitive functions. The hippocampal LFP shows three main rhythms
7
which can be distinguished by their frequencies: theta (~4-10 Hz), gamma (~30-120 Hz)
and sharp-wave ripples (Buzsáki, 2002; Colgin, 2016).
1.2.1. Theta oscillations
Theta oscillations are relatively low frequency sinusoidal waves occurring during
periods of active wakefulness and rapid eye movement (REM) sleep (Winson, 1978).
These oscillations have been detected in several species ranging from rabbits to
humans (Aghajan et al., 2016; Jutras et al., 2013; Nokia et al., 2009), and can be
measured in cortical and subcortical areas (Holsheimer, 1982; Kocsis, 2006). They are
the most widely studied rhythm in the rodent hippocampus. This is due to the accessible
location of the rodent hippocampus, as it lies just below the dorsal cortical surface of the
brain, where large amplitude continuous theta can be clearly recorded.
1.2.1.1. The medial septum and hippocampal theta
As seen in section 1.1.2, the fornix connects many key memory regions to the
hippocampus, of which the medial septum is the most relevant for the present work. The
medial septum (MS) is a structure shaped like an inverted “Y” and is positioned centrally
across the midline, framed by the lateral ventricles and the anterior portion of the
anterior commisure. The MS receives input from several regions including the
hypothalamus, brain stem, and hippocampus (Swanson and Cowan, 1979), while its
major outputs are the entorhinal cortex and the hippocampus via the fornix (Gonzalez-
Sulser et al., 2014; Khakpai et al., 2013). The medial septum neural population consists
of three broad groups: GABAergic, cholinergic and glutamatergic neurons, and all three
8
cell types project to all regions of the hippocampus, targeting both excitatory and
inhibitory neurons in different proportions (Amaral and Kurz, 1985; Colom et al., 2005;
Freund and Antal, 1988; Sotty et al., 2003). In CA1, principal cells receive a majority of
MS innervation from cholinergic (∼66%) and GABAergic (∼27%) cells (Sun et al., 2014).
In comparison, CA1 inhibitory cell types receive much less cholinergic MS innervation
(∼12%) and much stronger GABAergic innervation (∼67%). Finally, glutamaterigic MS
cells more strongly innervate CA1 inhibitory (∼21%) versus glutamatergic neurons
(~7%) (Freund and Antal, 1988; Gulyás et al., 1990; Sun et al., 2014).
GABAergic neurons within the MS are known to fire rhythmically at theta
frequencies and are phase-locked to hippocampal theta rhythms in vivo (Simon et al.,
2006). Although the reason why these neurons fire at theta frequency is not clearly
known, it may be due in part to the expression of specific conductances, such as HCN
channels, which confers them with an intrinsic ‘pacemaking’ rhythmicity (Varga et al.,
2008). Nevertheless, MS GABAergic neurons are critical for hippocampal theta, since
their inactivation significantly decreases theta power in the hippocampus, and
stimulation of these neurons entrains the network at the frequency of the stimulation
(Bender et al., 2015; Boyce et al., 2016; Etter et al., 2019; Tóth et al., 1997). In contrast,
MS cholinergic neurons show a slower firing pattern (Simon et al., 2006) and are
therefore unlikely to be directly involved in pacing the hippocampal theta rhythm. In
support of this, optogenetic manipulation of MS cholinergic neurons in vivo suggests
that these neurons may promote theta by suppressing peri-theta frequencies within the
hippocampus (Vandecasteele et al., 2014). Finally, optogenetic MS glutamatergic
stimulation can synchronize hippocampal theta oscillations in CA1, probably
9
predominantly through local MS innervation rather than their direct projections to the
hippocampus (Fuhrmann et al., 2015; Robinson et al., 2016). Thus, based on the data
available to date, hippocampal theta rhythms in mice and rats likely arise from the
activity of GABAergic neurons, although the precise mechanism by which this occurs
remains to be uncovered.
1.2.1.2. Functional role of theta oscillations
Theta has been widely implicated in learning and memory(Winson, 1978). As
early as the 1970s, studies showed that higher amounts of theta was correlated with
future rate of learning (Berry and Thompson, 1978) or with the degree that rodents
remembered an event, such as a footshock (Landfield et al., 1972). Moreover,
disrupting theta rhythms in the hippocampus by inactivating the medial septum during
wakefulness or REM sleep consistently leads to memory deficits (Boyce et al., 2016;
Shirvalkar et al., 2010; Winson, 1978), while reinstating these rhythms restores memory
performance (McNaughton et al., 2006). Theta is also required to coordinate neural
assembly activity between distant brain structures (Sirota et al., 2008), as well as within.
For example, abolishing theta rhythms via medial septum inactivation disrupts
organized ensembles of place cells, called theta sequences, which are linked to
memory (Dragoi and Buzsáki, 2006; Skaggs et al., 1996; Wang et al., 2014). Finally,
this temporal code for neural populations may be enabled by theta through the
facilitation of synaptic plasticity (Hölscher et al., 1997; Huerta and Lisman, 1995). At the
neuronal level, memory formation may be due to changes in the strength of synaptic
connections in the networks underlying the memory. Exploration of a novel
10
environment, in which theta frequency oscillations predominate in the hippocampus, is
associated with enhancement of persistent changes in synaptic strength, a
phenomenon termed long-term potentiation (LTP) (Davis et al., 2004; Orr et al., 2001).
Interestingly, applying electrical stimulation at theta frequencies can also efficiently
induce LTP-associated synaptic changes both in vitro and in vivo (Capocchi et al., 1992;
Larson et al., 1986; Larson and Munkácsy, 2015; Staubli and Lynch, 1987). The
association between theta activity and synaptic strength provides a possible mechanism
for the temporal coding of information. Theta activity can act as a temporal metric to
bind together or segregate neural activity, regulating synaptic connections important for
memory storage (Buzsáki, 2002).
1.2.1.3. Theta rhythm in humans
The hippocampus is located ventrally in humans and is therefore harder to
access, which may explain in part the relative scarcity of research on human
hippocampal theta. During REM sleep, studies using depth electrodes in epileptic
patients undergoing pre-surgical screening have described transient (average duration
of ~1.5s) and slower theta rhythms (~5 Hz) than in rodents (Cantero et al., 2003).
Furthermore, these hippocampal theta frequency oscillations were not found to couple
with gamma oscillations in sleep, a common feature in rodents (Bandarabadi et al.,
2019; Montgomery et al., 2008; Zhang et al., 2019)(see section 1.2.2).Given these
differences, it has been proposed that a continuous lower frequency oscillation (~1-3 Hz
equivalent to the rodent delta frequency range) could be the human analog to the rodent
hippocampal theta. Indeed, continuous 1-3 Hz oscillations have been recorded during
11
REM sleep in epileptic patients (Bódizs et al., 2001) and gamma oscillations
significantly phase-lock to this frequency range (Clemens et al., 2009). Additionally,
similarly shifted low frequency rhythms have been reported within the hippocampus
during task acquisition and recall in a virtual environment (Clemens et al., 2013;
Watrous et al., 2013). However, this is at odds with a recent study by (Bohbot et al.,
2017), where clear hippocampal theta oscillations were reported in humans navigating
real rather than virtual environments. Bohbot et al’s results may reflect findings in
rodents where theta frequency, which usually increase with running speed, increased
less when running in virtual as opposed to real environments (Chen et al., 2018).
Adding to the difficulties in confirming a single theta band human analog during both
REM and wake behaviors, gamma oscillations have been shown to couple with ~2.5-
8hz theta in humans performing memory tasks and, mirroring evidence from rodent
studies, are also associated with memory performance (Heusser et al., 2016; Lega et
al., 2016; Mormann et al., 2005). Thus, although the proposed ~1-3 Hz human
hippocampal oscillations appear consistent with the basic features of theta rhythms
during REM sleep in rats and mice, confirming the human analog of the latter still
requires further research.
1.2.2. Gamma oscillations
It has been suggested that the transfer of information to hippocampal region CA1
from CA3 and the entorhinal cortex is organized in two separate gamma frequency
channels that are largest in amplitude during theta states (Colgin et al., 2009; Csicsvari
et al., 2003). Accordingly, these two gamma bands appear to be differentially prevalent
12
in different layers of CA1 (Belluscio et al., 2012; Schomburg et al., 2014). The first type
of gamma, low gamma (~30- 60 Hz) is thought to be driven by CA3, since it has higher
amplitudes in stratum radiatum of CA1. The second, high gamma (~60-120 Hz), is
entrained by inputs from the medial entorhinal cortex and dominates in CA1 stratum
lacunosum-moleculare.
Functionally, since CA3 is associated with memory retrieval, low gamma is
proposed to be linked to these processes. Evidence supporting this idea comes from
behavioral studies in rats showing that, during periods of low gamma, place cell
ensembles preferentially represent upcoming locations rather than recent ones, when
navigating familiar environments (Bieri et al., 2014). Moreover, in an associative
memory task, the coupling between theta and low gamma amplitude at the time when
memory retrieval was expected to occur was correlated with task performance
(Shirvalkar et al., 2010; Tort et al., 2009). In contrast, the entorhinal cortex processes
sensory information and transmits this information to the hippocampus (Canto et al.,
2008). Consequently, high gamma has been linked to memory encoding and working
memory. For example, it has been shown that place cells tended to encode recent
locations and ongoing trajectories during high gamma (Bieri et al., 2014; Zheng et al.,
2016), and high gamma power increases when mice use cues in the external
environment to navigate towards the location of a reward (Cabral et al., 2014).
1.2.3 Sharp-wave ripple complexes
Sharp-wave ripple complexes (SWRs; labeled ripple in the thesis figures) consist
of 1-3 Hz sharp waves with superimposed ~110 -250 Hz fast oscillation ripples. These
13
rhythms are thought to be generated within the hippocampus and are present in rodents
(Buzsáki, 1986), non-human primates (Hussin et al., 2020) and humans (Norman et al.,
2019). They are detected in periods of immobility, during consummatory behaviors, and
most frequently in non-REM (NREM) sleep (Buzsáki, 2015, 1998, 1986; Mölle et al.,
2006). Sharp-wave ripples have been hypothesized to carry out offline mnemonic
functions, such as memory consolidation (Clemens et al., 2007; Ramadan et al., 2009;
Siapas and Wilson, 1998), and their disruption impairs memory (Girardeau et al., 2009;
Talakoub et al., 2016). Moreover, SWRs contain trajectory information, since place cells
-pyramidal neurons that preferentially fire at given location in the environment- are
‘replayed’ in sequence during SWRs (Karlsson and Frank, 2009; Levenstein et al.,
2016; Nádasdy et al., 1999; Nakashiba, Buhl, McHugh, 2009; Wu et al., 2017). Although
sharp-waves and ripples often occur together, they are separate events with distinct
origins. Sharp-waves are excitatory events that are transmitted from CA3 to CA1
(Buzsáki, 1986; Sullivan et al., 2011). In contrast, ripples are generated locally. In CA1,
ripples result from strong inhibitory feedback on pyramidal cells via the activation of
reciprocally connected inhibitory interneurons (English et al., 2014; Klausberger T. et
al., 2003; Ylinen et al., 1995). This inhibition raises spiking thresholds and prevents
most cells from firing, which suggests that ripples may function to reinforce the most
strongly encoded memories for consolidation. In other words, only cells with synapses
that were recently potentiated during learning, which are likely more excitable, would fire
during this local inhibition and send their memory traces further downstream (Chrobak
and Buzsáki, 1994; Tingley and Buzsáki, 2020) for long-term storage in the neocortex.
14
1.3. Sleep and memory consolidation
In mammals, sleep can be classified by combining analysis of brain and muscle
activity via EEG/LFP and electromyogram (EMG) recordings, respectively. Since state-
dependent analysis is presented in this thesis, a summary of the basic physiological
characteristics that distinguish this state is provided.
The first major distinction is observed between sleep and wake periods (Fig. 1.3).
Wakefulness can be characterized as a state of external awareness during which
subjects may engage in activities ranging from quiet rest to ambulation. A sustained
muscle tone is present within postural muscles throughout periods of wakefulness,
which is accompanied by large bursts of movement-associated muscle activity that can
be detected in EMG recordings without the need for video monitoring. During active
wake, theta and gamma frequencies can be easily detected from cortical and
hippocampal recordings. In contrast, quiet or drowsy wakefulness is accompanied by
prominent slow neural activity patterns in the delta (0.3-4 Hz) range, together with the
presence of discrete SWR events in the hippocampus (Brown et al., 2012).
During sleep, muscle tone decreases. Though no or barely any movement can
be detected during sleep, the brain is most definitely still active as sleep cycles through
electrophysiologically and neurochemically distinct sleep stages. These stages are
generally split into two main categories, based on the occurrence or absence of rapid
eye movement: REM and non-REM (NREM) sleep, respectively (Brown et al., 2012).
15
1.3.1 Differences between REM and NREM sleep
REM and NREM sleep differ remarkably in several ways. NREM sleep is
characterized by high amplitude, low frequency (0.3–4 Hz) activity similar to quiet
awake which reflects synchronization across large cortical neuronal populations.
Furthermore, an increase in the number of SWRs occurs in NREM in comparison to
wake periods (Buzsáki, 2015). NREM is usually observed upon the initial transition from
Fig. 1.3: Neural activity patterns associated with wake and sleep behavior in the
human and rodent brain. Example EEG recordings from the human (left) and rat
(right) brain are for wakefulness, NREM, and REM sleep. Figure from Brown et al., 2012
with contribution from Purves et al., 1997
16
wakefulness to sleep, although under certain sleep disorders, such as narcolepsy, REM
sleep may immediately follow wakefulness (Fujiki et al., 2009). Neuromodulator levels
also differ between these two brain states: during NREM sleep, acetylcholine levels in
the brain stem, forebrain, and hippocampus are at a physiological low (Hobson et al.,
1975; Marrosu et al., 1995), while serotonergic and noradrenergic neurons fire at
reduced rates compared to wake (Aston-Jones and Bloom, 1981).
In humans, NREM sleep is divided into light sleep and slow-wave (or deep) sleep
(Berry et al., 2015), while in rodents all NREM sleep stages are collectively referred to
as NREM or slow-wave sleep (Genzel et al., 2014; Oyanedel et al., 2014; Van Twyver,
1967). Only a few studies differentiate between light and deep NREM sleep in rodents
(Benedetto et al., 2013), or clarify if the spindle-rich transition to REM sleep is included
in the NREM analysis or excluded (Watts et al., 2012). This spindle-rich transition
appears to be unreported in the human literature and may not be a characteristic in
human sleep.
In normal conditions, REM always follows NREM periods, both in humans and
rodents (Genzel et al., 2014). Apart from the rapid darting eye movements, REM can
also be characterized by a complete absence of muscle tone. This atonia of the skeletal
musculature is a vital characteristic of REM sleep, the loss of which results in dream
enactment (McCarter et al., 2012). Cortical activity is reminiscent of active wake and
includes the presence of theta and gamma frequencies (Llinas and Ribary, 1993;
Steriade et al., 1996). Moreover, cholinergic modulation during REM sleep increases to
levels just below that of wake in the neocortex and even exceeds wake levels in the
hippocampus (Hasselmo, 1999). In contrast, serotonergic and noradrenergic neurons
17
are almost completely silenced during REM sleep (Pace-schott et al., 2009). In human
nocturnal sleep, slow-wave sleep predominates during the first half of the night, with
REM sleep becoming increasingly prevalent toward the morning (Wurts and Edgar,
2000), while in rodents REM sleep does not appear to follow such circadian rhythm
(Yasenkov and Deboer, 2012).
1.3.2. REM sleep and memory
The hypothesis that sleep subserves memory consolidation is well established
(Rasch and Born, 2013). Current theories posit that memories are initially encoded into
a flexible learning store (i.e., the hippocampus) and then gradually transferred to the
neocortex for long-term stable storage. The hippocampus may ensure, even in one
attempt, the quick and efficient encoding of memories; yet, these representations are
unstable and vulnerable to interference by more recent experiences. Thus, information
must be gradually transferred to the neocortex to ensure that the gist of the memory is
preserved. It is presumed that the repeated reactivation of new memory traces during
off-line periods like sleep gradually strengthens and stabilizes this information.
Repeated reactivation in the form of SWRs during NREM periods has been amply
described in the hippocampus (see section 1.2.3). Notably, SWRs co-occur with cortical
spindles, highlighting a potential mechanism of cortico-hippocampal communication
during sleep (Gelinas et al., 2016; Siapas and Wilson, 1998). On the other hand, it is
thought that REM episodes following NREM periods may further stabilize memories, but
the mechanisms by which this process occurs is less clear.
18
REM sleep has been clearly implicated in memory consolidation: in human and
rodent studies, REM sleep deprivation negatively affects memory performance
(Chernik, 1972; Karni et al., 1994; Pearlman and Becker, 1973, 1974; Smith and Butler,
1982; Walsh et al., 2011), while cue-induced re-exposure during REM enhances
subsequent memory performance (Guerrien et al., 1989; Hars et al., 1985; Hennevin et
al., 1989; Smith and Weeden, 1990).
Recent evidence indicates that synaptic downscaling may be one of the
mechanisms underlying memory stabilization in REM. This downscaling may enhance
the signal to noise ratio for more strongly encoded memory representations by nullifying
the strength of connections that were only weakly potentiated during wakefulness.
Indeed during REM sleep, new synapses are pruned, and the remaining synapses get
strengthened (Li et al., 2017). Network modeling suggests a strong link between the
strength of a neuron’s connectivity and its firing rate, indicating that firing rate
fluctuations may be a proxy for synaptic changes (Olcese et al., 2010; Tononi and
Cirelli, 2014). Supporting this notion, unit studies have shown that hippocampal
activation during REM sleep periods might contribute to an overall downscaling of
neuronal firing rates observed across sleep, as this decrease in firing rates during sleep
was correlated with REM theta power (Born and Feld, 2012; Grosmark et al., 2012;
Miyawaki et al., 2019; Miyawaki and Diba, 2016). Directly highlighting the importance of
theta oscillations in REM, inhibiting theta power in the hippocampus by optogenetically
inactivating the medial septum during post-training REM sleep results in memory
impairments (Boyce et al., 2016). Taken together, this data suggests that reactivation of
19
sequences in SWRs during NREM, and subsequent theta power-associated synaptic
downscaling during REM may underlie memory consolidation.
1.4. Alterations in hippocampal activity and Alzheimer’s Disease*
The interplay between excitatory and inhibitory circuits underlies the brain’s
processes and their dysregulation has been linked to cognitive decline, psychiatric
disorders and epilepsy. In patients with Alzheimer’s disease (AD), an elevated
occurrence of seizures has been observed in both sporadic and familial forms of the
condition. Although seizure activity in AD has been mainly viewed as a result of
neuronal death and considered to occur in later stages, it is now increasingly clear that
aberrant neuronal activity may be more common in patients at earlier stages than
previously thought, and this aberrant activity may trigger and contribute significantly to
memory defects. In this section, we review abnormal oscillatory activity in the context of
AD, focusing on alterations of inhibitory and excitatory hippocampal and cortical circuits
that may lead to overexcitability and early dysregulation of neuronal networks, and the
therapeutic outcomes of restoring this excitatory-inhibitory balance.
1.4.1. Introduction
Alzheimer’s Disease (AD) is a neurodegenerative disorder characterized by
declarative memory impairments and increasingly severe cognitive decline that
ultimately leads to dementia. To date, no successful treatment has been found. AD
pathology is predominantly characterized by the occurrence of amyloid beta (Ab)
plaques and neurofibrillary tangles of hyperphosphorylated tau in the brain, as well as
20
neuronal loss. While Ab and tau may drive AD progression, the pathogenic cascade that
leads to AD appears to start decades before these clinical symptoms are present. In
particular, subjects at genetic risk for AD show hippocampal hyperactivity (as measured
with fMRI) during memory tasks (Bookheimer et al., 2000; Dickerson et al., 2005; Quiroz
et al., 2010) and impaired default network activity (Filippini et al., 2009), indicating the
presence of early network alterations. Importantly, although AD pathology is related to
global measures of cognition in unimpaired elderly subjects (Bennett et al., 2012), some
individuals display Ab accumulation without dementia (Bennett et al., 2012; Lue et al.,
1996; Roberts et al., 2018; SantaCruz et al., 2011). In fact, despite amyloid plaques
being a hallmark of the disease, the density of Ab deposits does not significantly
correlate with AD progression (Arriagada et al., 1992; Berg et al., 1993; Bierer et al.,
1995; Dickson et al., 1992; Guillozet et al., 2003; Schmitt et al., 2000), or does not
correlate as well as other measurements such as synaptic loss, which is regarded as
the best predictor (Bennett et al., 2004; Blennow et al., 1996; Giannakopoulos et al.,
2003; Lue et al., 1996; Scheff and Price, 1993; Terry et al., 1991). There is growing
evidence that alterations of the amyloid precursor protein (APP) and particular isoforms
of soluble Ab peptides can alter synaptic function and affect cognition before any
neurodegeneration or plaque deposition is observed in the brain (Balducci et al., 2010;
DʼHooge et al., 1996; Giacchino et al., 2000; Mucke et al., 2000, 1994; Oddo et al.,
2003; Shankar et al., 2008).
Beyond the main pathological hallmarks of AD, synaptic dysfunction is also of
major importance, and AD has consequently been referred to as a “disease of synaptic
failure” (Selkoe, 2002). Particularly, changes in excitatory and inhibitory synapses
21
releasing glutamate and GABA, respectively, has gathered increasing interest as a
mechanism contributing to AD pathology. The fine balance between excitatory and
inhibitory transmission (E/I balance) is essential for brain oscillations (Amilhon et al.,
2015; Boyce et al., 2016; Huh et al., 2016) and normal cognitive function (Zhou and Yu,
2018). Perturbations in E/I balance probably contribute to cognitive changes in AD
(Busche and Konnerth, 2016; Selten et al., 2018), considering that abnormal oscillatory
rhythmic activity and network hypersynchrony are observed in AD mice models and AD
patients (de Waal et al., 2012; Goutagny et al., 2013; Irizarry et al., 2012; Lozsadi and
Larner, 2006; Osipova et al., 2005; Palop et al., 2007; Palop and Mucke, 2016; Vogt et
al., 2011).
1.4.2. Abnormal rhythmic activity in AD
Reflecting the progressive disruption in the fine balance between excitatory and
inhibitory transmission, EEG abnormalities are strongly related to disease severity and
disease stage in AD patients. Indeed, in the earliest stages of the disease, changes in
EEG tend to be mild or absent (Boerman et al., 1994; de Waal et al., 2012; Penttilä et
al., 1985). The most frequently observed spectral alteration is the “slowing” of the EEG
in resting state: a decrease in theta peak frequency, together with an increase of theta
power (~4-7 Hz) is expected in early AD patients, followed by a reduction of alpha
amplitude (~8-12 Hz) (Adler et al., 2003; Czigler et al., 2008; Huang et al., 2000; Jelic et
al., 2000; Moretti et al., 2010; van der Hiele et al., 2007). Although this is the most
common described characteristic, progressive EEG slowing could only be detected in a
proportion of early AD cases, with 50% showing no deterioration at 12 months follow-up
22
(Rae-Grant et al., 1987; Soininen et al., 1989). In fact, theta power has been reported to
be unchanged at rest (Caravaglios et al., 2010; Wang et al., 2017) or even decreased
during visual and auditory stimulus processing (Başar et al., 2010; Caravaglios et al.,
2010; Yener et al., 2007). These discrepancies may in part be attributed to differences
in diagnostic criteria and methodological approaches, as well as to the fact that these
global EEG measures might not be quite sensitive enough (Jelic and Kowalski, 2009).
In AD rodent models, depth electrodes have been used to directly record
changes in oscillations from the hippocampus. In the early stages of the disease, before
plaque deposition is apparent, theta peak frequency has been shown to be decreased
in vivo (Cayzac et al., 2015; Siwek et al., 2015) and in vitro (Goutagny et al., 2013; Scott
et al., 2012). On the other hand, no changes in theta power have been found neither in
vivo (Rubio et al., 2012; Schneider et al., 2014) nor in vitro (Goutagny et al., 2013;
Mondragón-Rodríguez et al., 2018; Scott et al., 2012), with only one study detecting an
early decrease (Ittner et al., 2014). Once plaques are present, theta amplitude is
generally diminished (Mably et al., 2017; Rubio et al., 2012; Schneider et al., 2014;
Scott et al., 2012), although no changes have also been reported (Etter et al., 2019;
Siwek et al., 2015). All in all, the evidence here presented indicates that alterations in
theta amplitude, specially during rest, may not be a reliable early marker by itself for the
diagnosis of AD.
As seen in section 1.2.2, gamma amplitude and its coupling with theta are also
indicative of memory performance. Indeed, alteration in gamma and in its coupling with
slower frequencies have been observed in AD, although the studies in patients are few
and the results contradictory (Kim et al., 2012): both decreases (Goodman et al., 2018;
23
Kurimoto et al., 2012) and increases (Van Deursen et al., 2008; Wang et al., 2017) in
gamma and theta-gamma coupling have been reported. The picture emerging from
hippocampal recordings in rodent models is clearer, with reductions in theta-gamma
coupling apparent early on (Goutagny et al., 2013; Mondragón-Rodríguez et al., 2018),
followed by decreases in gamma power (Etter et al., 2019; Gillespie et al., 2016; Mably
et al., 2017; Rubio et al., 2012; Schneider et al., 2014)(but see (Ittner et al., 2014)).
Interestingly, one early AD study has also reported decreased gamma power, but
specifically during sharp-wave ripples (Iaccarino et al., 2016). Although this SWR-
specific deficit may indicate that memory consolidation could be compromised in the
beginning stages of AD, it should be carefully interpreted: in hippocampal SWRs, the
presence of gamma appears to be spurious (Oliva et al., 2018), and may simply reflect
differences in inter-SWR separation between groups. Finally, gamma amplitude is not
only found to be lower in the hippocampus, but also in cortex (Verret et al., 2012). This
decrease in cortical gamma has been linked to aberrant GABAergic inhibition, which is
though to be reduced due to a loss of the voltage gated sodium channel subunit Nav 1.1
in interneurons (Martinez-Losa et al., 2018; Verret et al., 2012).
1.4.3. Abnormal network activity in AD promotes epileptiform activity
Significant E/I imbalance leading to epileptic-like activity probably contributes to
AD pathogenesis, since up to 22% of patients experience unprovoked seizures, with
rates increasing up to 58% in familial forms of AD (Amatniek et al., 2006; Friedman et
al., 2012; Larner, 2011; Mendez and Lim, 2003; Vossel et al., 2016, 2013). Seizures in
AD can be convulsive tonic-clonic, or more prevalently, non-convulsive with patients
24
experiencing altered consciousness, amnestic periods, and confusion which can go
undiagnosed for years (Vossel et al., 2013). Furthermore, at least 20% of patients with
AD experience transient episodes of amnestic wandering and disorientation, common
elements of several dementias, which have been associated with epileptiform
discharges (Bradshaw et al., 2004; Lee et al., 2012; Rabinowicz et al., 2000). It is likely
that epileptiform activity in the early stages of AD is often underdiagnosed and
undetectable as intracranial electrodes positioned adjacent to the mesial temporal lobe
may be necessary to reveal abnormal electrical signals such as spike and wave activity
during sleep in patients with no history of epilepsy (Brown et al., 2018; Lam et al.,
2017).
Several studies suggest that increases in Ab, and particularly early soluble forms
of Ab such as oligomers, as well as Ab associated proteins, are key factors responsible
for altering E/I balance. Such rapid increases in soluble forms of Ab are central to
familial early onset forms of AD, which have mutations in the gene for APP or
presenilin-1 (PSEN1) (Brouwers et al., 2008; Potter et al., 2013; Shepherd et al., 2009;
Suzuki et al., 1994). In AD transgenic mouse models with similar APP mutations,
prominent neuronal hyperactivity and impaired E/I are also observed. For example,
amyloid oligomers in APP mice models were shown to acutely affect synaptic
transmission (Busche et al., 2012) and change the structure of the circuit (i.e. dendritic
morphology) that directly cause hyperexcitability (Šišková et al., 2014). Mice with
overexpression of mutated APP, or mutated PSEN1 or ApoE4 genes (a risk factor firmly
linked to sporadic AD in humans), display spontaneous seizures and spike-wave
discharges (SWDs), suggesting an early E/I imbalance linked to amyloid (Born, 2015;
25
Born et al., 2014; Minkeviciene et al., 2009; Nuriel et al., 2017). SWDs are of particular
interest as an early feature of E/I imbalance in AD, as they occur even before the
presence of spontaneous seizures (Bezzina et al., 2015; Born et al., 2014; Kam et al.,
2016; Nygaard et al., 2015; Verret et al., 2012). SWDs reflect sudden, transient
synchronous hyperactivity, consisting of high voltage deflections with amplitudes
exceeding twice the baseline of recording, and resemble the waveform of interictal
spikes (Bezzina et al., 2015; Kam et al., 2016). Recent studies have focused on these
SWDs in AD mouse models as an important early factor (as illustrated in Fig.1.4)
underlying or contributing to cognitive defects, and are a relevant marker in the
diagnosis of epilepsy (Krendl et al., 2008; Rosati et al., 2003; Staley et al., 2011). SWDs
are notable in AD models carrying the APP Swedish mutation, which increases overall
Ab levels, in conjunction with other mutations, including the J20 (Palop et al., 2007),
Tg2576 (Bezzina et al., 2015), APP/TTA (Born et al., 2014), APP/PS1 (PSEN1)
(Minkeviciene et al., 2009; Reyes-Marin and Nuñez, 2017), and 3xTg (Nygaard et al.,
2015) models. To study the role of APP in the incidence of SWDs, models that enable
the expression of the human-mutated APP at different time points in a tetracycline-
responsive manner with the use of tetracycline analogs have been created (called
APP/TTA model, where TTA stands for tetracycline-controlled transactivator protein).
Once APP is expressed, SWDs are observed from the earliest time point that the
authors examined, and their number is higher during the light cycle when sleep is more
frequent (Born et al., 2014). The occurrence of SWDs was analyzed in more detail by
Kam et al. (2016) in Tg2576 mice, where they reported that SWDs appeared before
plaque deposition, and were more prominent in quiet wakefulness and sleep, and
26
especially during rapid eye movement sleep (REM). A similar distribution of SWDs
during before widespread plaque deposition sleep has also been observed in the J20
model (Brown et al., 2018). On the other hand, Brown et al. also measured the
incidence of interictal spikes in a patient with amnesic mild cognitive impairment using
in-depth electrodes and found a higher incidence of events during non-REM rather than
REM sleep. This finding is in agreement with studies of temporal lobe epilepsy, where
the rate of interictal spikes is generally higher during non-REM sleep (Clemens et al.,
2003; Lieb et al., 1980; Rossi et al., 1984; Sammaritano et al., 1991). The discrepancy
with AD animal studies may be partially dependent on the recording area since, in
epileptic patients, the prevalence of interictal spikes is higher during REM sleep if
recording from the primary epileptogenic region (Lieb et al., 1980; Rossi et al., 1984;
Sammaritano et al., 1991). Nevertheless, the occurrence of SWDs during sleep and
quiet wakefulness may have significant implications for memory impairment since these
periods are known to be necessary for memory consolidation (Boyce et al., 2016;
Buzsáki, 2015; Girardeau et al., 2009; Karlsson and Frank, 2009).
27
Fig. 1.4: Earliest recorded Spike-Wave Discharges (SWDs) in AD models. The
corresponding APP mutation is in parenthesis. References: J20 (Mucke et al., 2000;
Palop et al., 2007; Sanchez et al., 2012a; Verret et al., 2012). Tg2576 (Jacobsen et al.,
2006; Kam et al., 2016; Kawarabayashi et al., 2001). APP/PS1 (Minkeviciene et al.,
2009; Reyes-Marin and Nuñez, 2017; Schmid et al., 2016; Trinchese et al., 2004).
APP/TTA: (Born et al., 2014; Marin et al., 2016; Sri et al., 2019). 3xTg: 8 to 10 months is
the only time point were SWDs have been studied in this model to date (Billings et al.,
2005; Nygaard et al., 2015; Oddo et al., 2003).
Although not as much is known regarding the incidence of seizures in tau models
without APP mutations, tau aggregates have been reported in patient with epilepsy (Tai
et al., 2016) and reducing tau in experimental models of epilepsy ameliorates seizures
(Holth et al., 2013). FTDP-17 mice (a model of frontotemporal dementia with
parkinsonism linked to chromosome 17 which overexpresses human mutant tau) have
spontaneous seizures starting as early as 5 months of age, before tau aggregates are
present (García-Cabrero et al., 2013). Tau also appears to modulate hyperactivity and
seizure incidence in rodents, as knocking out tau completely, or reducing tau levels in
APP models, reduces SWDs and prevents increased susceptibility to induced seizures
(Roberson et al., 2011, 2007).
1.4.4. Excitatory Disruption and Hyperactivity
In AD, there has been a strong focus on excitatory dysfunction as the basis for
E/I disruption. Typically, changes in excitatory synaptic transmission have been
28
suggested to play a key role in the aberrant hyperactivation and hypersynchrony of
circuits resulting in the generation and spreading of epileptic discharges. Similarly,
administration of soluble Ab in vivo (Busche et al., 2012), in vitro (Minkeviciene et al.,
2009) and in neuronal cultures (Cuevas et al., 2011) have also been shown to generate
neuronal hyperexcitability in hippocampal neurons and circuits. The Ab-mediated
increase in neuronal excitability could be due to several mechanisms associated with
glutamate synaptic transmission. Ab soluble oligomers are known to rapidly enhance
hippocampal NMDAR currents in vitro (Wu et al., 1995), in vivo (Molnár et al., 2004) and
in cell membranes of AD patients microtransplanted into oocytes (Texidó et al., 2011),
suggesting that these oligomers may directly cause increased neuronal firing.
Moreover, soluble Ab oligomers could cause hyperexcitability by disrupting
glutamate uptake as demonstrated by experiments employing cultured neurons and
astrocytes, wherein Ab directly alters glutamate transporter expression and/or function
(Fernández-Tomé et al., 2004; Harris et al., 1996; Matos et al., 2012). The disruption of
glutamate transporters, named excitatory amino acid transporters or EAATs, can cause
abnormal increases in extracellular glutamate concentrations, triggering elevated
spontaneous excitatory postsynaptic currents, population spike frequency, and
impairments in long-term-potentiation (Lei et al., 2016; S. Li et al., 2009). In addition,
reducing glutamate transporters can have significant effects on excitability because
these transporters can normally prevent the generation and spread of seizures
(Demarque et al., 2004). Glutamate is also a GABA precursor; consequently, disruption
of glutamate reuptake could indirectly reduce GABAergic synthesis, thereby intensifying
E/I imbalance and further promoting hyperexcitability (Sepkuty et al., 2012). Such
29
reductions in glutamate transporter expression have been reported in AD patients and
may contribute to reductions in glutamate uptake, E/I imbalance and neurodegeneration
(Masliah et al., 1996; Scott et al., 2011).
Additionally, APP and soluble Ab can modulate synaptic plasticity and glutamate
receptor trafficking (see Hoe et al., 2012 for review). APP levels have been shown to
change early during development and peak in the second post-natal week, which
coincides with the NMDAR developmental receptor subtype switch from GluN2B to N2A
(Liu et al., 2004; Löffler and Huber, 1992). However, if APP is over-expressed, this can
cause an upregulation of NMDARs containing GluN2B subunits in the hippocampus due
to increased GluN2B mRNA levels (Cousins et al., 2009; Hoe et al., 2009). This
increase in GluN2B NMDA receptor subtype may, in turn, increase excitability and
directly contribute to seizures (Chen et al., 2016; Okuda et al., 2017). Although the
mediator behind this APP synaptic regulation may involve its intracellular domain
(Pousinha et al., 2017), the oligomeric form of Ab can alter surface levels of GluN2B
NMDARs, which is required for the presence of seizure phenotype in the APP/PS1 AD
mice model (Um et al., 2012). At the same time, Ab also disrupts the ability of other
mechanisms to limit excessive NMDAR activity, thus adding to network hyperexcitability
(You et al., 2012).
Apart from its role in modulating receptor trafficking, soluble Ab can directly
activate the GLuN2B NMDA receptor subunit, thereby disrupting intracellular calcium
homeostasis and synaptic plasticity (Ferreira et al., 2012; Li et al., 2011). Overactivation
of glutamate receptors has been shown to be highly toxic, an effect termed
excitotoxicity. Undesired increases of intracellular Na+ and Ca2+ that result in cell death
30
will occur once the NMDAR Mg2+ block is released by persistent depolarization of the
cell (Arundine and Tymianski, 2003; Choi, 1985; Koh and Choi, 1991; Liu et al., 2007;
Rothman, 1985). A substantial body of evidence indicates that this neurotoxic
mechanism may contribute to the eventual neuronal loss seen in AD (Brorson et al.,
1995; Hynd et al., 2004; Mattson et al., 1992; Mattson and Goodman, 1995; Miguel-
Hidalgo et al., 2002; Tominaga-Yoshino et al., 2001; Yatin et al., 2001). Moreover,
activation of NMDARs is also known to regulate APP trafficking and processing, as well
as facilitating Ab production (Bordji et al. 2010; Lesne et al. 2005; Hoe et al. 2009),
potentially creating a feedback loop which eventually results in cell death.
Ab toxicity may also be potentiated by tau through increases in glutamatergic
signaling leading to excitotoxicity. Tau can stabilize NMDARs in synapses and increase
NMDA receptor-dependent currents through an aberrant association with the kinase
Fyn, ultimately strengthening glutamate neurotransmission (Ittner et al., 2010). Notably,
the reduction of tau suppresses NMDA dependent excitotoxicity in hippocampal slices
(Miyamoto et al., 2017) and decreases hyperexcitability in the form of SWDs and
seizure activity in animal models as noted previously (AD: Roberson et al. 2007;
Epilepsy: Holth et al. 2013). This enhanced glutamatergic signaling has been proposed
to account for the higher NMDAR binding found in post-mortem brain tissue from AD
patients (Ułas et al., 1994).
Finally, Ab can also interact with mGluRs, and its oligomeric form in particular, is
able to aberrantly cluster mGluR5, which in turn elevates intracellular calcium and could
ultimately cause synaptic deterioration (Renner et al., 2010). In agreement with this,
patients with AD have enhanced mGluR5 levels as measured by immunostaining in
31
comparison to age-matched controls, at least in astrocytes (Casley et al., 2009) and
deletion of mGluR5 is known to improve cognitive decline in the APP/PS1 model of AD
(Hamilton et al., 2014).
Excitatory circuitry alterations may also directly dysregulate inhibitory
neurotransmission. There is evidence that glutamate may developmentally regulate
axons and dendrites of GABAergic interneurons (De Marco García et al., 2011), and
that glutamatergic signaling itself is capable of modulating post-synaptic GABAA
receptor expression and clustering (Bannai et al., 2015) as well as inhibitory circuit
plasticity (Mapelli et al., 2016; McLean et al., 1996; Moreau and Kullmann, 2013).
Although a great deal of data indicates that aberrant changes in excitatory synaptic
transmission are important for AD progression, significant evidence also exists
suggesting that disruption in GABAergic synaptic transmission is also important.
32
Fig. 1.5: Schematic of neural network in normal and AD conditions, including
Glutamatergic/GABAergic neurons and connections. Presynaptic glutamate release
triggers postsynaptic activation through the binding of membrane receptors. In the
synaptic cleft, glutamate can be taken up by transporters located in astrocytes and
converted to glutamine and GABA. On the other hand, presynaptic GABA release
33
triggers postsynaptic deactivation through GABAr, and can also be taken up by
astrocytic GABAergic transporters. In AD conditions, glutamatergic concentration in the
synaptic cleft is increased due to altered glutamatergic transport. This alteration leads to
overactivation of postsynaptic neurons through upregulated NMDA and mGluR
receptors. GABAergic transmission is impaired due to a loss of presynaptic sodium
currents and postsynaptic GABA receptors. To counteract hyperexcitability, astrocytes
may facilitate GABAergic synthesis, release and innervation. [1] Busche et al., 2012. [2]
Minkeviciene et al., 2009. [3] Cuevas et al., 2011. [4] Molnár et al., 2004. [5] Texidó et
al., 2011. [6] Wu et al., 1995. [7] Fernández-Tomé et al., 2004. [8] Harris et al., 1996. [9]
Matos et al., 2012. [10] Sepkuty et al., 2012. [11] Masliah et al., 1996. [12] Scott et al.,
2011. [13] Cousins et al., 2009. [14] Hoe et al., 2009. [15] Chen et al., 2016. [16] Okuda
et al., 2017. [17] Ferreira et al., 2012. [18] Li et al., 2011. [19] Ulrich, 2015. [20] Howell
et al., 2000. [21] Ikonomovic et al., 2003. [22] Limon et al., 2012. [23] Mizukami et al.,
1998. [24] Ren et al., 2018. [25] Mondragón-Rodríguez et al., 2018. [26] Verret et al.,
2012. [27] Martinez-Losa et al., 2018. [28] G. Li et al., 2009. [29] Bell et al., 2003. [30]
Born et al., 2014. [31] Hollnagel et al., 2019. [32] Palop et al., 2007.
1.4.5. Inhibitory Disruption
Although the predominant view has been that inhibitory synaptic transmission is
relatively more resilient in AD (Francis et al., 1993; Palmer and Gershon, 1990;
Reinikainen et al., 1988; Rissman and Mobley, 2011), it is now becoming clear that
inhibitory circuits are severely disrupted early in the disease process (Bell et al., 2006;
Rossor et al., 1982; Ulrich, 2015), as depicted in Fig.1.5. For example in the APP/TTA
34
AD mice model, an E/I imbalance is apparent since administering 1.75mg/kg of the
GABAA antagonist picrotoxin leads to seizures within an hour in vivo, an effect not
observed with the same dosage in controls (Born et al., 2014). Another GABAA receptor
antagonist, Pentylenetrazol, has also been shown to induce seizures at a higher rate in
the Tg2576 (Westmark et al., 2008), J20 (Palop et al., 2007) and tgCRND8 (Del
Vecchio et al., 2004) AD models compared to controls. These effects may be explained
by factors related to reduced inhibitory function. For example, Ab application to cortical
slices in vitro results in the endocytosis of GABAA receptors (Ulrich, 2015), and
evidence from AD patients suggests that GABAergic receptor activation provides
smaller inhibitory currents, indicating a remodeling of GABAergic inhibition in human AD
(Limon et al., 2012). Other studies report that specific GABA receptor subunits are
downregulated in particular regions (Howell et al., 2000; Limon et al., 2012; Mizukami et
al., 1998; Rissman et al., 2003), while a preservation in the expression of other subunits
has also been observed (Limon et al., 2012; Mizukami et al., 1997; Rissman et al.,
2003), suggesting area-specific changes in receptor composition (see Rissman and
Mobley, 2011 for review).
Ab also produces a decrease in GABA release from fast-spiking interneurons
innervating principal cells in slice (Ren et al., 2018). Fast spiking firing patterns have
been associated with parvalbumin (PV) expressing GABAergic neurons since the 1980s
(Kawaguchi et al., 1987; see Hu et al., 2014 for review), have been linked to memory
consolidation (Ognjanovski et al., 2017; Xia et al., 2017a) and are essential for spatial
working memory (Murray et al., 2011). Stimulating PV interneurons, but not other cell
types, can enhance gamma oscillatory activity (20-80 Hz) (Cardin et al., 2009), and
35
increases in this frequency band during memory encoding have been linked with
memory performance (Sederberg et al., 2006; Yamamoto et al., 2014), see section
1.2.2). Early in the J20 AD model, a reduction of PV interneuron firing has been
observed in areas such as the hippocampus (Mondragón-Rodríguez et al., 2018) and
has been linked to the loss of the voltage-gated sodium channel subunit Nav 1.1,
leading to a decrease of GABAergic inhibition and possibly gamma power (Verret et al.,
2012). Importantly, interneuron transplants overexpressing Nav1.1 in cortex and
hippocampus were able to reduce hypersynchrony, enhance gamma oscillations and
improve memory in the same transgenic mouse line (Martinez-Losa et al., 2018). Along
the same line, stimulating PV interneurons at gamma frequency appears to decrease
Ab-40 and 42 isoforms (Iaccarino et al., 2016). In findings linked to interneuron
hypoactivity, neuronal pentraxin-2, a protein secreted by excitatory neurons which
specifically mediates activity-dependent strengthening of excitatory synapses onto PV
interneurons (Chang et al., 2010), is down-regulated in the brains of AD patients (Xiao
et al., 2017).
Several lines of research also show that APP itself may modulate GABAergic
inhibition and GABAA receptors (Chen et al., 2017; Seabrook et al., 1999; Yang et al.,
2009). Most recently, Rice et al. (2019) demonstrated that secreted APP is able to bind
to GABAB receptors to suppress synaptic vesicle release, thus modulating hippocampal
synaptic plasticity. In addition to APP, the AD risk factor ApoE4 is known to decrease
both the number of interneurons as well as GABAergic innervation in ApoE4 knockin
mice (G. Li et al., 2009), whereas conditionally deleting ApoE4 in neurons rescues
GABAergic neuronal loss and memory deficits (Knoferle et al., 2014). Other studies
36
point to important changes in synaptic inhibition and the subsequent potential
compensatory responses of the GABAergic circuitry that are associated with
hyperactivity and cognitive decline in AD models. For instance, in transgenic models
where early epileptic discharges have been observed, an increase in the number of
GABAergic presynaptic boutons and GABAergic terminals in cortex (Bell et al., 2003;
Born et al., 2014), CA1-3 hippocampal regions (Hollnagel et al., 2019) and dentate
gyrus (Palop et al., 2007) have been noted.
As the disease progresses and once amyloid deposits are present, the number of
GABAergic presynaptic boutons is severely decreased near plaques in the cortex of
APP/PS1 mice at 18 months of age (Bell et al., 2006). A diminishing number of
GABAergic cortical terminals at the perisomatic level immediately adjacent to Ab
plaques has also been observed in AD patients (Garcia-Marin et al., 2009; Hardy et al.,
1987), as well as general lower levels of GABA neurotransmitters in temporal cortices
(Gueli and Taibi, 2013; Seidl et al., 2001). In particular, somatostatin-expressing
GABAergic hippocampal interneurons, which are essential for learning and memory
(Davies et al., 1980; Siwani et al., 2018), appear to be preferentially affected. In a
complete longitudinal study spanning the age of 4 to 11 months, Schmid et al. (2016)
imaged this population in APP/PS1 mice in vivo, and found, dependent on plaque
proximity, progressive axonal loss starting at 5 months of age and additional plasticity
deficits after a learning task. Related findings have been described in temporal lobe
epilepsy models and patients (Dinocourt et al., 2003; Robbins et al., 1991), as well as in
AD patients, where somatostatin-like immunoreactivity is reduced in cortex and
37
hippocampus (Davies et al., 1980; Davis et al., 1999; Grouselle et al., 1998; Rossor et
al., 1980).
Consistent with a reduction in GABAergic inhibition, a decreased number of
GABAergic neurons in the hippocampus has also been found in the tgCRND8 (Krantic
et al., 2012), APoE4 knockin (G. Li et al., 2009) and 3xTg AD models (Zallo et al.,
2018). Specially neuronal loss (50-60%) is markedly pronounced in the stratum oriens
of CA1-3 and dentate regions of APP/PS1 mice, before any other neural loss is
observed in these regions of the hippocampus (Baglietto-Vargas et al., 2010; Ramos et
al., 2006). In AD patients, a reduction of GABAergic neurons measured using GAD65
immunostaining has been noted in the dentate gyrus (Schwab et al., 2013), and region-
specific hippocampal GABAergic morphological changes and neural loss has been
described in PV (Brady and Mufson, 1997) and somatostatin/NPY (Chan-Palay, 1987;
Chan-Palay et al., 1986) interneuron populations. Other cell type changes were not
compared in these hippocampal measurements, but in the entorhinal cortex, GABAergic
interneuron degeneration precedes changes to principal cells, at least in the early
stages of the disease (Mikkonen et al., 1999; Solodkin et al., 1996).
In addition to neurons, astrocytes modulate the brain’s excitatory/inhibition
balance and play an important role in brain homeostasis (McKenna et al., 2002; Pekny
et al., 2016; Rose and Ransom, 1996; Rothstein et al., 1996; Schousboe and
Waagepetersen, 2003; White et al., 2002). Changes in their function appear early on in
AD patients (Carter et al., 2012) and AD animal models (Heneka et al., 2005). For
example, as a response to glutamatergic stimulation, astrocytes can reverse the
function of GABAergic GAT2/3 receptors in slice, increasing GABA release (Héja et al.,
38
2009). This reversal has been shown to lead to an abnormal tonic inhibition in the
5xFAD mouse model, which can be countered by the application of GABA transporter
inhibitors, supporting a dysregulation of GABAergic mechanisms (Wu et al., 2014).
Indeed in this same model, the enzymes GAD65 and MaoB are upregulated and GABA
synthesis is therefore enhanced in areas such as the dentate gyrus (Jo et al., 2014; Wu
et al., 2014). Results from patients further corroborate the involvement of astrocytes in
the early remodelling of the GABAergic system, as the largest signal of MaoB is
detected in activated astrocytes in prodromal AD (Saura et al., 1994). The expression of
astrocytic transporters in patients is clearly altered as well, but these changes are
complex and region-specific (Fuhrer et al. 2017). While GAT3, the human/rat equivalent
of GAT4 in mice, has been found to be upregulated in the dentate gyrus (Wu et al.,
2014), it is downregulated in other hippocampal and cortical areas (Fuhrer et al., 2017).
In Fuhrer et al.’s study, GAT3 downregulation was accompanied by an increase of the
astrocytic transporter BGT1, and a similar co-regulation between GAT3 and BGT1 has
been described after excitatory injury of the hippocampus (Zhu and Ong, 2004) which
the authors propose is a protective mechanism.
1.4.6. Targeting cognitive dysfunction by circuit modulation
Identifying the mechanisms that promote E/I dysfunction in early AD can guide the
development of future therapies. To date, only four drugs have been approved to treat
the symptoms of AD, and they can be separated into two categories:
acetylcholinesterase inhibitors (AChEI) comprising Donepezil, Galantamine and
Rivastigmine, and the NMDAR GluN2B antagonist Memantine. AChEI drugs increase
39
cholinergic transmission and are recommended for the treatment of mild to moderate
AD (Arce et al., 2009; Ballard, 2002; NICE, 2011). Memantine is the only NMDAR
antagonist used by AD patients to prevent the decline in cognition (although the effects
are modest) and is recommended for the treatment of moderate to severe AD
(Limapichat et al., 2013; Matsunaga et al., 2015; NICE, 2011). These drugs only
temporarily reduce the rate of decline, but do not stop its progression.
Presently, treatments focusing on amyloid reduction and clearance have
dominated the therapeutic landscape even though amyloid only weakly correlates with
cognitive decline in the symptomatic phase of the disease. These drugs aim to clear
pre-existing plaques or inhibit the formation of new ones by using monoclonal
antibodies against Ab or by inhibiting the secretases that cleave APP and produce the
plaque-forming Ab isoforms. However, it remains to be determined if eliminating or
reducing Ab will have a significant effect on the cognitive decline in AD patients. A
recent phase III clinical trial with the monoclonal antibody Solanezumab, reduced free
Ab levels by at least 90%, but was unable to clear existing plaques or slow-down
cognitive decline (Honig et al., 2018). Likewise, the beta-secretase inhibitor
Verubecestat was shown to reduce Ab cerebrospinal fluid levels by 75%, but had no
effect in decreasing the rate of cognitive decline (Egan et al., 2018). Similar results have
been found in clinical trials with gamma-secretase inhibitors (Doody et al., 2013) and
although these and other anti-amyloid drugs have been proven ineffective (Biogen,
2019; Sevigny et al., 2016), clinical trials targeting different Ab molecules are still
ongoing and may show promise (Salloway et al., 2014; see Folch et al., 2017 for
review).
40
The overall lack of success of amyloid therapies suggest that once dementia is
present, disease progression is likely independent of Ab production and could be
irreversible. It is reasonable to assume that treatments targeting amyloid should be
implemented early in the disease and not simply focus on plaque clearance, as the
pathological cascade is thought to begin 10 to 20 years before the onset of clinical
symptoms (Bateman et al., 2012; Villemagne et al., 2013). This highlights the need for
developing useful biomarkers which may aid clinicians in the detection of the disease at
earlier stages, and ease the recruiting of patients for clinical trials before the dementia
stage has been reached (Blennow and Zetterberg, 2018).
Interventions targeting mechanisms reducing the E/I imbalance discussed in this
review may provide new therapeutic opportunities to improve cognition and quality of life
in patients. For example, memantine is one such drug that targets NMDAR GluN2B and
can taper the rate of cognitive decline by directly regulating hyperexcitability. Other
glutamate receptor antagonists such as Ifenprodil, a GluN2B selective antagonist, can
prevent Ab induced Ca2+ rise and synaptic plasticity impairments in vitro (Ferreira et al.,
2012; Hu et al., 2009; Rönicke et al., 2011). mGluR5 antagonists show some promise
when tested in models of AD since they can reduce Ab production, seizures, and help
restore LTP (Kazim et al., 2017; Rammes et al., 2011). Overall, these results suggest
the validity of developing drugs targeting the glutamatergic system as they may
potentially provide beneficial effects to help restore E/I balance. Using a combination of
glutamate receptor antagonists with other drugs in the treatment of AD is now being
explored. One such combination is Memantine and AChEIs to potentially achieve
additive positive effects in patients (Parsons et al., 2013; Tariot et al., 2004).
41
Anti-epileptic drugs such as the closely related Brivaracetam and levetiracetam
have been shown to successfully normalize the excitation/inhibition balance observed in
AD mice models. Specifically, when J20 mice are treated with levetiracetam, they show
a reduction in epileptiform activity (such as SWDs) and an improvement in memory
(Sanchez et al., 2012a). Brivaracetam has also been successful at decreasing SWD
hyperactivity in the APP/PS1 and 3xTg AD models while ameliorating spatial memory
(Nygaard et al., 2015). Although their mode of action remains to be clearly established,
brivaracetam and levetiracetam are known to interact with the synaptic vesicle protein
2A (SV2A)(Lynch et al., 2004) and may regulate its expression. Since SV2A knockout
animals or mice with missense mutations of SV2A have severe seizures, these drugs
may regulate the expression of the Ca2+ sensor protein Syt1, thereby changing the
sensitivity of synaptic vesicles to Ca2+, and specifically affecting the modulation of
GABA release in the hippocampus (Tokudome et al., 2016). In humans, these drugs
have shown promising results as levetiracetam administered to patients suffering from
mild cognitive impairment (which often progress to AD), results in reduced hippocampal
hyperactivation as measured by BOLD levels, and improve performance in a memory
task (Bakker et al., 2012).
Medications that restore E/I balance which are already clinically approved could
be potentially repurposed as valuable disease modifying treatments for AD and should
be taken into consideration. One such example is Acamprosate, a synthetic GABA
analog that is thought to interact with NMDARs (Qatari et al., 1998). Acamprosate is
often prescribed as an anti-craving medication to prevent alcohol relapse and can
decrease glutamate levels and reduce hyperexcitability (Dahchour et al., 1998; Kalk and
42
Lingford-Hughes, 2014; Umhau et al., 2010). Other examples include Baclofen, a
GABAB receptor agonist, which in conjunction with Acamprosate is currently being
tested in AD patients (Chumakov et al., 2015).
Beyond pharmacology, novel therapies such as deep brain stimulation (DBS), a
neurosurgical procedure able to focally modulate circuit activity, are being developed
(Lozano et al., 2016; Smith et al., 2012). In rodents, there is strong evidence that
memory can be enhanced by indirectly stimulating the hippocampus through electrical
activation of associated regions or the white matter tracts that connect them. Stimulation
of the entorhinal cortex (Stone et al., 2011), fornix (Hescham et al., 2017, 2016),
hypothalamus (Soriano-Mas et al., 2005) and anterior thalamic nuclei (Hamani et al.,
2011) have been associated with significant improvement in spatial memory in healthy
animals. Memory deficits are also abolished on spatial learning tasks, particularly after
stimulation of the fornix (McNaughton et al. 2006- tetracaine; Zhang et al. 2015- Aβ 1-42
injection; Hescham et al. 2013- scopolamine injection; Hao et al. 2015- RTT mouse
model; Gallino et al., 2019; Mann et al., 2017; Xia et al., 2017b; - AD transgenic
models).
In humans, evidence from drug-resistant epileptic populations and in one case
of morbid obesity appears to reinforce the idea that stimulation of hippocampal
associated areas may improve memory (Fell et al., 2013; Hamani et al., 2008;
Koubeissi et al., 2013; Miller et al., 2015; Oh et al., 2012; Suthana et al., 2012). In
patients with AD, a handful of studies particularly on the effect of fornix DBS have been
conducted. In an initial Phase 1 clinical trial, six patients with AD who were treated with
fornix DBS for a year showed that the implant procedure and stimulation were well-
43
tolerated, together with improvement in a subset of symptoms. A possible slowing of
cognitive decline and increased glucose metabolism in the temporal lobe as measured
with PET were observed, together with slower rates of hippocampal atrophy (Laxton et
al., 2010; Sankar et al., 2015; Smith et al., 2012). Similar results were found in a case
study by Fontaine et al. (2013). In a subsequent phase 2 clinical trial, no significant
differences in cognitive scores were observed in patients who had received fornix DBS
treatment vs sham stimulation, although if patients were segregated by age, a positive
cognitive change was noted if 65 or older (Holroyd et al., 2015; Lozano et al., 2016;
Ponce et al., 2015). Finally, fornix stimulation has been recently reported, in a case
study, to elicit memory flashback in a portion of AD patients (Deeb et al., 2019).
Taken together, the evidence presented here suggests that fornix DBS may have
the potential to modulate the memory network and reduce cognitive decline at least in a
subset of patients. Up to now, these studies have only employed high frequency
parameters, while stimulating chronically well in advance of any cognitive/memory
testing, and the effectivity of other stimulation paradigms remains unknown. A
systematic characterization of how these and other stimulation paradigms affect E/I
balance and memory mechanisms in the context of disease will be critical for the
efficient development of optimal stimulation protocols for AD.
1.4.7. Conclusions and Outlook
Although it is becoming increasingly clear that an E/I imbalance and epileptic
activity may be an early stage dysfunction in the brains of AD patients, non-convulsive
network anomalies may have gone unnoticed and undiagnosed in the patient
44
population, and it is not apparent when and how these anomalies start or progress.
Despite an increased number of studies pointing to multiple alterations to excitatory and
inhibitory mechanisms, the interrelation between these systems and how their
interaction progressively changes should be considered from the context of AD. Before
plaques are present in AD brains, APP and amyloid beta are able to disrupt both
glutamatergic and GABAergic signaling, thus upsetting the networks’ fine
excitatory/inhibitory balance. Glutamate uptake and enhanced glutamatergic signaling
via changes in NMDARs and mGluRs are involved in synaptic dysfunction in the early
stages of AD, and pharmacologically blocking these receptors shows promising results.
Recent research has also highlighted the changes in GABAergic innervation and
impaired firing of fast-spiking GABAergic neurons, and how antiepileptic drugs may be
able to abolish hyperexcitability and ameliorate cognitive decline. Finally, the
importance of astrocytes in balancing the system by reversing GABAergic transport
function and increasing GABA synthesis should also be considered, as astrocytic
alterations are detected early on in patients. These results suggest that determining the
effect and causes leading to neuronal network imbalance in neurodegenerative
diseases is an important question that spans beyond neuronal death and can lead to
the development of potential therapeutic targets.
Based on results from recent anti-amyloid clinical trials, it has become apparent that
reducing Ab levels in the later stages of AD is unlikely to result in better patient
outcome, highlighting the need to develop useful preclinical biomarkers. The use of
additional treatment approaches to modulate early E/I imbalance in AD may offer
45
promising disease-modifying strategies. Further studies on the effect of antiepileptic
drugs and other inhibitory modulating drugs in AD should be considered.
1.5. Hypothesis and Aims
Hypothesis: The modulation of hippocampal activity will improve memory
performance in an Alzheimer’s Disease mouse model.
To test this central hypothesis, the current work will address the following aims:
• Our first aim is to describe the early hippocampal oscillatory alterations in AD and
assess if a corresponding memory deficit is present. In chapter 2, we use
electrophysiological recordings of area CA1 in freely-moving mice, to evaluate
state-dependent hippocampal alterations in the J20+ APP-overexpressing AD
mouse model.
• The second aim of this project is to determine if rhythmic activation of septo-
hippocampal projections modulates oscillatory hippocampal alterations in J20+.
Using recordings from freely-moving animals, the effect of fornix optogenetic
stimulation is assessed on hippocampal rhythms, hyperactivity, and memory.
• Our final aim is to compare two stimulation paradigms on memory rescue by
employing electrical fornix stimulation, a potential therapeutic treatment tested in
AD patients. We used a similar physiological pattern to that employed in chapter
3 and contrasted its effects to the high-frequency paradigm applied in clinical
settings.
46
Chapter 2: Characterization of early hippocampal dysfunction and
spatial memory in Alzheimer’s Disease
2.1. Introduction
Currently, one of the critical efforts in Alzheimer’s disease research is the
identification of early markers to aid with diagnosis and guide the development of
effective therapies. The advanced stage at which the disease is typically diagnosed is
partly why existing disease-modifying therapies may have only a limited symptomatic
benefit. Indeed timing may be crucial for therapeutic intervention to be effective (Wolfe,
2016). Notably, before neural loss becomes widespread, it is thought that an initial
synaptic failure leading to hippocampal dysfunction gives rise to the memory deficits
associated with AD (Selkoe, 2002; Small et al., 2001). Even at the disease’s end stage,
as indicated by postmortem studies, synaptic loss is more robustly correlated with
premortem cognitive deficits than the number of amyloid plaques or tau tangles,
neuronal loss, or cortical gliosis (Terry et al., 1991). In accordance, compelling evidence
from the rodent literature indicates that synaptic and behavioral alterations arise in AD
before plaque deposition is apparent (DʼHooge et al., 1996; Giacchino et al., 2000;
Mucke et al., 2000; Oddo et al., 2003).
Neurophysiological tools are able to detect alterations in neural network activity
in patients (Babiloni et al., 2009; Rossini et al., 2007) and animal models (Palop et al.,
2007). Although network alterations are a reflection of these early excitatory-inhibitory
imbalances (Hollnagel et al., 2019; Martinez-Losa et al., 2018; Verret et al., 2012;
Volman et al., 2011; Wulff et al., 2009; Zarnadze et al., 2016), apparent rhythmic
47
alterations such as decreases in theta-gamma coupling or in theta and gamma power
have mainly been described once plaques are already present in vivo (Etter et al., 2019;
Iaccarino et al., 2016; Palop et al., 2007; Rubio et al., 2012; Verret et al., 2012), earlier
in the disease if under anesthesia (Hamm et al., 2017; Scott et al., 2012) or in vitro
(Goutagny et al., 2013; Mondragón-Rodríguez et al., 2018). Whether these same
network deficits can be detected in freely-behaving mice is unclear, with studies either
showing no (J20+, (Rubio et al., 2012)), slight (5xFAD (Siwek et al., 2015)) or overt
(APP23 (Ittner et al., 2014)) changes in power. Electrophysiological techniques have the
potential of providing a sensitive marker for aiding in early AD diagnosis, but the
predictive utility of currently employed measures, such as theta and gamma power,
appears to be lacking.
This chapter explores possible markers of early dysfunction in J20+ mice, an
APP-overexpressing mouse model of Alzheimer’s disease. To characterize early
alterations that may be relevant to spatial memory, we recorded from the hippocampus
of freely moving mice, before plaque deposition was apparent. Recordings were
analyzed in a state-dependent manner by segregating sleep and wake periods. First,
we compare the CA1 LFP of age matched J20+ and control littermates during 4 hours of
homecage recordings, assessing frequency bands which have been widely linked to
memory processes (see section 1.2). Then, we provide a detailed characterization of
spike-wave discharges (SWD), hypersynchronous aberrant events which clearly reflect
existing excitatory-inhibitory network imbalances. Lastly, we evaluate J20+ memory
performance in two hippocampal-dependent tasks and correlate the existing
impairments to SWD rate.
48
2.2. Methods
2.2.1. Animals
Hemizygous (J20+) male and female mice expressing a mutant form of the human
amyloid protein precursor bearing both the Swedish (K670N/M671L) and the Indiana
(V717F) mutations (APPSwInd) under the control of the PDGFB promoter [J20 (PDGF-
APPSw,Ind) (Mucke et al., 2000), (Jackson laboratory)] and their non-transgenic
littermates were used in all experiments. For the collection of in vivo data, 3 to 5 months
old mice were used. After surgeries, mice were housed individually in polycarbonate
cages at constant temperature (22° C) and humidity (30-50%) and were kept on a
regular circadian cycle (12 h:12 h light: dark cycle, lights on at 8 a.m.). Mice were
provided with food and water ad libitum. Animals were treated according to protocols
and guidelines approved by McGill University and the Canadian Council of Animal Care.
Animals used in this chapter were injected, and an optic fiber was implanted as they
were also used in the optogenetic experiments of chapter 3. Therefore, the viral
injection and optic fiber implant procedures are further detailed in sections 3.2.2 and
3.2.3 respectively.
2.2.2. Electrode implantation for in vivo experiments
At ~12 weeks mice were anesthetized with isoflurane (5% induction, 0.5-2%
maintenance). The skull was completely cleared of all connective tissue and thoroughly
dried using alcohol. Teflon-insulated tungsten electrodes of 101.6 µm diameter were
used for LFP hippocampal CA1 recordings. For tungsten electrode placement, holes
were drilled through the skull above the dorsal hippocampus (AP, -1.8; ML, +1.12, 1.45).
49
After gently cutting the dura, the electrode array was first coated with DiI and then
slowly lowered 1.4 mm (relative to Bregma) with the end target being the stratum
radiatum of the dorsal CA1 region. For 32-channel (25 μm site spacing oriented
vertically) silicon probe implantation targeting the pyramidal layer of CA1 (N=2), the
same procedure was used with slightly different coordinates (probe tip target: AP, -1.8;
ML, +1.3; DV, – 1.3). Screws placed in the bone above the frontal cortex and
cerebellum served as ground and reference, respectively. Additionally, for the tungsten
electrode surgeries, an EEG screw was placed in the skull above the hippocampus in
the contralateral hemisphere (AP, -1.8; ML, -1.35), as well as 2 EMG electrodes
consisting of stranded tungsten wires that were inserted into the neck musculature to
record postural tone. Following optic fiber, electrode, ground, and reference placement,
dental cement was applied to secure the implant permanently to the skull.
For tetrode experiments (n=3), three anchor screws were secured to the skull,
and a ground wire was positioned either above the cerebellum at midline position or the
left visual cortex. A ‘versadrive’ containing four independently movable tetrodes (Axona,
Inc) was implanted on top of the right hippocampal CA1 at the following stereotaxic
coordinates: (AP, -1.9; ML, -1.5). Tetrodes were gold-plated to lower impedances to
150-250 kΩ at 1 kHz prior to surgery. Tetrodes were lowered 0.5mm from the dorsal
surface of CA1.
2.2.3. Post-surgery habituation and electrophysiological recordings
Following 1 week of post-surgical recovery and one week of habituation to being
chronically tethered, LFP from implanted mice was recorded. All recorded signals from
implanted electrodes were amplified by the tether pre-amplifier before being digitized at
50
32 KHz using a digital recording system (Neuralynx, USA). Prior to any behavioral
experiments, LFP was recorded for 4 h at the start of the light cycle as a baseline
recording.
2.2.4. Novel object place recognition procedure and behavioral analysis
Mice were tested in the novel object place recognition task, a modified version of
the novel object recognition task (Antunes and Biala, 2012). The test area consisted of
a 30.5 cm3 open-topped square container (Boyce et al., 2016). The bottom and walls of
the container were painted with white waterproof paint, and unique black patterns were
present on each of the 4 walls. The test was run on two consecutive days. On the first
day of testing (test area habituation/baseline (Day 0)), mice were placed in the empty
test area and allowed to explore freely for 10 minutes before being returned to their
home cage. For the second day of testing (Day 1), the animal received two sessions in
the behavioral apparatus. In the training session, two identical yet individually identified
objects (Object 1 and Object 2) were each placed in a randomly assigned quadrant
within the test area, and mice were again allowed to explore freely for 10 minutes before
being returned to their home cage. Upon being placed back in their home cage,
patterned light pulses were delivered via the optic fiber patchcord/implant (as described
in section 3.2.3), for light-control purposes. After the 4 h post-training period, the second
session took place. The same two identical objects were placed within the test area as
done on the session prior, with the exception that Object 2 was located in a different
quadrant relative to the training session position. Mice were given 10 minutes to explore
the test area before being returned to their home cage. Both the test area and each of
the identical objects used in this task were thoroughly washed with Peroxyguard before
51
every test session. Test sessions on each day were recorded with an overhead video
camera. For analysis of novel place recognition data, the time spent exploring (sniffing
while facing the object within a 2 cm radius) each object during the test period was
measured for each mouse on the training and test sessions of Day 1. For statistical
analysis, the preference of mice for Object 2 exploration was determined by the
following equation: Object 2 recognition index, RI=(Object 2 exploration (s)−Object 1
exploration (s)) / (Object 2 exploration (s)+Object 1 exploration (s)).
Movement data for mice during each test session was completed through
analysis of video recordings from each test session using the Tracking software.
2.2.5. Passive avoidance procedure and behavioral analysis
A standard passive avoidance conditioning protocol was used (Ogren and Stiedl,
2010). Testing was completed over one week and always took place within the first 3
hours of the onset of the light cycle (8 a.m. to 11 a.m.). On the first day of testing, each
mouse underwent habituation to the apparatus, where they freely explored the entirety
of the box for 20 minutes, followed by the passive avoidance training protocol 20
minutes later. The floor of the test area consisted of a metal grid through which
electrical current could be applied, while the wall panels were composed of opaque
black Plexiglas. The apparatus was divided into two rooms by a guillotine door, one with
a roof composed of black Plexiglas and the other uncovered and illuminated with a
lamp. The mouse behavior was recorded with a video camera. During the training
session, the guillotine door was closed as soon as the animal entered the dark
compartment. The conditioning protocol consisted of two 1 second of 0.5 mA current,
spaced by a 5 second interval, and delivered through the metal grid floor as soon as the
52
door was closed. The animal was returned to its homecage 10 seconds after the shock
was delivered, and patterned light pulses were delivered via the optic fiber
patchcord/implant system implant, as described in section 3-2-3, for light-control
purposes. Additional memory recall testing was done one day and one week after
training, by letting the animals freely explore the box for 10 minutes. Between each
session, the test area was cleaned thoroughly with Peroxyguard. Passive avoidance
memory performance was assessed by quantifying the latency to cross to the dark
compartment by visual inspection of the recorded videos. The R package ARTool was
used to perform a mixed ANOVA with Aligned Rank Transform of the data, since the
residuals did not follow a normal distribution. Interactions were tested using the phia R
package with a Bonferroni Holms correction (Wobbrock et al., 2011).
2.2.6. Vigilance state architecture analysis
For all recordings, EEG, CA1 LFP and EMG data were plotted, and the vigilance
state was manually scored in 5s epochs using a custom-written Matlab program.
Scoring was based on visual characteristics of the EEG, CA1 LFP, and EMG data, fast
Fourier transform analysis of each epoch scored as well as video monitoring of mouse
behavior. Wakefulness was defined by a de-synchronized low-amplitude EEG and CA1
LFP and tonic EMG activity with periods of movement-associated bursts of EMG
activity. For optimal experimental objectivity, no epochs were excluded from analysis
due to the presence of movement-related artifacts. NREM sleep was defined as
synchronized, high amplitude, low-frequency (delta, 1-4 Hz) EEG, and CA1 LFP activity
that was accompanied by reduced EMG activity relative to that observed during
wakefulness. REM sleep was defined as having reduced delta power, a prominent theta
53
rhythm (4-10 Hz), and an absence of tonic muscle activity. Hypnogram analysis
(cumulative duration and average episode duration for each vigilance state) was
completed using custom scripts in Matlab.
2.2.7. Electrophysiological analysis of in vivo CA1 LFP and unit data
All analyses were performed using custom scripts written in MATLAB
(MathWorks) unless otherwise noted. Electrodes used for spectral analysis were
localized in the stratum radiatum. Only the data from one recording electrode was
analyzed per animal. Vigilance state-specific power spectral analyses were performed
with the Chronux toolbox (Bokil et al., 2010) using a moving window Fourier transform
with a window size of 5 s and step size of 1 s. For 16 channel probe analysis, current
source density (CSD) (Nicholson and Freeman, 1975) was calculated from 16-channel
silicon probe LFP recordings using the following equation:
𝐶𝑆𝐷(𝑥,𝑡)=𝜎(2(𝑥,𝑡)−(𝑥+𝛥𝑥,𝑡)−(𝑥−𝛥𝑥,𝑡))(Δx)²
where (x,t) is the potential at depth x for time point t, with Δx of 50 μm. Conductivity (σ)
was assumed constant, and units for CSD are reported as mV/ μm2.
For tetrode experiments, single units were manually isolated from 600 – 6000 Hz
filtered traces. Spike waveform thresholds were adjusted before commencing each
recording and ranged between 35-140 µV depending on unit activity. Clusters were
manually drawn using graphical cluster cutting software (Plexon, Inc) individually for
each recording session. Neurons were separated based on the peak amplitude and
principal component measures of spike waveforms with criteria similar to what has been
discussed elsewhere (Csicsvari et al., 1998). Briefly, only units that formed clusters with
54
clear boundaries were used for subsequent analysis. Additional requirements for single
units included the presence of a clear refractory period of at least 2 ms between
subsequent spikes determined through inter-spike interval analysis. Cross-correlations
were used to confirm single-unit isolation.
For 32 channel probe experiments, waveform extraction and spike sorting was
conducted using SpikeDetekt and Klustakwik, followed by manual adjustment of the
waveform clusters using phy (Rossant et al., 2016), as per the requirements described
above. Once sorting was complete, timestamp and spike waveform data of isolated
units was transferred into Matlab for further analysis.
2.2.8. SWD state-specific analysis of CA1 LFP
Detection of transient CA1 LFP SWDs was done over 4 h of recordings. For each
recording, the signal was first bandpass filtered (CA1 LFP, 250- 600 Hz), and the
absolute amplitude was computed. The mean and standard deviation (SD) for the
absolute amplitude was calculated and the detection threshold was set at 10 SDs above
the mean. Values for detection threshold were selected based on accuracy of automatic
detection that was then validated by manual scoring of observed SWDs. All
automatically detected SWDs were screened manually with the help of the EEG trace.
The number of SWDs per state was then normalized by the time spent in each state.
2.2.9. Ripple analysis
Detection of transient CA1 LFP ripple activity during NREM sleep was completed
on 20s windows of continuous NREM sleep that occurred during the first hour of the
recording period. For each window, the signal was first bandpass filtered (100-250 Hz)
55
and the absolute amplitude was calculated. The mean and SD for the absolute
amplitude were determined, and the detection threshold was set at 5 SD above the
mean for CA1 LFP ripple analysis, with 2SD for the edges. Values for detection
threshold were selected based on accuracy of automatic detection as compared to
manual scoring. CA1 LFP ripples were detected by taking above threshold data time
points that also passed additional criteria (minimum duration between successive
ripples = 30 ms, maximal duration =100ms).
2.2.10. Phase-amplitude coupling
For analyses of coupled oscillations, theta phase was obtained by bandpass
filtering signals between 4 and 10 Hz and performing a Hilbert transform. The theta
phase obtained was then binned in 40 bins of 9°, and low as well as high gamma power
was then averaged for each theta phase bin. The modulation index (MI) was computed
as the Kullback–Leibler distance between binned gamma amplitudes across theta
phases and a linear distribution of gamma amplitudes (Tort et al., 2010).
2.2.11. Statistical analysis
All data are presented as mean ± standard error of the mean (SEM) and statistic
test details are described in the corresponding results. The normality o the residuals
was assessed using the Shapiro–Wilk test and parametric tests were used only when
distributions were normal (non-parametric tests are described where applicable). P <
0.05 was considered statistically significant.
56
2.2.12. Histological confirmation of electrode/optic fiber placement
Following completion of all in vivo experiments, mice were deeply anesthetized
with ketamine/xylazine/acepromazide (100, 16, 3 mg/kg, respectively, intraperitoneal
injection). Mice were then perfused transcardially with 4% paraformaldehyde in 1 x PBS
(PFA). The brains were then extracted and postfixed overnight in PFA at 4 º C and
subsequently cryoprotected in 15% sucrose dissolved in PBS for an additional 24 h at 4
º C. Each brain was then flash frozen and sectioned at 35 μm using a cryostat and
processed as free-floating sections. Odd sections were collected, mounted on glass
slides and coverslipped with fluoromount mounting medium containing 4′,6-diamidino-2-
phenylindole for confirmation of electrode placement. Only mice with histologically
confirmed placement of at least one electrode in CA1 stratum radiatum were used in the
present study. Additionally, only mice with histologically confirmed placement of at least
one tetrodes or probe in the CA1 pyramidal cell layer were further considered for
analysis of CA1 LFP ripple and unit activity.
2.3. Results
2.3.1. Time spent in REM, wake, and NREM does not differ in J20+ transgenic AD mice
compared to non-transgenic littermate control animals
Using EMG, EEG, and CA1 LFP recordings, we characterized the sleep
architecture in freely moving mice during 4 h recordings in their homecage. Transgenic
AD mice (J20+) and non-transgenic age-matched littermates (controls) did not
significantly differ in the amount of time spent in wake, NREM, or REM sleep (Fig. 2.1
A-B). To evaluate sleep episode fragmentation, we quantified the number of episodes
57
per state (Fig. 2.1 C), during the same period and found no significant differences
between groups.
2.3.2. Shift in hippocampal theta peak frequency in AD
2.3.2.1. REM sleep
After scoring the recorded traces for sleep-wake periods, state-dependent power
spectral analysis of the hippocampal CA1 LFP was conducted to assess if any early
alteration existed in J20+ mice (Fig 2.2 A-B). First, we analyzed hippocampal activity
during REM sleep. Fig. 2.2 C shows representative raw field recordings obtained from
Fig. 2.1: Sleep Architecture in controls and J20+ mice. A. Percentage of time spent in REM,
NREM and wake in controls and J20+ mice. B. Cumulative time spent in seconds per stage. C.
Number of episodes quantified in wake, NREM and REM sleep. Data are expressed as mean ±
SEM. Control n =6, J20 n=11; two-way mixed ANOVA with Sidak’s multiple comparisons, non-
significant.
58
the two groups, as well as the filtered traces at different frequencies. We then examined
power spectra during REM sleep using a moving window analysis (Fig 2.2 D). No
significant differences were observed in the spectral power of theta (4-10 Hz), low and
high gamma (30-60 and 61-120 Hz, respectively) or ripple (125-250 Hz) frequency
bands (Fig 2.2 E). Interestingly, a significant shift towards lower frequencies in the theta
band was observed in J20+, (Fig. 2.2 F; control: 7.282± 0.055 ; J20+: 6.796± 0.1374
unpaired two-tailed t-test p=0.0069) whereas other measures of theta band power
remained unaltered (Fig. 2.2 G-H). Finally, we also examined the coupling of both low
and high-frequency gamma to the different phases of theta, and although coupling
appears reduced for J20+ in the mean comodulation plots (Fig 2.2 I-K), we found no
significant differences in the mean theta-gamma MI between control and J20+ mice (Fig
2.2 J-L).
2.3.2.2. Wake
The same analysis was performed for wake periods to examine if any
deficiencies in the LFP between controls and J20+ were present (Fig 2.3.A). As in REM
sleep, no significant differences were detected in the band power of theta, low or high
gamma or ripple frequencies (Fig 2.3 B-D). However, a similar significant decrease in
theta peak frequency was observed (Fig 2.3.E, control: 6.671 ± 0.1715. J20+: 5.835
±0.1911, unpaired two-tailed t-test p=0.0129) with mean and maximum theta band
power no different from controls (Fig 2.3 F- G). Importantly and in contrast to REM
sleep, wake theta-low gamma coupling was significantly impaired in J20+ (Fig 2.3 H- I.
control: 0.78 ± 0.016, J20+: 0.725±0.017 unpaired two-tailed t-test p=0.0424). Although
59
theta-high gamma coupling again appeared decreased in the comodulation plot, this
change was not significantly different (Fig. 2.3 J-k).
60
*
61
Fig. 2.2: Theta frequency decreased in J20+ during REM. A. Electrode recording site
located in stratum radiatum of CA1 and illustration of homecage recording during sleep.
B. Example 1 h hypnogram and representative REM sleep spectrogram. C. Example of
raw and filtered CA1 trace together with EMG during REM sleep for control and J20+
subject. D. Mean normalized spectrogram of all REM. E. Band power for theta, low
gamma, high gamma and ripple frequencies. F. Peak theta frequency. G. Mean peak
power of theta band. H Maximum peak power of the theta band. I. Low gamma
amplitude coupling to theta phase. J. Mean MI for theta-low gamma coupling. K. High
gamma amplitude coupling to theta phase. L. Mean MI for theta-high gamma coupling.
Data are expressed as mean ± SEM. Control n =6, J20 n=11; Unpaired two-tailed t-
tests, *p< 0.05
62
Fig. 2 3: Theta frequency decreased in J20+ during wake and impaired theta-low
gamma coupling. A. Example 1 h hypnogram and wake spectrogram. B. Example of
63
raw CA1 trace together with EMG during wake for control and J20+ subject. C. Mean
normalized spectrogram of all wake. D. Band power for theta, low gamma, high gamma
and ripple frequencies. E. Peak theta frequency. F. Mean peak power of theta band. G
Maximum peak power of the theta band. H. Low gamma amplitude coupling to theta
phase. I. Mean MI for theta-low gamma coupling. J. High gamma amplitude coupling to
theta phase. K. Mean MI for theta-high gamma coupling. Data are expressed as mean ±
SEM. Control n =6, J20 n=11; Unpaired two-tailed t-tests, *p< 0.05.
2.3.2.3 NREM
Lastly, hippocampal ripples in NREM sleep were detected in animals that had at
least one recording site in the CA1 pyramidal layer. Measurements of hippocampal
ripples, a significant memory-associated neural activity pattern, also revealed no
differences in their characteristics (Table 2.1)
Table 2.1: Analysis of CA1 cell layer LFP ripple characteristics during NREM
sleep. Ripples occurring in the first 20 minutes of the 4h period. Control: n=3; J20+; n =
6; non-significant, unpaired two-tailed t-test.
Ripple Parameter Control J20+
Number (20min) 765.3 ± 44.12 739 ± 82.86
Density (Ripples/min) 35.84 ± 1.47 31.8 ± 2.8
Ripple Duration (ms) 48.1 ± 0.61 46.45 ± 1.5
Max ripple amp (mV) 0.16 ± 0.017 0.22 ± 0.032
Ripple Interval (s) 1.83 ± 0.61 2.07 ± 0.18
64
2.3.3. Hippocampal hyperactivity during sleep in AD
We next aimed to characterize aberrant hypersynchronous hippocampal activity
in J20+ mice. We detected large amplitude and short duration spike-wave discharges
(SWD) in CA1 LFP recordings (Fig 2.4. A) which have a markedly different LFP and
CSD profile compared with sharp-wave ripples (Fig 2.4 B-C). The SWD events were
absent in control animals (Fig. 2.4.D, control: 1.143± 0.553, J20+:372.6±58.25. unpaired
two-tailed t-test p=0.0001) and therefore specific to J20+.
To further characterize these interictal-like discharges, we performed a state-
dependent analysis of their incidence and features (Fig. 2.5 A). Almost no SWDs were
detected during wake periods, so the majority of subsequent comparisons were
performed in NREM and REM sleep. The width of the SWD spike was significantly
higher in REM in comparison to NREM (Fig. 2.5.B, REM: 8.69±0.808 ms, NREM:
7.33±0.631 ms, paired two-tailed t-test p=0.0467), while no differences were found in
peak amplitude. As mentioned, SWDs were preferentially detected during sleep (REM:
135.8±32.63, NREM:227.3±42.68, wake:9.63±2.509; repeated-measures one- way
ANOVA p=0.0006, Tukey’s multiple comparisons REM vs Wake: p=0.008; NREM vs
Wake: p= 0.0009) and specially during REM when normalized by the time spent per
state (Fig. 2.5.C-D, REM: 4.783±0.881, NREM: 1.755±0.3409, wake: 0.1176±0.0288;
repeated-measures one- way ANOVA p=0.0004, Tukey’s multiple comparisons REM vs
NREM: 0.013, REM vs Wake: p=0.0009; NREM vs Wake: p= 0.0014). The number of
SWDs and the length of the episode duration were strongly correlated for both sleep
stages, but especially so during REM (Fig. 2.5 E-F, REM: p<0.0001 Pearson r=.6454;
NREM: p<0.0001; r=0.4626).
65
**
66
Therefore, we conducted a more in-depth episode-specific investigation where
the occurrence of SWDs was quantified according to the duration of the episode. A two-
way mixed ANOVA revealed a significant STATExDURATION interaction (p=0.0005)
and Tukey’s multiple comparison test confirmed a significant decrease in the number of
SWDs in NREM versus REM when the episodes lasted longer than 60 s (Fig. 2.5 G,
REM >60s: 0.08948±0.016, NREM >60s:0.0268±0.005; REM vs NREM >60 p=0.023).
Moreover, only during REM, the number of SWDs detected in episodes lasting longer
than 60s was significantly higher than the number detected in episodes less than 30s
(REM <30 s: 0.037±0.01, REM >60s: 0.08948±0.016, p= 0.0177).
Lastly, each episode was segmented in three position bins and SWDs were
counted at the beginning, middle or end of each episode (Fig. 2.5 H). A significant
interaction STATExSEGMENT was also found (p=0.0018) and Tukey’s multiple
comparison showed that the incidence of SWDs during the first 10 s of the episode was
significantly lower than during the rest of the episode for both REM (First 10s: 0.039 ±
Fig. 2.4: Spike-wave discharges are specific to J20+ mice. A. Example CA1 trace
during REM sleep showing the presence of SWDs. Bottom left, filtered high frequency
(250 to 750Hz) SWD. Bottom Right, example spectrogram of SWD. B. Example SWD
raw trace profile spanning CA1 with a linear probe, together with its corresponding
CSD. C. Example sharp-wave ripple raw trace profile spanning CA1 with a linear
probe, together with its corresponding CSD. D. Quantification of SWDs in a 4 h period
after onset of light cycle for both controls and J20+. Data are expressed as mean ±
SEM. control n =6, J20 n=11; Unpaired two-tailed t-tests, **p< 0.001.
67
**
** **
**
68
0.008, center segment:0.08 ± 0.013, last 10s: 0.106± 0.017; First 10s vs center
segment p=0.018; First 10s vs last 10s p=0.013) and less so for NREM (First 10s:
0.023± 0.005; center segment: 0.033± 0.007, last 10s: 0.033± 0.007; First 10s vs last
10s p=0.02). Interestingly, significantly lower SWD rates were also detected during the
center segment (NREM vs REM: p=0.037) and last 10s (NREM vs REM: p= 0.0118) of
NREM compared to REM episodes.
To understand in more detail how the hippocampal neural population in J20+
mice gives rise to spike-wave discharges observed in the LFP, we performed a last set
of experiments (n=5) where we recorded unit activity in the pyramidal cell layer of CA1
(Fig. 2.6). For the final population analysis, we built 1s peri-stimulus time histograms
(PSTH) with 10ms bins centered at the spike and normalized the activity of every single
Fig. 2.5: Spike-wave discharge characterization in sleep. A. Example SWD
distribution in a 4h hypnogram. B. Average peak and width of the SWD spike separated
by REM and NREM periods. C. Total number of SWDs per state. D. Total number of
SWDs normalized by time spent per state, in minutes. E. Scatterplot of episode length
in seconds vs number of SWDs in REM sleep. F. Scatterplot of episode length in
seconds vs number of SWDs in NREM sleep G. Average SWD per seconds according
to binned episode duration. H. Average SWD per second according to their timing (first
10 seconds, center of the episode or last 10 seconds) in a given episode. Data are
expressed as mean ± SEM. control n =6, J20 n=11; * p< 0.05, **p<0.001 across groups,
$ p< 0.05 within group
.
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isolated unit to that during a 1s baseline, starting 1.5s before every SWD. Multi-unit
activity (MUA) increased around the time of the spike (Fig. 2.7 A), while the firing
probability of single isolated units was heterogeneous, and increased (Fig. 2.7 B),
remained unchanged (Fig. 2.7 C), or decreased in the bin preceding the peak of the
spike.
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We then proceeded to categorize each unit depending on their activity during the
10ms just before the peak of the spike, with 2SD above the mean baseline being
classified as positively modulated (+, 66.1%), 2SD below the mean baseline as
negatively modulated (+, 27.1%), and the rest as non-modulated (~, 6.7%; Fig. 2.8 A).
PSTHs of the positively modulated units (with the firing probability normalized by
dividing by their baseline activity, so if there was no change the probability gain would
be 1) showed a significant increase in activity during the 10ms before the peak of the
spike in comparison to the 500ms time segment before, and a significant decrease in
the 500ms post-SWD (Fig 2.8B-C, Friedman test p<0,0001; Dunn’s multiple
comparisons: base vs before peak, p<0.0001; base vs post: p<0.0001). Negatively
modulated units significantly decreased their activity 10ms before the spike, at the time
of the spike, and during the 500ms post period after the spike (Fig 2.8 D-E, Friedman
test p<0,0001; Dunn’s multiple comparisons: base vs before peak, p<0.0001; base vs
peak: p<0.0001; base vs post: p<0.0001). Finally, the non-modulated neurons did not
show significant differences in firing just before, during, or after the spike (Fig 2.8 F-G).
Fig. 2.6: Unit activity in J20+ mice. A1. Unfiltered CA1 LFP. Below, filtered trace at
250-750Hz. Red arrows indicate detected SWDs. B1 Multi -unit activity. B2. Two
example isolated units. Inset shows single action potentials recorded by four channels
(different colors) of the tetrodes. Multi-unit and isolated unit examples are from same
tetrode.
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Fig. 2.7: Example PSTHs. A1. Multi-unit activity average waveform. A2. Baseline
PSTH for 35 SWDs; below, multi-unit raster-plot. A3. SWD PSTH; below, multi-unit
raster-plot. A4. Multi-unit baseline PSTH subtracted from SWD PSTH; below binned %
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change, corrected by bin size. B1. Example unit activity average waveform. B2. 200ms
autocorrelogram. B3. Baseline PSTH for the same 35 SWDs; below, unit raster-plot. B4.
SWD PSTH; below, multi-unit raster-plot. B5. unit baseline PSTH subtracted from SWD
PSTH; below binned % change, corrected by bin size. C1. Example unit activity average
waveform. C2. 200ms autocorrelogram. C3. Baseline PSTH for the same 35 SWDs;
below, unit raster-plot. C4. SWD PSTH; below, multi-unit raster-plot. Bins of PSTHs
have 5ms size. Bins of % change computed as follows: Before, -250ms to -7.5ms from
spike peak; -7.5m, from -10ms to -5ms; -2.5ms, from -5ms to 0; 2.5ms, from 0 to 5ms;
After, from 5ms to 250ms. Baselines were taken 1 s before every SWD. Examples from
same tetrode.
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ga
in
ga
in
ga
in
ga
in
** **
** ** **
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Fig. 2.8: PSTHs of all units classified by SWD response. A. Distribution of units
according to response: +, positively modulated; -, negatively modulated; ~ unmodulated.
B, D, E Positively- modulated, negatively- modulated and unmodulated unit PSTHs with
10ms bins. All units were corrected by dividing their respective baselines, a value of 1
therefore reflects no change. C,F,G. Same data around SWD with different bin size:
Base, -500ms to -10ms from spike peak; before peak, from -10ms to 0ms; peak, from 0
to 10ms; Post, from 10ms to 500ms. N=5 animals. **p< 0.001.
2.3.4. Performance impairments during behavior in AD
Our results highlight early aberrations in the hippocampal CA1 network activity of
J20+ mice during non-goal directed behavior in wake and sleep. Next, we investigated
whether these changes were associated with memory performance. Specifically, we
assessed hippocampal-dependent spatial memory in two tasks, the novel object place
recognition memory and passive avoidance tests. In the novel object place recognition
task (Fig. 2.9 A), both groups showed no preference for either object during the sample
phase and explored the objects equally. 4 hours later, the animals were reintroduced
into the open field, where one of the objects was moved to a different location. Because
mice preferentially investigate novel stimuli, if object place memory is intact, mice
should investigate the displaced object more than the non-displaced one (Antunes and
Biala, 2012). Preference for the displaced object (Recognition Index ,RI), was higher
relative to the sample phase in controls only (Fig. 2.9 B, sample: 0.485±0.014, test:
0.607±0.022; two-way mixed ANOVA , interaction p=0.0138; Sidak’s multiple
comparisons training-test: p<0.0001); J20+ did not explore the displaced object more
than during the sample phase, indicating a memory deficit (sample: 0.513±0.015, test:
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0.591±0.028). Control measures revealed no differences in locomotion or motivation
during testing between groups (Fig. 2.9 E-G).
In the passive avoidance task, the latency to cross to the dark compartment
during training was equivalent in both groups (Fig. 2.9 C-D; control:17.5±3.89 s,
J20+:12.78±4.61 s). The next day, mice were tested in the same chamber for 10min
and their latency to cross to the dark compartment was assessed. J20+ mice had
shorter latencies than controls (two-way mixed ANOVA of aligned-transformed data,
interaction p<0.001; Wilcoxon with Bonferroni-Holms adjustment p=0.0026;
control:473.9±53.83 s, J20+:108.55±28.07 s), reflecting a memory impairment. One
week later, the latency to cross to the dark compartment was still significantly lower in
the J20+ (p=0.0055; control:299.1±71.1 s, J20+:20.33±3.63 s).
Lastly, we investigated if the rate of SWDs during sleep correlates with
behavioural performance in the J20+ group. A negative correlation was found between
the #SWD/min observed during REM and the recognition index in the test phase of the
object place recognition task (Fig. 2.9 H; Pearson r=-0.62, p=0.015). This negative
correlation was also found in the same subjects when tested at the 1day mark in the
passive avoidance task (Fig. 2.9 J; Pearson r= -.5057, p=0.046). Such a relationship
was not found for the #SWD/min during NREM, suggesting that the association
between a higher number of SWD events with poorer memory performance is restricted
to REM sleep only (Fig. 2.9 I-K).
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**
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Fig. 2.9: Spatial memory impairments in J20+ mice. A. Schematic of novel object
place memory task. Below cumulative object exploration time for both objects during
sample and test phases. B. Displaced object recognition index. C. Schematic of
passive avoidance task. D. Latency in seconds to enter the dark compartment in the
passive avoidance task. E. Time spent in quadrants during habituation to the open field.
F. Distance traveled during habituation, sample and test sessions in the open field. G.
Cumulative total object exploration time during sample and test phases. H. Scatterplot
of SWDs per minute during REM sleep versus the recognition index of the displaced
object during the test phase. I. Scatterplot of SWDs per minute during NREM sleep
versus the recognition index of the displaced object during the test phase. J. Scatterplot
of SWDs per minute during REM sleep versus the latency to the dark compartment in
the passive avoidance at the 24 h mark. K. Scatterplot of SWDs per minute during
NREM sleep versus the latency to the dark compartment in the passive avoidance at
the 24 h mark. Data are expressed as mean ± SEM; two-way mixed ANOVAs were
performed if not otherwise stated. *p< 0.05, **p< 0.001
2.4. Discussion
In this chapter, we aimed to describe early electrophysiological alterations in the
hippocampus of the J20+ model in a state-dependent manner, including a detailed
characterization of the network’s epileptiform discharges. We show that, compared to
non-transgenic littermates, subtle deficits in state-dependent hippocampal rhythms can
be detected early in the disease progression. Importantly, we found the robust presence
of abnormal large-amplitude spikes, which was associated with spatial memory
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alterations, thereby reflecting a clear hippocampal excitation-inhibition network
imbalance in our model.
2.4.1. Deficits in theta frequency and theta-gamma coupling
We first showed that in 3 to 5 month old J20+ mice, peak theta frequency was
decreased in both wake and REM sleep when compared to wild types. Considering that
amyloid beta deposition begins at approximately 6 months of age in this mouse model
(Mucke et al., 2000), it is interesting to note that the abnormality in theta frequency and
the behavioral impairments described here occurs before amyloid plaque formation is
detected. This slowing of theta is consistent with previous reports in AD patients, where
a slowing of the dominant rhythmic activity has been described (Jackson and Snyder,
2008; Jeong, 2004), as well as in AD mouse models where similar slight decreases in
peak theta frequency are seen in 5xFAD mice during home cage recordings (Siwek et
al., 2015) and in APP-PS1 mice while running (Cayzac et al., 2015). The lower theta
frequency may produce a significant slowdown of the information exchanged in the
hippocampal network, since theta cycles are suggested to serve as a temporal
reference for coordinating local computations across a more extensive network
(Buzsáki, 2002).
Secondly, we found that theta -low gamma phase- amplitude coupling was
significantly reduced during wake periods only. The coupling of gamma oscillations to
theta phase is proposed to support memory processing in rats (Scheffer-Teixeira and
Tort, 2016; Tort et al., 2009, 2008), and humans (Canolty et al., 2006; Canolty and
Knight, 2010; Daume et al., 2017; Händel and Haarmeier, 2009). Moreover, decreased
theta–gamma phase-amplitude coupling has been reported in vitro for the TgCRND8
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and J20+ (Goutagny et al., 2013; Mondragón-Rodríguez et al., 2018) as well as in vivo
in PV-J20 (Etter et al., 2019), 3xTg AD (Mably et al., 2017) and APP23 (Ittner et al.,
2014) AD mouse models, suggesting that altered phase-amplitude coupling could play a
key role in AD pathophysiology. Interestingly, gamma power was intact at the age we
recorded our J20+ mice, while in animals showing Ab plaque deposition, their
impairment in theta-gamma coupling was accompanied by a decrease in gamma power
(Etter et al., 2019; Mably et al., 2017). This may indicate that deficits in theta-gamma
coupling during wakefulness are one of the first observed perturbations in the
hippocampal network.
2.4.2. Hyperactivity during sleep and its link to memory
Next, we investigated neuronal hyperexcitability in young J20+ mice prior to
plaque deposition in the form of spike-wave discharges (SWDs). We find that SWDs
could always and reliably be detected in all J20+ we recorded from, while in controls
SWDs were mostly absent. Interestingly, SWDs occur primarily during sleep and
particularly in REM sleep, although they are also present during periods of quiet
wakefulness at a significantly reduced rate. This finding is consistent with reports in
other AD mouse models in which hyperexcitability has been described when mice are
motionless and especially in REM sleep (Born et al., 2014; Brown et al., 2018;
Gureviciene et al., 2019; Kam et al., 2016). Similarly, depth electrode recordings in AD
patients have also shown that epileptiform discharges are also detected predominantly
during sleep (Brown et al., 2018; Horváth et al., 2017; Lam et al., 2017; Vossel et al.,
2016). These findings have significant implications because sleep subserves important
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functions related to memory consolidation (Boyce et al., 2016; Wilson and McNaughton,
1994), and we hypothesize that SWDs likely disrupt this process.
Highlighting the interaction between hyperactivity and cognition, significantly
faster cognitive decline over a period of 5 years has been observed in AD patients with
recorded epileptiform activity than in those without these events (Vossel et al., 2016).
Indeed, there is reason to believe that SWDs contribute to cognitive disruption based on
previous studies of interictal spikes in epileptic rodents and patients (Kleen et al., 2013,
2010). For example, it has been shown in a rat kindling model of epilepsy that interictal
spikes occurring during a sleep epoch between the learning and test phases of a
memory task disrupts memory consolidation, likely by inducing atypical coupling
between the hippocampus and cortex (Gelinas et al., 2016). Perhaps similarly, Kam et
al have suggested that SWDs may reflect abnormal hyperactivity both locally and
across cortical and hippocampal regions, a phenomena we have also observed in our
model (data not shown). The conclusion that aberrant hyperactivity may affect memory
is also consistent with studies showing that certain anti-epileptic drugs reduce
epileptiform activity in AD mice models and in patients with mild cognitive impairment,
while also having a beneficial effect in memory (Bakker et al., 2012, 2015; Sanchez et
al., 2012a). In line with this evidence, our data shows that the rate of SWDs, specifically
during REM sleep, negatively correlates with memory performance in two hippocampal-
dependent memory tests, the novel object recognition and passive avoidance tasks.
Although on average J20+ mice are significantly deficient in these tasks when
compared to controls, those with lower memory scores tend to have higher rates of
SWDs during REM sleep. In contrast, the incidence of SWDs during NREM sleep did
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not significantly correlate with memory. Furthermore, the number and characteristics of
ripples, which are an important feature of memory consolidation during NREM sleep,
were unchanged from controls. Altogether, our results suggest that hyperactivity,
particularly during REM sleep, may contribute to the early disruption of memory
processes in our model.
In this chapter, our aim was not to describe the mechanisms underlying SWDs,
but we unexpectedly found that both the length and segment of the REM (but not
NREM) episode modulated the incidence of SWDs, with higher SWD rates detected in
longer episodes and towards the end of a given episode. This modulation hints towards
a possible interplay between mechanisms involved in SWD generation and the egress
from REM sleep (Hayashi et al., 2015; Lu et al., 2006), as changes involved in
transitioning out of REM sleep may also promote hyperactivity in networks with
handicapped excitatory/inhibitory balance. Whether a similar significant modulation of
interictal spikes by REM sleep exists in epileptic or other AD models is unknown.
Finally, our unit recording experiments shed some light on how neurons change
their firing rate before, during and after SWDs. Strikingly, the majority of isolated units
(66%) increased their activity during the 10ms before the peak of the SWD and
proceeded to be inhibited especially for the 250ms after the peak. A smaller percentage
(27%), decreased their activity during these same 10ms before the peak of the SWD,
highlighting a heterogeneity of the neural response. This heterogeneity is similar to
reports describing changes in firing rates of single units during pre-ictal activity, where
41% of principal cells increase their firing and 15% decrease it (Fujita et al., 2014;
Toyoda et al., 2015). Reduced unit activity during spikes might be an artifact because,
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during periods of high activity, spike-sorting methods are prone to false negatives
(Cymerblit-Sabba and Schiller, 2012). While acknowledging this caveat, our findings do
suggest that a large proportion of neurons are transiently and aberrantly synchronous in
sleep and also simultaneously inactive, as their firing is also briefly disrupted just after
each SWD.
To conclude, both the hyperactive and rhythmic pathophysiological phenomena
detected in J20+ mice might have detrimental effects on cognitive function. The
temporal coding within neuronal ensembles (Buzsáki, 2002) may be disrupted by the
aberrant oscillatory patterns (i.e., reduced peak theta frequency and impaired theta-
gamma coupling) observed in our AD model. In addition, the normal neuronal input-
output computation could be altered by hyperexcitability during sleep (Hall et al., 2015;
Šišková et al., 2014), likely contributing to the interference of regular memory
processes.
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Supplemental Fig 2.1
Supplemental Fig. 2.1: Unit activity in J20+ mice. A1. Unfiltered CA1 LFP. Below,
filtered trace at 250-750Hz. Red arrows indicate detected SWDs. A2 Multi -unit activity
followed by 10 isolated units from same tetrode. A3. Example view of the isolated unit
clusters plotted using the peak in two of the recording sites of the tetrode. Orange
represents MUA.
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Chapter 3: Effects of optogenetic activation of septo-hippocampal
fibers in AD.
3.1. Introduction
In the previous chapter, we described early alterations in the hippocampus of the
APP-overexpressing J20+ AD mice model. These alterations included a decrease in
theta peak frequency, a decrease of theta-low gamma coupling during wake periods,
and marked aberrant neural hyperexcitability in sleep that correlated with impaired
behavioral performance. Given that AD is the most common form of dementia, and that
current treatments do not effectively halt or reverse the disease (Huang and Mucke,
2012), methods that probe memory and cognition in AD by directly modulating the
hippocampal network are needed to guide therapeutic development. For example,
recent studies in AD mouse models have used optogenetic approaches to stimulate the
hippocampal circuit at plaque-bearing (Etter et al., 2019; Perusini et al., 2017) and pre-
plaque (Roy et al., 2016) stages of the disease, thus reversing their memory deficits and
shedding light on the mechanisms underlying this effect. In these studies, neural somas
were acutely stimulated with light during the testing phase of behaviour, thereby
influencing retrieval. In contrast, current clinical trials that employ chronic electrical
stimulation in AD patients, which could have the potential of improving cognitive
performance, have used different approaches. On one hand, these trials target the
fornix, a bundle of white fiber projections connecting the hippocampus and other
memory structures, such as the medial septum. On the other hand, treatment is not
focused to the retrieval phase of testing as patients receive constant stimulation
encompassing wake and sleep periods, and the effects on cognitive performance is
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assessed once the stimulation device is switched off (Laxton et al., 2010; Lozano et al.,
2016).
To more closely mirror these two clinical trial parameters, our present goal is
twofold: first, we aim to evaluate the effect of optogenetic stimulation on the altered
J20+ hippocampal rhythmic network activity (as described in chapter 2) by specifically
targeting medial septo-hippocampal projections at the level of the fornix. We find that
theta stimulation of the fornix increases peak power at the given stimulation frequency
and observe a significant reduction in hyperactivity during REM sleep. Second, we aim
to evaluate the effect of continuous stimulation on memory by restricting light delivery to
both sleep and non-task related awake periods. Given our findings in the J20+ showing
that hippocampal hyperactivity in the form of SWDs is higher during sleep and how their
incidence correlates with memory task performance, we focused light delivery to the
post-training period. Our results show that stimulation of the fornix after training
improved memory performance in J20+ mice.
3.2. Additional Methods
Most of the methods used in this chapter have been described in the previous
chapter. Refer to section 2.2 for details.
3.2.1. Virus-mediated targeting of opsin and eYFP expression
Adeno-associated virus (AAV) of serotype 2 containing a channelrhodopsin-2
with the E123T mutation for ultrafast control (ChETA construct) fused to eYFP and
driven by the nonspecific synapsin promoter, and control virus AAV2-hSyn-eYFP were
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obtained from the Canadian Neurophotonic Platform, at Laval University. For in vivo
electrophysiology experiments, J20+ mice were injected with AAV2-hSyn-ChETA-eYFP,
while for all other animals and tests AAV2-hSyn-eYFP was used. These viral injections
were to obtain controls for viral injection and light delivery for the optogenetic
manipulation. At ~8 weeks of age, J20+ mice and littermates were anesthetized with
isoflurane (5% induction, 1-2% maintenance). 0.5 μL were stereotaxically injected into
the medial septum (MS) (anteroposterior (AP), +0.86; mediolateral (ML), 0.0;
dorsoventral (DV), -4.5; all coordinates relative to Bregma) through a 28 G cannula at a
rate of 50 ηL/min. The injection needle was lowered through a hole drilled lateral to the
midline (AP, +0.86; ML, -0.5; DV, 4.5) at an angle of ~6.5 ° (in the ML axis) relative to
the vertical plane in order to avoid the sagittal sinus.
3.3.2. Optic fiber implantation for in vivo experiments
At ~ 12 weeks, mice injected for in vivo experiments were anesthetized with
isoflurane (5% induction, 0.5-2% maintenance). The skull was completely cleared of all
connective tissue and thoroughly dried using alcohol. To facilitate light delivery to the
transfected projections from the medial septum, a hole was drilled in the skull above the
dorsal fornix (AP, -.34; ML, -0.5). After gently cutting through the dura, an optic fiber
implant, constructed by cementing a cleaved 200 μm diameter piece of optic fiber into a
ceramic ferrule (Thor labs), was lowered through the hole at an angle of ~4.5 ° (in the
ML axis) with the optic fiber end targeted just above the dorsal fornix (AP, -0.34; ML, -
0.1; DV, -1.9). For optic fiber + LFP recordings the optic fiber was positioned as
described above; however, Teflon-insulated tungsten electrodes of 101.6 µm diameter
were also lowered in hippocampal area CA1 as described in section 2.2.2.
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3.3.3. Light delivery to the fornix
Blue (450 nm wavelength) light was delivered through a fiber optic cord using a
laser diode fiber light source (Doric Lenses, Canada). Light intensity was calibrated and
wavelength-corrected using the Power Meter Bundle with the PM100D Console and
S130C Slim Photodiode Sensorlight (Thorlabs). For each mouse, the minimum amount
of light at the tip of the optic fiber implant that was required to produce maximal effects
on the CA1 theta rhythm was estimated before behavioral experiments were started.
This estimation was done in order to minimize unwanted side-effects, including light-
induced tissue damage as well as excessive disturbance of animals. The estimated
amount of light was based on pre-surgery testing of transmittance of light transmittance
through the optic fiber patch cord and optic fiber implant and was never allowed to
exceed 20 mW. Every stimulation was performed at 50% duty cycle (100ms) with 5Hz
frequency for the duration of 4 h, the day after the baseline was recorded and starting at
the same time.
3.2.4. Immunohistochemistry and construct expression confirmation
Brain extraction and slicing were performed as described in section 2.2.12. To
confirm construct expression and for choline acetyltranferase (ChAT) quantification
experiments, double fluorescent immunohistochemistry for eYFP and ChAT were
performed. Even sections were first washed (3 x 15 min washes under agitation) in PGT
(0.45% Gelatin and 0.25% Triton in PBS). Next, slices were incubated with primary
antibodies (1:1000 chicken anti-GFP from ThermoFisher Scientific and 1:200 goat anti-
ChAT from Millipore) in PGT at 4 º C for 24 h. Following 3 x 15 min PGT washes,
sections were then incubated with secondary antibodies [1:1000 donkey anti-goat
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coupled with Alexa 647, and 1:2000 donkey anti-chicken coupled with Alexa 488 from
Jackson Immunoresearch) for 2 h, followed by PBS rinses and then mounted as
described above.
For PV staining, double fluorescent immunohistochemistry for PV and eYFP was
performed. Sections were first incubated overnight with PGT (0.45% Gelatin and 0.25%
Triton in PBS) at 4°C. Next, slices were incubated with primary antibodies (1:1000 rabbit
anti-GFP from Life Technologies, and 1:500 mouse anti-parvalbumin monoclonal IgG1
from Sigma-Aldrich) in PGT at room temperature for 2 hours. Following washes of 10,
20, and 30 min, sections were incubated with secondary antibodies [1:1000 goat anti-
rabbit coupled with Alexa 488 (Molecular Probes), and 1:500 goat anti-mouse IgG1
coupled to Alexa 555 (Life Technologies)] in PGT for 45 min. Following 10, 20, and 30
min PGT washes, sections were then mounted on glass slides and permanently
coverslipped with Fluoromount mounting medium containing 4′,6- diamidino-2-
phenylindole.
Only mice with histologically confirmed placement of at least one electrode in
CA1 stratum radiatum and optic fiber in the dorsal fornix as well as proper construct
expression in the medial septum were used in the present study. Additionally, only mice
with histologically confirmed placement of probe sites spanning the CA1 pyramidal cell
layer were further considered for analysis of unit activity.
3.3.5. In vitro patch-clamp electrophysiology.
Mice were deeply anesthetized using ketamine/xylazine/acepromazine mix, and
intracardially perfused with N-methyl- d-glutamine (NMDG) recovery solution (4°C)
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oxygenated with carbogen (5% CO2 and 95% O2). NMDG solution contains the
following (in mM): 93 NMDG, 93 HCl, 2.5 KCl, 1.2 NaH2PO4, 30 NaHCO3, 20 HEPES,
25 glucose, five sodium ascorbate, two thiourea, three sodium pyruvate, with the pH
adjusted with HCl to 7.4 before the addition of 10 MgSO4 and 0.5 CaCl2. Following
NMDG perfusion, brains were quickly removed and immersed for an extra 1 min in cold
NMDG recovery solution. Coronal slices (300 µm) were cut using a vibrating microtome
(Leica-VT1000S), then collected in 32°C NMDG recovery solution for 10–12 min. Slices
were then transferred to room temperature and to a bath with oxygenated artificial
cerebrospinal fluid (aCSF) containing the following (in mM): 124 NaCl, 24 NaHCO3, 2.5
KCl, 1.2 NaH2PO4, 2 MgSO4,5 HEPES, 2 CaCl2, and 12.5 glucose (pH 7.4). Patch
pipettes (3–5MΩ) were filled with internal solution, containing the following (in mM): 140
K gluconate, 2 MgCl2, 10 Hepes, 0.2 EGTA, 2 NaCl, 2 mM Na2-ATP and 0.3 mM Na2-
GTP, adjusted pH to 7.7 with KOH, adjusted osmolarity to 290. Slices were transferred
to a submerged recording chamber filled with aCSF (4 ml /min flow rate, 30 °C) that was
continuously oxygenated with carbogen (5% CO2 and 95% O2). Single whole-cell
patch-clamp recordings of septal neurons (identified by YFP fluorescence) were done
after transferring brain slices to the recording chamber. All recordings were done at 32
°C. For recordings, neurons were either tested in current-clamp mode (resting potential
between −60 and −70 mV) or maintained at −70 mV in voltage-clamp mode. For both,
the effect of different intensities of blue light (450 nm) was tested (1–20 mW), using a
laser diode fiber light source (Doric Lenses, QC, Canada). Electrophysiological signals
were amplified, using a Multiclamp 700B patch-clamp amplifier (Axon Instruments, CA,
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USA), sampled at 20 kHz, and filtered at 10 kHz. Data were analyzed using pClamp 10
(Molecular devices, CA, USA).
3.3. Results
3.3.1. Optogenetic targeting of medial septum neurons
To characterize neurons of the medial septum (MS) in mice transfected with
ChETA-eYFP, we first examined virus distribution, specificity, and co-expression with
other cell type markers within the septum. Virus expression, as indicated by eYFP
labeling of cell bodies, was distributed extensively across the MS (Fig. 3.1. A). Since
synapsin is a non-specific neuronal promoter, we quantified the extent of ChETA
expression in cholinergic and GABAergic cell populations in the MS using ChAT and
parvalbumin (PV) immunocytochemistry (Fig. 3.1. B). We found that the eYFP reporter
was widely expressed in PV neurons as 72.5 ± 2.3% of cells positive for PV also co-
expressed eYFP (number of eYFP cells counted: 2770, n = 3 mice). 21 ± 1.7% were
positive for eYFP only indicating that other cell populations were transfected. Co-
expression in cholinergic neurons was very low because only 0.5 ± 0.2% of ChAT
neurons were also positive for eYFP (Fig. 3.1. C; total number of eYFP cells counted:
2697, n = 3 mice). Therefore, the remaining ~21% eYFP labeled cells that were
negative for PV are most likely glutamatergic and non-PV GABAergic neurons, as it has
been previously shown that septal neurons can also express glutamatergic and other
GABAergic markers apart from PV (Kiss et al., 1997; Robinson et al., 2016).
We next determined whether optogenetic activation of medial septum neurons
reliably evoked firing of the transfected neurons in slice, as well as the optimal
illumination intensity to induce spikes (Fig. 3.2. A, B). Since both GABAergic and
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glutamatergic cells were transfected by the viral injection, the firing frequency of MS
cells to injected current was heterogenous, comprising fast, slow and bursting neurons
(Fig. 3.2 C, D). Voltage and current-clamp were used to characterize photocurrent and
depolarization elicited by light. We found that 100ms light pulses induced large
Fig. 3.1: Optogenetics and MS neurons. A. Coronal section showing unspecific
ChETA-eYFP expression from MS neurons and fibers. Blue/cyan: DAPI, green:
eYFP, scale bar 16um. B, ChETA-eYFP expression compared with PV and ChAT.
Scale bar 51 um. C, Quantification of eYFP, PV, ChAT and their co-localization.
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photocurrents and elicited spikes starting at 5 mW laser intensity (Fig. 3.2 E-F). We then
tested the capacity of these neurons to respond to blue light stimulation in semi-natural
firing patterns. Accordingly, we tested the fidelity of MS neural activation to the
application of 50% duty cycle blue light pulses at 5 Hz frequency. When light power was
10mW or above, the fidelity of action potential generation in MS responsive neurons
was 100% reliable on average, with more than 1 spike elicited per pulse at higher light
power intensities (a spike probability of 1 reflects 1 spike per pulse) (Fig. 3.2 G, H).
Overall, these experiments demonstrate that a reliable stimulation of MS
GABAergic/glutamatergic neurons can be achieved in a semi-naturalistic fashion.
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3.3.2 Characterizing the effect of theta stimulation of MS projections in hippocampal
rhythms
To determine whether optogenetic stimulation of the projections from MS
neurons could drive hippocampal networks at theta frequency in vivo, we activated
ChETA-expressing MS projections at the level of the fornix while recording freely
moving J20+ mice in home cage. Hippocampal oscillations were recorded both during
quiet awake and sleep states (Fig 3.3 A). Recordings consisted of 4h baseline followed
by 4h of stimulation and an additional 4h of post-stimulation, spanning the complete
duration of the light-cycle.
Fig. 3.2: In vitro characterization of AAV2-ChETA in medial septum neurons. A, In
vitro recording configuration. B, Example ChETA-eYFP MS neuron visually identified.
Scale bar: 20 μm. C, response of MS cell to injected current in current clamp. D,
Average spike frequency of identified ChETA-eYFP MS neurons in response to current
injection. E, Example photocurrent and depolarization to different light intensities in
voltage and current clamp, respectively. F, Photocurrent (left) and depolarization (right)
in response to each mW laser stimulation. G, Example depolarization in response to 5
Hz, 50% duty-cycle light stimulation at 1, 10 and 20 mW light intensity. H, Example
raster sweep of neuron in response to 5 Hz, 50% duty-cycle light stimulation at 10mW
(top) and associated spike timing histogram in response to each pulse (bottom). To the
right, spike probability for 5 Hz laser stimulation at different light intensities.
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3.3.2.1. Sleep
First, we conducted a state-dependent power analysis to assess the effect of
fornix stimulation. Starting with REM sleep, we compared hippocampal activity during
baseline, stimulation, and post-stimulation sleep periods (Fig. 3.3) using the same
measures as in chapter 2. We confirmed that optogenetic activation of the fornix was
able to drive hippocampal rhythms (Fig. 3.3.B-D). Although activation of the fornix did
not significantly affect the power of the ongoing oscillations (Fig. 3.3. E), the peak
frequency was significantly shifted to the stimulation frequency for the duration of the
stimulation (Fig. 3.3 F; Base: 6.796±0.1374 ; Stim: 5.669±0.2415 Post:6.565±0.1675; n
= 11 per group, repeated-measures one-way ANOVA p<0.0001; Dunnet’s multiple
comparisons, Baseline vs Stim: p<0.0001), and the power at the stimulating frequency
was also significantly higher than in baseline conditions (Fig. 3G; Base: 0.1588±0.02 ;
Stim: 0.4072±0.057 Post:0.1703±0.02271, repeated-measures one-way ANOVA
p<0.0001; Dunnet’s multiple comparisons, Baseline vs Stim: p<0.0001). Both peak
power and peak frequency regained their baseline levels after the stimulation. All other
measures, including the strength of theta-gamma coupling as quantified by the MI, were
unchanged (Fig. 3.3. J-G).
Additionally, we also quantified hippocampal ripples and their characteristics
during NREM in animals that had at least one recording site in the pyramidal layer of the
hippocampus. Measurements of hippocampal ripples revealed no differences in their
characteristics (Table 3-1).
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3.3.2.2. Wake
The same analysis was performed for wake periods to examine the effect of theta fornix
stimulation in the hippocampus of J20+ mice (Fig 3.4). As in REM sleep, optogenetic
activation of the fornix was able to drive hippocampal rhythms (Fig. 3.4.A-D), with no
effect on the power of ongoing oscillations (Fig. 3.4.E). Similarly, the peak frequency in
the theta band was significantly shifted to the stimulation frequency for the duration of
the stimulation (Fig. 3E; Base: 5.835±0.191 Hz; Stim: 5.307±0.167 Hz; Post:5.74±0.185
Hz; repeated-measures one-way ANOVA p<0.0127; Dunnet’s multiple comparisons,
Baseline vs Stim: p=0.0107), and the power at the stimulating frequency was also
significantly higher than in baseline conditions (Fig. 3E; Base: 0.2293±0.0205; Stim:
0.4687±0.093 Post:0.213±0.0193; repeated-measures one-way ANOVA p=0.0036;
Dunnet’s multiple comparisons, Baseline vs Stim: p=0.0077). Both peak power and
peak frequency regained their baseline levels after the stimulation. All other measures,
including the strength of theta-gamma coupling as quantified by the MI, were
unchanged (Fig. 3.4. H-J).
Finally, we investigated the effect of fornix activation in the wake-sleep cycle and
confirmed that continuous stimulation did not alter the sleep architecture of the animals
(Fig. 3.5. A-C).
Overall, these results show that rhythmically activating septal projections can
drive theta oscillations. These results confirm that optogenetic stimulation of the fornix
can modulate hippocampal network activity through septal connections.
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** **
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Table 3. 1: Analysis of CA1 cell layer LFP ripple characteristics during NREM
sleep occurring in the first 20 minutes of the 4h period. Baseline: n = 6, stimulation= 2;
non-significant, unpaired t-test).
Ripple Parameter baseline Stim
Density (Ripples/min) 31.8 ± 2.8 30.184±1.679
Ripple Duration (ms) 46.45 ± 1.5 43.109 ± 1.247
Max ripple amp (mV) 0.22 ± 0.032 0.264 ± 0.084
Ripple Interval (s) 2.07 ± 0.18 2.12 ± 0.085
Fig. 3.3: Optogenetic driving of theta frequency in the hippocampus of J20+
during REM sleep. A. Diagram and coronal sections of viral transfection in the
MS, optic fiber in the fornix and electrode placement in hippocampus. Blue: DAPI,
Green: eYFP, Red, DII. B. Example hypnogram and REM spectrogram during
baseline and B. stimulation. C. Example raw and filtered CA1 trace together with
EMG during REM sleep for baseline, stimulation and post-stimulation recordings.
D. Mean normalized spectrogram of all wake for baseline, stimulation, and post-
stimulation periods. E. Band power for theta, low gamma, high gamma and ripple
frequencies. F. Peak theta frequency. G. Maximum peak power of the theta band.
H. Mean peak power of theta band. I. Mean MI for theta-low gamma coupling. J.
Mean MI for theta-high gamma coupling. Data are expressed as mean ± SEM.
n=11 per group; repeated-measure one-way ANOVA, *p< 0.05, *p<0.001
.
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Fig. 3.4: Optogenetic driving of theta frequency in the hippocampus of J20+ mice
during wake. A. Example hypnogram and wake spectrogram during baseline and B.
stimulation. C. Example Raw CA1 trace together with EMG during wake for baseline,
stimulation and post-stimulation periods. D. Mean normalized spectrogram of all wake
for baseline, stimulation, and post-stimulation periods. E. Band power for theta, low
gamma, high gamma, and ripple frequencies. F. Peak theta frequency. G. Maximum
peak power of the theta band. H. Mean peak power of theta band. I. Mean MI for theta-
low gamma coupling. J. Mean MI for theta-high gamma coupling. Data are expressed
as mean ± SEM. n=11 per group; repeated-measure one-way ANOVA, *p< 0.05.
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3.3.3. Modulation of SWDs by fornix optogenetic activation
Next, we aimed to characterize the effect of fornix stimulation on aberrant
hypersynchronous hippocampal activity in the same cohort of J20+ mice. We employed
the same state-dependent analysis from chapter 2. Starting with REM sleep, the total
number of SWDs as well as the corrected number per time spent in REM sleep, were
Fig. 3.5: Stimulation of fornix fibers does not alter sleep architecture in J20+. A.
Percentage of time spent in REM, NREM, and wake in J20+ mice during 4h baseline,
4h stimulation, and 4h post-baseline. B. Cumulative time spent in seconds per stage. C.
Number of episodes quantified in wake, NREM, and REM sleep. Data are expressed as
mean ± SEM. N=11; two-way mixed ANOVA with Sidak’s multiple comparisons, non-
significant.
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significantly decreased during stimulation, and this effect persisted 4h post-stimulation
(fig 3.6 A-C, total number. Base: 153.7±32.63; stim: 72.09±13.07; post: 79.91±21.65,
repeated-measures one-way ANOVA, p=0.0339. Dunnett’s multiple comparisons, base
vs stim p=0.0315; base vs post =0.06; Corrected, base: 4.783±2.924; stim:2.91±0.546;
post: 3.091±0.8141; repeated-measures one-way ANOVA, p=0.024, Dunnett’s multiple
comparisons, base vs stim=0.024; base vs post =0.041). Although a decrease in the
number of SWDs in NREM was observed, this decrease was not significant (fig 3.6 D-
F). Lastly, the stimulation had no effect in the number of SWDs during wake (fig 3.6 G-
H). Interestingly, the rate of SWDs returned to baseline levels 24 hours after stimulation,
indicating that the decrease was transient (fig 3.7 A; n=5, Dunnett’s multiple
comparisons, REM base vs pos, p=0.0038, base vs post 24h, p=0.7).
Since the vast majority of SWDs occurred during sleep, we focused on further
comparing their characteristics during REM and NREM (fig 3.7). Fornix stimulation had
no effect on the amplitude or width of the spike neither during REM nor NREM periods
(fig 3.7 B-C). REM episode analysis revealed a significant difference in the number of
SWDs depending on episode duration, but not the stage of stimulation (two-way
repeated-measures ANOVA, duration: p=0.0011, stage, p=0.096). Interestingly, the
analysis of SWD distribution per episode showed a significant main difference with no
interaction for both factors, stage (Fig. 3.7 D, supplementary table 3.1 p=0.016) and
segment (Fig. 3.7 E, supplementary table 3.2 p=0.0017, two- way repeated measures
ANOVA). During the first and last 10 s of REM, the ratio of SWDs was reduced
compared to baseline levels, while the center segment was not statistically different
(First 10s, base: 0.039±0.008, stim:0.012±0.002; last 10s, base:0.106±0.017,
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stim:0.056±0.014; Sidak’s multiple comparisons first 10s: base vs stim p=0.0243; last
10s: base vs stim p=0.0103). For NREM, only an effect specific to length (p=0.0147)
and segment (p=0.0002) was observed, but the stage of stimulation was not a
significant factor (Fig. 3.7 F-G, see supplementary table 3.3-4 for multiple comparisons).
Finally, since we found a decrease in the rate of SWDs during stimulation, we
sought to determine if neurons changed their firing probability before or after these
events. We built PSTHs and classified each unit activity as described in chapter 2
(section 2.3.3). We followed the same isolated units during 4h of baseline and 4h of
stimulation by performing the spike detection and sorting in the concatenated recording
(Fig 3.8 A, C). Almost all units (98%) were found to be positively modulated during
baseline (Fig 3.8 B), with the proportion decreasing to 89% during stimulation (Fig 3.8
D). We focused on the positively modulated units and found that the overall firing
probability during the 10ms just before the peak of the SWD was significantly decreased
in the stimulation stage (matched-pair Wilcoxon with Bonferroni-Holms correction
p<0.0001, Fig 3.8 E-G).
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.
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Fig. 3.6: Effect of fornix stimulation on the number of spike-wave discharges. A.
Total number of SWDs in REM per stage (baseline, stimulation, post-stimulation). B.
Total number of SWDs normalized by time spent per REM, in minutes. C. % change of
normalized SWDs between baseline and post-stimulation and baseline-stimulation
periods during REM. D. Total number of SWDs in NREM per stage (baseline,
stimulation, post-stimulation). E. Total number of SWDs normalized by time spent per
NREM, in minutes. F. % change of normalized SWDs between baseline and post-
stimulation and baseline-stimulation periods during NREM. G. Total number of SWDs in
wake per stage (baseline, stimulation, post-stimulation). H. Total number of SWDs
normalized by time spent per wake, in minutes. Data are expressed as mean ± SEM.
n=11 * p< 0.05
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105
Fig. 3. 7: Effect of fornix stimulation in spike-wave discharge characteristics. A.
Total number of SWDs in REM sleep per state and per stage (baseline, post-
stimulation, post- stimulation 24 h later). B. Average peak of the SWD spike separated
by REM and NREM periods. C. Average width of the SWD spike separated by REM
and NREM periods. D. Average SWD per seconds according to binned episode
duration in REM. E. Average SWD per second according to their timing (first 10
seconds, center of the episode or last 10 seconds) in a given episode of REM. F.
Average SWD per seconds according to binned episode duration in NREM. G. Average
SWD per second according to their timing (first 10 seconds, center of the episode or
last 10 seconds) in a given episode of NREM. Data are expressed as mean ± SEM.
n=11 except A (n=5); * p< 0.05 across groups, $ p< 0.05 within group.
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ga
in
ga
in
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3.3.4. Modulation of hippocampal memory by fornix optogenetic activation
Our results so far highlight a decrease in network hyperactivity during
optogenetic stimulation of the fornix. We next investigated whether memory
performance, which we previously showed to be impaired in J20+ mice and correlated
with SWD rate (see chapter 2) could be positively modulated by forniceal stimulation.
We assessed hippocampal-dependent spatial memory in the same two tasks (the novel
object location recognition and passive avoidance tasks), and light was delivered post-
training for 4h in homecage. In the novel object location recognition task (Fig. 3.8 A),
mice showed no preference for either of the objects during the sample phase and
explored the object equally. 4 hours later, the animals were reintroduced into the open
field, where one of the objects was moved to a different location. Preference for the
displaced object was higher relative to the sample phase in optogenetically stimulated,
J20+ ChETA mice (Fig. 3.8 B, sample: 0.509±0.013, test: 0.591±0.028; two-way mixed
ANOVA , interaction p=0.0173; Sidak’s multiple comparisons training-test: control
Fig. 3.8: Effect of fornix stimulation on hippocampal single unit activity. A.
Example raster plot and PSTH (below) of isolated unit during baseline (left) and SWD
(right), in 4h with no stimulation. B. Distribution of units according to response with no-
stimulation: +, positively modulated; -, negatively modulated; ~ unmodulated. C. Same
unit as in A, but during 4h stimulation. D. Same as in B, but during 4h stimulation. E
PSTH of all positively modulated units corrected by their respective baselines F. Same
as in E, but during 4h stimulation. PSTHs with 10ms bins. G Same data as in E ad F
with different bin size: Base, -500ms to -10ms from spike peak; before peak, from -
10ms to 0ms; peak, from 0 to 10ms; Post, from 10ms to 500ms. N=2 animals. *p<0.001
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p<0.0001, J20+ ChETA p=0.0038), reflecting a memory improvement. Control
measures revealed no evidence for differences in locomotion or motivation during
testing compared to controls (Fig. 3.8 E-G, see supplementary table 3.5-7).
In the passive avoidance task, the latency to cross to the dark compartment
during training was equivalent in the three groups (Fig. 3.8 C-D; control:17.5±3.89, J20+
eYFP:12.78±4.61, J20+ ChETA: 27.3±5.53). Light was delivered for 4 hours
immediately after training, and the next day mice were tested in the same chamber for
10min. The J20+ ChETA group had latencies comparable to controls (two-way mixed
ANOVA of aligned-transformed data, interaction p<0.001; Wilcoxon with Bonferroni-
Holms adjustment: control vs J20+ChETA p=0.93; control vs J20+ eYFP p=0.0053;
control:473.9±53.83, J20+:108.55±28.07, J20+ ChETA:429.4±64.96) indicating that the
shock was still remembered. One week later, the latency to cross to the dark
compartment was still significantly lower in the J20+ (control vs J20+ChETA p=0.19;
control vs J20+ eYFP p=0.018; control: 299.1±71.1 s, J20+:20.33±3.63 s, J20+
ChETA:107±37.84 s).
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Fig. 3. 9: Spatial memory impairments in J20+ mice. A. Schematic of novel object
place memory task. Below cumulative object exploration time for both objects during
sample and test phases. B. Displaced object recognition index. C. Schematic of
passive avoidance task. D. Latency in seconds to enter the dark compartment in the
passive avoidance task. E. Time spent in quadrants during habituation to the open field.
F. Distance traveled during habituation, sample and test sessions in the open field. G.
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Cumulative total object exploration time during sample and test phases. Data are
expressed as mean ± SEM; two-way mixed ANOVAs were performed if not otherwise
stated. *p< 0.05
3.4. Discussion
The primary purpose of the current study was to investigate if stimulation of
septo-hippocampal projections is able to rescue memory impairments by reducing early
alterations in hippocampal oscillations in an AD mouse model. To examine this, we
used optogenetics and in vivo LFP recordings to obtain evidence of state-dependent
network modulation. We first demonstrate that optogenetic activation of mainly
GABAergic fibers from the medial septum can drive hippocampal rhythms at a specific
theta frequency. At the level of the fornix, light stimulation could effectively increase
peak power at the given frequency in hippocampal CA1 without changing overall theta
power or theta-gamma coupling. Importantly, the stimulation resulted in a decrease of
hyperactivity during REM sleep, accompanied by an improvement in memory
performance in two hippocampal-dependent spatial tasks.
3.4.1. Stimulation of septo-hippocampal projections modulates hippocampal rhythms
The fornix is a white matter pathway comprising afferent and efferent
connections to and from the hippocampus and is therefore crucial in mediating
hippocampal function. Highlighting the importance of the fornix in memory, its
transection has consistently been demonstrated to impair spatial memory in rodents
(Cassel et al., 1998; Winters and Dunnett, 2004), monkeys (Gaffan, 1994; Gaffan and
Harrison, 1989) and humans (Grafman et al., 1985; Moudgil et al., 2000). Supporting
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this conclusion, forniceal electrical stimulation results in improved recollection without
affecting familiarity-based recognition, as well as enhancing spatial memory (Hamani et
al., 2008; Hao et al., 2015; Hescham et al., 2013; Sweet et al., 2014).
In the present study we used optogenetics to specifically target septo-
hippocampal projections at the level of the fornix instead of broadly stimulating afferent
and efferent hippocampal connections with current. The medial septum sends its input
to the hippocampus through the fornix, and is known to contribute prominently to
hippocampal theta rhythm generation. It has been shown that septal lesions result in a
decrease of hippocampal theta power as well as memory deficits (Winson, 1978),
whereas fornix stimulation at theta frequency reverses the observed impairments
(McNaughton et al., 2006; Shirvalkar et al., 2010). In the first part of this chapter, we
demonstrated that stimulating the fornix at 5 Hz theta frequency increased peak power
in the hippocampus at this frequency, with no other effect in power in the theta, gamma,
or ripple bands. We hypothesize this effect is mainly driven by stimulation of GABAergic
projections from the medial septum, as it has been shown that activating septal PV
projections at the level of the hippocampus also drives the network at a specific
frequency, while power remains unchanged (Bender et al., 2015). In contrast,
stimulating glutamatergic projections from the medial septum at the level of the fornix
has no effect on theta frequency or peak power in the hippocampus, and it appears their
effect on theta is mainly though local connections in the medial septum (Robinson et al.,
2016). While our virus construction used the promoter synapsin to transfect all neural
types (Schoch et al., 1996), ChAT septal neurons appeared to be minimally colocalized
with eYFP in our experiment. Thus, not only is the presence of synapsin unnecessary
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for cholinergic neurotransmitter release (Bogen et al., 2009) but also, at least in the
medial septum, synapsin is specifically absent in the majority of cholinergic neurons.
Consequently, any of our resulting effects induced by forniceal stimulation were likely
independent from direct stimulation of cholinergic projections.
Finally, in chapter 2, we had observed attenuation of theta -low gamma coupling
in J20+ during wake periods. Thus, we wanted to ascertain if fornix activation had any
effect on the modulation index. Although direct 5 Hz stimulation of principal neurons in
the hippocampus has been shown to increase theta-gamma coupling under anesthesia
(Butler et al., 2018), we did not see an equivalent theta- low gamma coupling increase
during wakefulness. Interestingly, while continuous stimulation of medial septum
neurons at gamma frequencies has been shown to increase theta-gamma coupling
during active behavior, this result is a direct consequence of increasing gamma band
power across all phases of theta, instead of enhancing coupling at a specific phase
(Etter et al., 2019). In fact, it has been proposed that local gamma- generating circuits
might not be activated at a particular theta phase, as commonly interpreted, but rather
gamma itself might be setting the phase of the local theta oscillation (Fernández-Ruiz et
al., 2017; Lopez-Madrona et al., 2018). It remains to be established if specific phase
coupling could be enhanced during freely moving behaviour without an accompanying
increase in gamma power, and if this manipulation would be sufficient to improve
memory in models of AD.
3.4.2 Stimulation of septo-hippocampal projections decreases SWDs in a state-
dependent manner
Our results show that fornix stimulation effectively reduce SWDs specifically
during REM sleep in our AD model, and this effect was accompanied by an
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improvement in memory performance. Recent studies have revealed that hyperactivity
in the form of hippocampal spike-wave discharges in AD models can be decreased with
anti-epileptic drugs, and this decrease is accompanied by an improvement in memory
performance (Gureviciene et al., 2019; Nygaard et al., 2015; Sanchez et al., 2012b; Shi
et al., 2013; Ziyatdinova et al., 2011). One such drug, levetiracetam, improves cognitive
function in human patients diagnosed with amnestic mild cognitive impairment (Bakker
et al., 2015, 2012) and AD (Cumbo and Ligori, 2010). Other drugs not employed for
epilepsy, such as the retinoid X receptor antagonist bexarotene, also exert a striking
improvement in memory in AD models that is accompanied by a reduction of
hyperactivity (Bomben et al., 2014). Similar to our stimulation effect, in these studies
once administration of the drug is stopped, hyperactivity returns to baseline levels.
In the past few years, optogenetic stimulation has been successfully used to
reduce or halt seizures in rodent models of epilepsy (Tønnesen and Kokaia,
2017). Generally, these effects have mainly been obtained by inhibiting excitatory
principal cells or by stimulating inhibitory interneurons (Krook-Magnuson et al., 2013;
Ledri et al., 2014), but the picture is likely more complex (Bui et al., 2018). In our unit
recording experiments, we showed that optogenetic stimulation of the fornix decreased
the firing probability at SWD onset, indicating that the stimulation can disrupt anomalous
synchrony by reducing the engagement of neurons just before the peak of the spike.
Although the exact mechanism is unknown, this disengagement is presumably due to
entrainment of the network at lower frequencies, which consequently disrupts higher
frequency synchronization. Indeed, others have shown that pacing the firing of either
principal cells or interneurons at low frequencies can lower ictal discharges. (Ladas et
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al., 2015; Shiri et al., 2017). Chronically pacing the activity of hippocampal interneurons
at theta frequency for 14 days also diminishes interictal spikes for the duration of the
stimulation in a model of epilepsy (Lévesque et al., 2019). From our results, we cannot
conclude that the reduction we observe in SWDs and firing probability is a consequence
of pacing the network at a particular frequency, and further experiments will be needed
to test if this effect is dependent on stimulation rate. While manipulations employing 5
Hz theta frequency stimulation effectively abate epileptiform activity and can improve
memory in epilepsy models (Kim et al., 2020; Rajdev et al., 2011; Salam et al., 2015;
Schiller and Bankirer, 2007) and patients (Koubeissi et al., 2013; Miller et al., 2015),
higher frequencies may also suppress seizures in certain conditions (Boëx et al., 2007;
Lothman and Williamson, 1993; Wyckhuys et al., 2010).
3.4.3 Post-training stimulation improves spatial memory in AD
Lastly, we demonstrated that septo-hippocampal fiber stimulation at the level of
the fornix during the post-training period restored memory performance to control levels
in the J20+. We verified this result in two hippocampal-dependent memory tasks, the
novel object-place recognition and passive avoidance tests, with similar outcomes. Our
post-training manipulation suggests that J20+ mice are able to appropriately learn the
tasks, and it is during consolidation that the memory trace is impaired. In line with our
results, it has been shown that specific re-activation of dentate gyrus engram cells,
which had been labeled during learning, improved memory in APP/PS1 mice when
activated during retrieval (Perusini et al., 2017). Moreover, Perusini et al’s co-labeling
results imply that the cells activated during context re-exposure were not the same as
those activated during memory encoding, which the authors’ interpret as a problem in
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the retrieval process. Another explanation, which our results seem to point to, is that the
encoded trace is not appropriately consolidated and, therefore, cannot be retrieved.
Memory consolidation refers to the stabilization of memory traces, possibly
including intrahippocampal synaptic reinforcement and the transfer of information
initially encoded in the hippocampal system to the neocortex for long-term storage
(Marshall and Born, 2007; Rasch and Born, 2013; Sirota et al., 2003). The consolidation
process has been proposed to occur during post-learning rest or sleep (Prince et al.,
2014; Rasch and Born, 2013; Wilson and McNaughton, 1994) via reactivation of
memory traces in short bouts of neuronal activity associated with ripple events in quiet
awake and NREM sleep (Buzsáki, 2015; Girardeau et al., 2009; Nádasdy et al., 1999)
and stabilization of these reactivated sequences during REM (Li et al., 2017; Rasch and
Born, 2013). Recently, synaptic pruning during REM was suggested to be necessary for
removing spurious neuronal connectivity and to increase the signal-to-noise ratio in
neuronal networks (Li et al., 2017). Directly linking REM to memory, inhibiting the
medial septal GABAergic population for 4h post-training during REM sleep severely
inhibits memory performance (Boyce et al., 2016). In the context of aberrant
hippocampal hyperactivity, memory consolidation deficits have been linked to abnormal
hippocampal-cortical coupling triggered by interictal epileptiform discharges (Gelinas et
al., 2016). Thus, SWDs have the potential of hijacking both the local and connected
circuits by facilitating the expression of cortical oscillations in brain states that do not
naturally exhibit these oscillations, such as during REM, resulting in suboptimal
stabilization of the memory trace (Beenhakker and Huguenard, 2009; Busche and
Konnerth, 2016; Palop and Mucke, 2016).
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Finally, while we did not investigate the effects of optogenetic stimulation on
soluble amyloid beta levels, its reduction may have contributed to the memory
improvement after stimulation (Kittelberger et al., 2012; Lesné et al., 2006; Nicole et al.,
2016), even if plaques are not observed at this time point in our model. While
stimulation of medial septum PV neurons did not have an effect in amyloid deposition
(Etter et al., 2019), direct stimulation of hippocampal PV-expressing neurons during 1h
was able to reduce cleavage of APP in APP/PS1 mice (Iaccarino et al., 2016). On the
other hand, Yamamoto and colleagues found the opposite effect, with optogenetic
manipulation resulting in an increase of amyloid beta, although the strength of the
neuronal activation might have been more intense compared to physiological
conditions, since their stimulation paradigm also resulted in seizures (Yamamoto et al.,
2015).
In conclusion, the present optogenetics study demonstrates that 1) activation of
mainly GABAergic fibers from the medial septum can drive hippocampal rhythms at a
specific theta frequency without changing overall theta power or theta-gamma coupling.
2) Fornix stimulation decreased hyperactivity in an AD mice model during REM sleep,
accompanied by an improvement in memory performance in two hippocampal-
dependent spatial tasks. Taken together these results suggest that lowering existing
hyperactivity during sleep may benefit memory processes in AD.
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3.5 Supplementary Data
Table 3.2: REM length -SWDs
SIDAK'S MULTIPLE COMPARISONS TEST
MEAN DIFF.
95.00% CI OF DIFF.
SIGNIFICANT?
SUMMARY
ADJUSTED P VALUE
BASELINE
<30 VS. >30 & <60 -0.01597 -0.04064 to 0.008694
No ns 0.2556
<30 VS. >60 -0.05223 -0.08814 to -0.01631
Yes ** 0.0058
>30 & <60 VS. >60 -0.03625 -0.07556 to 0.003055
No ns 0.0726
STIM
<30 VS. >30 & <60 -0.01171 -0.02884 to 0.005429
No ns 0.2193
<30 VS. >60 -0.03307 -0.06128 to -0.004856
Yes * 0.0218
>30 & <60 VS. >60 -0.02136 -0.04584 to 0.003118
No ns 0.0920
POST
<30 VS. >30 & <60 -0.01442 -0.02873 to -0.0001050
Yes * 0.0483
<30 VS. >60 -0.02904 -0.05548 to -0.002612
Yes * 0.0311
>30 & <60 VS. >60 -0.01463 -0.03914 to 0.009881
No ns 0.3153
Table 3.3: REM segment -SWDs
SIDAK'S MULTIPLE COMPARISONS TEST
MEAN DIFF.
95.00% CI OF DIFF.
SIGNIFICANT?
SUMMARY
ADJUSTED P VALUE
FIRST 10S
BASELINE VS. STIM 0.02692 0.003500 to 0.05033
Yes * 0.0243
BASELINE VS. POST 0.01901 -0.004621 to 0.04265
No ns 0.1268
STIM VS. POST -0.00790
4
-0.01931 to 0.003500
No ns 0.2100
CENTER SEGMENT
BASELINE VS. STIM 0.02992 -0.004043 to 0.06388
No ns 0.0885
BASELINE VS. POST 0.02842 -0.01026 to 0.06711
No ns 0.1744
STIM VS. POST -0.00149
5
-0.02300 to 0.02001
No ns 0.9964
LAST 10 S
BASELINE VS. STIM 0.05001 0.01246 to 0.08756
Yes * 0.0103
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BASELINE VS. POST 0.02171 -0.03725 to 0.08067
No ns 0.6815
STIM VS. POST -0.02830 -0.05906 to 0.002450
No ns 0.0733
BASELINE
FIRST 10S VS. CENTER SEGMENT
-0.04107 -0.06962 to -0.01252
Yes ** 0.0063
FIRST 10S VS. LAST 10 S -0.06708 -0.1111 to -0.02310
Yes ** 0.0042
CENTER SEGMENT VS. LAST 10 S
-0.02601 -0.05654 to 0.004526
No ns 0.1016
STIM
FIRST 10S VS. CENTER SEGMENT
-0.03807 -0.06560 to -0.01053
Yes ** 0.0081
FIRST 10S VS. LAST 10 S -0.04398 -0.08175 to -0.006206
Yes * 0.0227
CENTER SEGMENT VS. LAST 10 S
-0.00591
5
-0.02497 to 0.01313
No ns 0.7789
POST
FIRST 10S VS. CENTER SEGMENT
-0.03166 -0.06170 to -0.001609
Yes * 0.0386
FIRST 10S VS. LAST 10 S -0.06438 -0.1219 to -0.006845
Yes * 0.0282
CENTER SEGMENT VS. LAST 10 S
-0.03272 -0.06367 to -0.001776
Yes * 0.0379
Table 3.4: NREM length -SWDs
SIDAK'S MULTIPLE COMPARISONS TEST
MEAN DIFF.
95.00% CI OF DIFF.
SIGNIFICANT?
SUMMARY
ADJUSTED P VALUE
BASELINE
FIRST 10S VS. CENTER SEGMENT
-0.0099
01
-0.01909 to -0.0007169
Yes * 0.0343
FIRST 10S VS. LAST 10 S
-0.0103
0
-0.01763 to -0.002971
Yes ** 0.0073
CENTER SEGMENT VS. LAST 10 S
-0.0004
018
-0.009654 to 0.008850
No ns 0.9991
STIM
FIRST 10S VS. CENTER SEGMENT
-0.0109
7
-0.01653 to -0.005413
Yes *** 0.0006
FIRST 10S VS. LAST 10 S
-0.0059
83
-0.01092 to -0.001047
Yes * 0.0180
119
CENTER SEGMENT VS. LAST 10 S
0.004990
-0.001688 to 0.01167
No ns 0.1649
POST
FIRST 10S VS. CENTER SEGMENT
-0.0059
78
-0.01157 to -0.0003837
Yes * 0.0359
FIRST 10S VS. LAST 10 S
-0.0033
89
-0.01039 to 0.003612
No ns 0.4809
CENTER SEGMENT VS. LAST 10 S
0.002589
-0.002670 to 0.007847
No ns 0.4674
Table 3.5: NREM segment -SWDs
SIDAK'S MULTIPLE COMPARISONS TEST
MEAN DIFF.
95.00% CI OF DIFF.
SIGNIFICANT?
SUMMAR
Y
ADJUSTED P VALUE
BASELINE
<30 VS. >30 & <60 0.008204
-0.008090 to 0.02450
No ns 0.4495
<30 VS. >60 0.01755
0.0005777 to 0.03452
Yes * 0.0424
>30 & <60 VS. >60 0.009346
-0.001128 to 0.01982
No ns 0.0838
STIM
<30 VS. >30 & <60 0.001628
-0.004548 to 0.007803
No ns 0.8497
<30 VS. >60 0.005918
-0.003620 to 0.01546
No ns 0.2863
>30 & <60 VS. >60 0.004290
-0.001217 to 0.009798
No ns 0.1426
POST
<30 VS. >30 & <60 0.006534
-0.008318 to 0.02139
No ns 0.5555
<30 VS. >60 0.01175
-0.006033 to 0.02954
No ns 0.2416
>30 & <60 VS. >60 0.005217
-0.002119 to 0.01255
No ns 0.1939
Table 3.6: Control behavioural measures
Corresponding to Fig 3.8E
SOURCE OF VARIATION
% OF TOTAL VARIATION
P VALU
E
P VALUE SUMMARY
SIGNIFICANT?
GEISSER-GREENHOUSE'S
EPSILON
120
INTERACTION 20.82 0.0065
** Yes
TIME 0.9949 0.7152
ns No 0.6159
COLUMN FACTOR
0.1808 0.3312
ns No
SUBJECT 1.874 >0.9999
ns No
SIDAK'S MULTIPLE COMPARISONS TEST
MEAN DIFF.
95.00% CI OF DIFF.
SIGNIFICANT?
SUMMARY
ADJUSTED P VALUE
J20+ (N=9)
Q1 VS. Q2 38.54 -118.7 to 195.7
No ns 0.9992
Q1 VS. Q3 -7.889 -238.6 to 222.8
No ns >0.9999
Q1 VS. Q4 -48.33 -193.4 to 96.70
No ns 0.9801
Q2 VS. Q3 -46.43 -157.7 to 64.86
No ns 0.8892
Q2 VS. Q4 -86.87 -188.0 to 14.24
No ns 0.1120
Q3 VS. Q4 -40.44 -166.3 to 85.38
No ns 0.9857
J20- (N=9)
Q1 VS. Q2 -16.17 -71.61 to 39.28
No ns 0.9946
Q1 VS. Q3 -33.11 -145.8 to 79.62
No ns 0.9942
Q1 VS. Q4 -21.15 -89.76 to 47.46
No ns 0.9905
Q2 VS. Q3 -16.94 -117.2 to 83.33
No ns >0.9999
Q2 VS. Q4 -4.982 -80.03 to 70.06
No ns >0.9999
Q3 VS. Q4 11.96 -56.21 to 80.13
No ns >0.9999
J20+ YFP (N=9)
Q1 VS. Q2 -4.146 -70.42 to 62.13
No ns >0.9999
Q1 VS. Q3 39.88 -23.60 to 103.4
No ns 0.4081
Q1 VS. Q4 45.92 -26.44 to 118.3
No ns 0.3949
Q2 VS. Q3 44.02 -1.284 to 89.33
No ns 0.0587
Q2 VS. Q4 50.06 -16.52 to 116.6
No ns 0.2080
Q3 VS. Q4 6.042 -46.33 to 58.42
No ns >0.9999
Table 3 7: Control behavioural measures
Corresponding to Fig 3.8F
121
SOURCE OF VARIATION
% OF TOTAL VARIATION
P VALU
E
P VALUE SUMMARY
SIGNIFICANT?
GEISSER-GREENHOUSE'S
EPSILON
INTERACTION 2.708 0.3230
ns No
TIME 8.693 0.0014
** Yes 0.9819
COLUMN FACTOR
20.27 0.0083
** Yes
SUBJECT 41.25 0.0005
*** Yes
SIDAK'S MULTIPLE COMPARISONS TEST
MEAN DIFF.
95.00% CI OF DIFF.
SIGNIFICANT?
SUMMARY
ADJUSTED P VALUE
HABITUATION
J20- (N=9) VS. J20+ (N=9) 126.0 -13102 to 13354
No ns >0.9999
J20- (N=9) VS. J20+ YFP (N=9)
-8993 -19433 to 1447
No ns 0.1140
SAMPLE
J20- (N=9) VS. J20+ (N=9) -4332 -17121 to 8456
No ns 0.9045
J20- (N=9) VS. J20+ YFP (N=9)
-11407 -25040 to 2227
No ns 0.1318
TEST
J20- (N=9) VS. J20+ (N=9) 4495 -4164 to 13155
No ns 0.5885
J20- (N=9) VS. J20+ YFP (N=9)
-5581 -13696 to 2534
No ns 0.2793
Table 3 8: Control behavioural measures
Corresponding to Fig 3.8G
SOURCE OF VARIATION
% OF TOTAL VARIATION
P VALUE
P VALUE SUMMARY
SIGNIFICANT?
INTERACTION 2.540 0.2409 ns No TIME 3.515 0.0520 ns No COLUMN FACTOR 13.40 0.0902 ns No SUBJECT 60.37 0.0047 ** Yes
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Chapter 4: Fornix deep brain theta-burst stimulation improves
memory performance in an Alzheimer’s disease mouse model
4.1. Introduction
In the first part of this thesis we described early electrophysiological
abnormalities in the hippocampus of an Alzheimer’s mouse model and linked these
abnormalities to spatial behavioral deficits. In this section, we investigate, using
intracranial depth electrodes (deep brain stimulation, DBS), the effect of focal electrical
stimulation of the brain on spatial memory. Recently, significant interest has grown to
use this technique to modulate the hippocampal circuit and potentially reduce negative
symptoms in AD patients.
Evidence from drug-resistant epileptic populations and in one case of morbid
obesity first gave rise to the hypothesis that stimulation of hippocampal-associated
areas may improve memory (Fell et al., 2013; Hamani et al., 2008; Koubeissi et al.,
2013; Miller et al., 2015; Oh et al., 2012; Suthana et al., 2012). Indeed, small clinical
trials in AD patients have shown that DBS of the fornix (fDBS) may potentially provide
some improvement in symptoms. In an initial Phase 1 clinical trial, six patients who were
treated with fDBS for a year showed improvement in a subset of symptoms. These
patients showed a possible slowing of cognitive decline and increased glucose
metabolism in the temporal lobe as measured with PET, together with slower rates of
hippocampal atrophy (Laxton et al., 2010; Sankar et al., 2015; Smith et al., 2012).
Similar results were found in a case study by Fontaine et al. (2013). In a subsequent
phase 2 clinical trial, a positive cognitive change was observed in patients who had
received fDBS treatment and were older than 65, while a worse cognitive score was
123
seen in younger individuals when DBS was on versus off (Holroyd et al., 2015; Lozano
et al., 2016; Ponce et al., 2015).
These studies have in common the use of high-frequency DBS, which has been
most widely employed in the treatment of Parkinson’s disease, often yielding significant
reductions in symptoms (Benabid et al., 1998; Krack et al., 1997; Limousin et al., 1995).
However, the limited clinical effect of high-frequency stimulation in these AD trials partly
draws attention to the suitability of using stimulation protocols developed to alleviate
motor symptoms in Parkinson’s disease (Benabid et al., 1998; Groiss et al., 2009) which
may not be necessarily optimized to mitigate memory dysfunction in AD.
An alternative stimulation paradigm, theta-burst stimulation, has been shown to
have an effect on memory in rodent and epileptic patients (Miller et al., 2015; Shirvalkar
et al., 2010; Sweet et al., 2014; Titiz et al., 2017). Theta-burst stimulation resembles
physiological patterns of activity as it consists of temporally spaced patterns of
stimulation bursts repeated in a theta (4-10 Hz) frequency interval, and is able to
efficiently elicit long-term potentiation (LTP, (Albensi et al., 2007; Capocchi et al., 1992;
Larson et al., 1986; Nguyen and Kandel, 1997; Staubli and Lynch, 1987). The effects of
high-frequency and theta-burst stimulation of the fornix on memory have yet to be
assessed in AD mice models. Thus, in this chapter, we used high-frequency or theta-
burst stimulation in J20+ mice during the post-training period of two hippocampal-
dependent memory tasks, the passive avoidance and novel object place recognition
tests. We report that continuous theta-burst stimulation right after the training session of
the passive avoidance or novel object place recognition tasks, can significantly rescue
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memory performance, whereas under our experimental conditions high-frequency
stimulation does not.
4.2. Methods
Most of the methods used in this chapter have been described in the previous
chapter. Refer to section 2.2. and 3.2. for the details.
4.2.1. Surgical coordinates
Mice were implanted in fimbria-fornix with bipolar tungsten electrodes (AP, -0.5;
ML, 0; DV, -2.35).
4.2.3 Post-training stimulation paradigm
After the training session in each of the memory tasks, animals were returned to
their homecage and received continuous fornix theta-burst stimulation (fTBS :100μs
85µA pulses in trains of 50ms at 200 Hz, 5 times per second) or 130 Hz stimulation
(fHFS: 100μs 85µA pulses at 130 Hz) for the duration of the post-training period. These
DBS parameters have been used previously (Gallino et al., 2019; Hescham et al., 2017;
Miller et al., 2015; Sweet et al., 2014; Xia et al., 2017b). A Master-9 (AMPI) pulse
generator and stimulus isolators (WPI) were used. The sham groups were tethered but
received no stimulation. The mice were monitored for the first 2-4 hours to determine
possible adverse reactions to the stimulation (i.e. excessive rearing, grooming).
4.2.4. Passive avoidance procedure.
The standard passive avoidance conditioning protocol was used as described in
the previous chapters with the addition of a ten-minute test 5min after the training to
assess the short-term memory of all groups. Post-training stimulation started
125
immediately after the short-term memory test and lasted until the 24h time mark, when
animals were tested immediately without a shock. The latency to cross to the dark
compartment was measured.
4.2.5. Novel object place recognition procedure (NOPR) procedure.
The experimental protocol was performed as previously described. After
exploring the objects in the sample phase, animals received 4 hours of fTBS, 130Hz, or
sham stimulation and were tested immediately after with a displaced object (test phase).
The recognition index (RI) of the displaced object was computed for statistical analysis:
Object 2 recognition index, RI=(Object 2 exploration (s)−Object 1 exploration (s)) /
(Object 2 exploration (s)+Object 1 exploration (s)).
4.3. Results
4.3.1. Passive avoidance memory is improved after fornix theta-burst stimulation in
J20+ mice.
To investigate the effect of fDBS in the passive avoidance test, we compared the
latency to cross to the dark compartment between controls and J20+ groups which had
received either sham (Control Sham, J20 Sham), fornix theta-burst stimulation (Control
fTBS, J20 fTBS), or fornix high frequency stimulation (fHFS: J20 130Hz) treatment
(Fig.4-1 A-B). The latency was measured during training and 5min, 1 day, and 1 week
after training (Fig.4-1 C). A mixed ANOVA of the aligned rank transformed latencies
revealed a significant interaction of GROUPxTIME (F(12,84)= 13.106, p<0.001). Holm-
adjusted interaction analysis using Chi2 test highlighted significant differences between
the J20 and Control Sham latencies in the 24h testing timepoint compared to the
training session, indicating the J20+ had poorer performance than controls 24h after
126
training (Control Sham vs. J20 Sham- train vs 24h: p<0.001). Remarkably, the J20
130Hz stimulation group’s performance was similar to the J20 Sham and no rescue was
observed (Control Sham vs. J20 130Hz- train vs 24h: p<0.001). In contrast, the J20
fTBS group’s avoidance response improved at the 24h timepoint as the latency was not
significantly different from controls (Controls Sham vs. J20 fTBS- train vs 24h: p=0.226)
(Fig.4-1 D).
To investigate if the J20 130Hz group’s performance could be rescued, these
same animals were re-tested in a second experiment using an identical passive
avoidance protocol. Mice were randomly assigned to two different groups: one receiving
fHFS again (J20 130Hz re-tested, internal control) and the other fTBS (J20 fTBS re-
tested) (Fig.4-1 E). A mixed ANOVA of the aligned rank transformed latencies showed
a significant interaction of GROUPxTIME (F(6,45)=11.935, p<0.001). Furthermore, chi2
analysis showed significant differences between the J20 fTBS re-tested group and the
J20 130Hz and J20 130Hz re-tested groups, in the training vs 24h timepoints (J20
130Hz vs. J20 fTBS retested - train vs 24h: p<0.001) (Fig.4-1 F). See supplemental
materials for all statistical comparisons.
4.3.2. Novel object place recognition memory improves after fTBS in J20+ mice.
To further validate the effect of fHFS and fTBS on memory, a second
hippocampal-dependent task was used (Fig.4-1 G). A mixed-ANOVA revealed a
statistically significant interaction of GROUPxSESSION (F(4,39)=5.883, P=0.0089).
Multiple comparisons using Sidak's test highlighted significant differences only in the
test phase between the Control Sham and J20 Sham (p=0.0055), and Control Sham
and J20 130Hz groups, (p=0.0047), suggesting that the J20 Sham and J20 130Hz
127
group showed impaired memory performance as there was no preference for the
displaced object during the test phase. However, performance in the J20 fTBS group
reached control levels (Fig.4-1 H).
In a second experiment, we aimed to investigate if the performance of the J20
130Hz group could be rescued with fTBS. The same J20 130Hz animals were re-tested
with different objects in different positions, this time receiving fTBS instead (Fig.4-1 I). A
mixed ANOVA revealed a statistically significant difference between groups
(F(1,9)=6.381, P=0.0324). Sidak's multiple comparisons test showed significant
differences between the J20 fTBS re-tested and J20 130Hz groups in the test phase
(sample: p=0.5071, test: p=0.0454), suggesting that fTBS but not fHFS, was able to
improve memory on the same animals (Fig.4-1.J). Controls measures revealed no
evidence for differences in locomotion or motivation during testing between groups (Fig.
4-2).
128
129
Fig. 4.1: Memory improvements following fTBS in J20 mice. A. Electrode placement
schematic. Right: Photomicrographs illustrating fornix DBS electrode (scalebar:
500microns). B. Schematic of stimulation paradigms. C. Schematic of passive
avoidance apparatus and schedule. D. Latency to cross to dark compartment in the
passive avoidance task. Control Sham n=5, Control fTBS n=5, J20 Sham n=7, J20 fTBS
n=6, J20 130Hz n=10; *p<0.001 (statistical significance shown only compared to Control
Sham). E. Schematic of repeated design passive avoidance for the J20 130Hz group. F.
Latency to cross to dark compartment in the repeated design passive avoidance task.
J20 fTBS re-tested n=4, J20 130Hz re-tested n=4; *p<0.001 (statistical significance
shown only compared to J20 130Hz). G. Schematic of NOPR task and stimulation
delivery. Below, cumulative exploration time for both objects. H. RI of the displaced
object. I: Schematic of re-tested NOPR task design for J20 130Hz group. Below,
cumulative exploration time for both objects. J, RI of the displaced object. Control Sham
n=8, Control Treatment n=8, J20 Sham n=9, J20 Treatment n=9; J20 130Hz n=10; J20
fTBS re-tested n=6; *p<0.05.
All sample and control measures such as total object exploration time, distance
travelled, time spent in the center of the open field, distance travelled during habituation
and time spent in quadrants during habituation, showed no significant differences
between groups.
130
4.4. Discussion
In the clinical trials completed to date in AD patients, the efficacy of high-
frequency fornix DBS on memory performance has been variable (Holroyd et al., 2015;
Lozano et al., 2016; Ponce et al., 2015). Recent evidence from rodent studies and
epileptic patients suggests that an alternative frequency stimulation in the form of
patterned theta-bursts may modulate memory more efficaciously. For example, after
using intra-medial septum infusions of muscimol to attenuate theta rhythm and impair
spatial memory in Long-Evans rats, Shirvalkar et al. (2010) stimulated the fimbria-
fornix using three stimulation protocols: theta (7.7 Hz), 100 Hz, and theta-burst
stimulation. The latter was the only stimulation protocol that improved spatial memory
performance. Similar results were found by Sweet et al. (2014) in a rodent model of
traumatic brain injury where theta-burst was compared to tonic low and high-
frequency fornix stimulation. Additionally, two studies with epileptic patients in which
this patterned theta burst stimulation was delivered to the fornix (Miller et al., 2015)
and entorhinal cortex (Titiz et al., 2017), indicates that the paradigm might also be
capable of improving memory in humans. The authors hypothesize that theta-burst
stimulation compared to single pulse high-frequency stimulation may enhance
Fig. 4.2: Novel object place recognition (NOPR) control measures.
131
memory performance by better replicating natural physiological activity in the
hippocampus.
The present results show that fornix theta-burst stimulation, when delivered for
the duration of the post-training period, can reverse memory impairments in two
hippocampal-dependent tasks in a transgenic AD mouse model. However, under our
experimental conditions, 130 Hz high-frequency stimulation was unable to modulate
memory. The timing of stimulation delivery may be significant, since other studies have
observed enhanced memory performance with high-frequency stimulation when
delivered weeks before training (Gallino et al., 2019; Hao et al., 2015; Hescham et al.,
2017; Mann et al., 2017; Xia et al., 2017b; Zhang et al., 2015). On the other hand when
theta frequencies have been used (Hescham et al., 2013; Miller et al., 2015; Shirvalkar
et al., 2010; Sweet et al., 2014; Titiz et al., 2017) or electrodes were implanted in other
memory areas (Kádár et al., 2014; Redolar-Ripoll et al., 2002; Soriano-Mas et al., 2005;
Suthana et al., 2012; Titiz et al., 2017), stimulation was delivered during or just
before/after the task. Therefore, we cannot conclude a complete lack of effect in
memory from high-frequency stimulation, since if our timing of stimulation were different,
we might have been able to see an effect in memory performance, albeit reduced as
compared with theta-burst (Hescham et al., 2013; Shirvalkar et al., 2010; Sweet et al.,
2014).
4.4.1. Possible therapeutic mechanisms of DBS in memory circuitry
The exact mechanisms by which DBS modulates the memory circuit are not
clearly understood, and several hypotheses have been put forward.
132
First, DBS studies that focus on memory performance improvements weeks after
the stimulation posit a strong involvement of neurogenesis in the process. Although not
as clear in adult humans (Snyder, 2019; Sorrells et al., 2018), neurogenesis
continuously occurs throughout life in the subventricular zone and granule cell layer of
the dentate gyrus. Moreover, adult-generated dentate gyrus cells are thought to
contribute to the formation of hippocampus-dependent memories (Deng et al., 2010). In
line with this hypothesis, DBS of the entorhinal cortex results in memory improvement
and is correlated with activity-dependent regulation of hippocampal neurogenesis in AD
(Mann et al., 2017) and unimpaired (Stone et al., 2011) mice. A similar result has been
described in a model of Rett syndrome when DBS was delivered to the fornix (Hao et
al., 2015). However another study on the effects of fDBS found no evidence for induced
neurogenesis based on numbers of BrdU/NeuN double-labeled cells in the dentate,
while memory performance still improved (Hescham et al., 2017), suggesting that
neurogenesis is not necessary for DBS to modulate memory.
Second, acute changes in circuit activity appear to be sufficient for memory
modulation. Indeed, the immediate effects we found in memory performance are in
agreement with many studies showing that changes in behavior can occur immediately
after the onset of stimulation (Deeb et al., 2019; Hamani et al., 2008; Kádár et al., 2014;
Redolar-Ripoll et al., 2002; Shirvalkar et al., 2010; Soriano-Mas et al., 2005; Sweet et
al., 2014). In accordance with these results, fDBS for 1h induced expression of c-Fos,
an immediate‐early marker of neural activation, in areas CA1 and CA3 of the
hippocampus (Gondard et al., 2015; Hescham et al., 2016). Further, fDBS can acutely
enhance acetylcholine levels in the hippocampus after 20 minutes of stimulation
133
(Hescham et al., 2016) as well as increase dopaminergic and glutamatergic
transmission throughout the nucleus accumbens (Ross et al., 2016).
Moreover, fDBS has been found to rapidly enhance levels of the synaptic
markers synaptophysin and GAP-43 in the hippocampus (Gondard et al., 2015). These
molecules play a key role in axonal growth and guidance in addition to synaptic
plasticity and synaptogenesis, and are important for memory processing (Aigner et al.,
1995; Biewenga et al., 1996; Grasselli et al., 2011; Rekart et al., 2005; Um et al., 2012).
In the Gongard et al. study, BDNF was also significantly increased after fDBS. BDNF
plays an important role in neuronal differentiation, neuron survival, synapse formation,
and regulation of activity-dependent changes in synapse structure and function
(Acheson et al., 1995; Park and Poo, 2013). BDNF is also a regulator of LTP in the
hippocampus (Bramham and Messaoudi, 2005; Minichiello, 2009) and plays a crucial
role in learning and memory (Callaghan and Kelly, 2012; Furini et al., 2010; Heldt et al.,
2007). Indeed, LTP is enhanced in the hippocampus after fDBS (Hao et al., 2015).
Lastly, DBS influences amyloid beta load. Although amyloid plaques may not be
the best predictor of cognitive impairment, and their clearance does not appear to result
in cognitive improvement, as discussed elsewhere in this thesis, AD largely remains
defined by their presence (Dodart et al., 2002; Hsia et al., 1999; Jacobsen et al., 2006;
Morris et al., 2014; Mucke et al., 2000; Panza et al., 2016). In young AD transgenic
mice, entorhinal DBS is reported to reduce Aβ plaque load in the dorsal hippocampus,
prefrontal cortex, and amygdala (Xia et al., 2017b). However, in the same study, DBS
did not reduce Aβ plaque load in older AD mice, despite successful rescue of their
memory deficits. Entorhinal DBS has also been shown to induce a significant reduction
134
in CA1 amyloid beta-42 levels along with decreased hippocampal tau (Mann et al.,
2017). fDBS was similarly able to decrease amyloidosis, as well as modulate
inflammatory response and taper neuronal loss (Leplus et al., 2019).
In conclusion, the studies mentioned here show that DBS can modulate the
hippocampal memory circuit through immediate and longer-term effects through a
myriad of mechanisms. We present evidence that theta-burst fDBS delivered post-
training is able to improve hippocampal-dependent memory in an AD mice model.
Similar to others, we found that the more physiological theta-burst pattern of stimulation
performed better than high-frequency stimulation (Hescham et al., 2013; Miller et al.,
2015; Shirvalkar et al., 2010; Sweet et al., 2014; Titiz et al., 2017). Further research will
be needed to elucidate the underlying processes by which theta-burst stimulation led to
memory improvement in our experiments.
135
4.5. Supplementary Information
Contains supplementary tables 4.1, 4.2 and 4.3
Table 4.1: Passive Avoidance additional statistics.
Corresponds to data shown in Fig.4-1 D
Chisq Test:
P-value adjustment method: holm
Value Df Chisq Pr(>Chisq)
CS-JS : train-5m -41.714 1 3.9658 1.0000000
CS-CD : train-5m -30.400 1 1.8053 1.0000000
CS-JD : train-5m -48.167 1 4.9442 0.7329960
CS-J130 : train-5m -17.600 1 0.8068 1.0000000
JS-CD : train-5m 11.314 1 0.2918 1.0000000
JS-JD : train-5m -6.452 1 0.1051 1.0000000
JS-J130 : train-5m 24.114 1 1.8710 1.0000000
CD-JD : train-5m -17.767 1 0.6727 1.0000000
CD-J130 : train-5m 12.800 1 0.4267 1.0000000
JD-J130 : train-5m 30.567 1 2.7378 1.0000000
CS-JS : train-24h -127.714 1 37.1740 5.834e-08 ***
CS-CD : train-24h -43.000 1 3.6120 1.0000000
CS-JD : train-24h -58.333 1 7.2516 0.2266766
CS-J130 : train-24h -146.200 1 55.6733 5.134e-12 ***
JS-CD : train-24h 84.714 1 16.3559 0.0021522 **
JS-JD : train-24h 69.381 1 12.1524 0.0186299 *
JS-J130 : train-24h -18.486 1 1.0995 1.0000000
CD-JD : train-24h -15.333 1 0.5010 1.0000000
CD-J130 : train-24h -103.200 1 27.7404 6.937e-06 ***
JD-J130 : train-24h -87.867 1 22.6232 9.263e-05 ***
CS-JS : train-1w -63.343 1 9.1444 0.0848227 .
CS-CD : train-1w 69.400 1 9.4088 0.0783896 .
CS-JD : train-1w -50.200 1 5.3704 0.6144245
CS-J130 : train-1w -60.100 1 9.4081 0.0783896 .
JS-CD : train-1w 132.743 1 40.1590 1.288e-08 ***
JS-JD : train-1w 13.143 1 0.4361 1.0000000
JS-J130 : train-1w 3.243 1 0.0338 1.0000000
CD-JD : train-1w -119.600 1 30.4835 1.717e-06 ***
CD-J130 : train-1w -129.500 1 43.6810 2.242e-09 ***
JD-J130 : train-1w -9.900 1 0.2872 1.0000000
CS-JS : 5m-24h -86.000 1 16.8561 0.0016936 **
CS-CD : 5m-24h -12.600 1 0.3101 1.0000000
CS-JD : 5m-24h -10.167 1 0.2203 1.0000000
CS-J130 : 5m-24h -128.600 1 43.0759 3.001e-09 ***
JS-CD : 5m-24h 73.400 1 12.2787 0.0178680 *
JS-JD : 5m-24h 75.833 1 14.5178 0.0055537 **
JS-J130 : 5m-24h -42.600 1 5.8390 0.4859031
CD-JD : 5m-24h 2.433 1 0.0126 1.0000000
136
CD-J130 : 5m-24h -116.000 1 35.0484 1.705e-07 ***
JD-J130 : 5m-24h -118.433 1 41.1011 8.095e-09 ***
CS-JS : 5m-1w -21.629 1 1.0661 1.0000000
CS-CD : 5m-1w 99.800 1 19.4570 0.0004630 ***
CS-JD : 5m-1w -2.033 1 0.0088 1.0000000
CS-J130 : 5m-1w -42.500 1 4.7047 0.7820939
JS-CD : 5m-1w 121.429 1 33.6049 3.511e-07 ***
JS-JD : 5m-1w 19.595 1 0.9694 1.0000000
JS-J130 : 5m-1w -20.871 1 1.4016 1.0000000
CD-JD : 5m-1w -101.833 1 22.0995 0.0001191 ***
CD-J130 : 5m-1w -142.300 1 52.7427 2.243e-11 ***
JD-J130 : 5m-1w -40.467 1 4.7984 0.7691095
CS-JS : 24h-1w 64.371 1 9.4438 0.0783896 .
CS-CD : 24h-1w 112.400 1 24.6801 3.316e-05 ***
CS-JD : 24h-1w 8.133 1 0.1410 1.0000000
CS-J130 : 24h-1w 86.100 1 19.3090 0.0004892 ***
JS-CD : 24h-1w 48.029 1 5.2573 0.6338043
JS-JD : 24h-1w -56.238 1 7.9844 0.1557031
JS-J130 : 24h-1w 21.729 1 1.5191 1.0000000
CD-JD : 24h-1w -104.267 1 23.1683 7.125e-05 ***
CD-J130 : 24h-1w -26.300 1 1.8016 1.0000000
JD-J130 : 24h-1w 77.967 1 17.8124 0.0010483 **
Table 4. 2: Corresponds to data shown in Fig.4-1 F.
Chisq Test:
P-value adjustment method: holm
Value Df Chisq Pr(>Chisq)
J130-J130r : train-5m 0.40 1 0.0011 1.0000000
J130-JDr : train-5m -4.35 1 0.1319 1.0000000
J130r-JDr : train-5m -4.75 1 0.1101 1.0000000
J130-J130r : train-24h -1.15 1 0.0092 1.0000000
J130-JDr : train-24h 73.35 1 37.5172 1.540e-08 ***
J130r-JDr : train-24h 74.50 1 27.0920 2.910e-06 ***
J130-J130r : train-1w -7.25 1 0.3665 1.0000000
J130-JDr : train-1w 54.25 1 20.5225 7.072e-05 ***
J130r-JDr : train-1w 61.50 1 18.4620 0.0001907 ***
J130-J130r : 5m-24h -1.55 1 0.0168 1.0000000
J130-JDr : 5m-24h 77.70 1 42.0990 1.562e-09 ***
J130r-JDr : 5m-24h 79.25 1 30.6568 4.927e-07 ***
J130-J130r : 5m-1w -7.65 1 0.4081 1.0000000
J130-JDr : 5m-1w 58.60 1 23.9456 1.387e-05 ***
J130r-JDr : 5m-1w 66.25 1 21.4240 4.786e-05 ***
J130-J130r : 24h-1w -6.10 1 0.2595 1.0000000
J130-JDr : 24h-1w -19.10 1 2.5439 1.0000000
J130r-JDr : 24h-1w -13.00 1 0.8249 1.0000000
137
Table 4. 3: Novel object place recognition (NOPR) additional statistics.
Corresponds to data shown in Fig. 4-1 H
Sidak's multiple comparisons test Predicted (LS) mean diff.
95.00% CI of diff. Significant? Adjusted P Value
Sample
Control sham vs. Control fTBS 0.03389 -0.1710 to 0.2388 No 0.9888
Control sham vs. J20 Sham 0.004908 -0.1942 to 0.2041 No >0.9999
Control sham vs. J20 fTBS 0.01503 -0.1841 to 0.2142 No 0.9995
Control sham vs. J20 130hz 0.01862 -0.1758 to 0.2130 No 0.9986
Test
Control sham vs. Control fTBS -0.09338 -0.2983 to 0.1115 No 0.6816
Control sham vs. J20 Sham 0.2591 0.05999 to 0.4583 Yes 0.0055
Control sham vs. J20 fTBS -0.01191 -0.2110 to 0.1872 No 0.9998
Control sham vs. J20 130hz 0.2570 0.06256 to 0.4514 Yes 0.0047
138
Chapter 5: Concluding remarks
Alzheimer’s disease (AD) is characterized by progressive cognitive deterioration,
which leads to a severely reduced quality of life. Currently, no viable therapies are able
to prevent, delay or reverse AD progression (Alzheimer’s Association, 2017). As such,
non-drug alternatives are currently being tested, including deep brain stimulation (DBS).
The rationale for using DBS in AD is that, in addition to AD being a neurodegenerative
disease, it is also a neural circuit disorder affecting several integrated cortical and
subcortical pathways, especially those involved in memory and cognition (Mirzadeh et
al., 2016). Additionally, stimulation of memory-associated areas has the potential of
improving cognitive function, which has incentivized clinical trials on DBS for AD (Chen
et al., 2014; Deeb et al., 2019; Dürschmid et al., 2017; Hamani et al., 2008; Hardenacke
et al., 2013). However, there is still no comprehensive theory explaining the effects of
DBS on AD symptoms or a consensus on which paradigms provide optimal benefits
(Benabid et al., 2002). The focus of this thesis has been to gain further insight into the
potential effects of fornix stimulation in the context of progressive memory impairment.
The work detailed here first examined early alterations in the hippocampal activity of the
J20+ AD mouse model, both during wake and sleep. Next, we explored the modulatory
effect of septo-hippocampal projection stimulation on deficient hippocampal oscillations
and memory, and finally used the more clinically relevant deep brain stimulation to
assess the effect of two distinct stimulation paradigms.
In chapter 2, we first characterized hippocampal rhythms in the J20+ in a state-
dependent manner. As discussed in previous chapters, alterations in the LFP of plaque-
bearing mice had been detected mostly in the form of decreased low gamma amplitude
139
and theta- low gamma coupling during active wake (Etter et al., 2019; Mably et al.,
2017), while deficits pre-plaque seem to be more variable and inconsistent, showing no
(J20+, (Rubio et al., 2012)), slight (5xFAD (Siwek et al., 2015)) or overt (APP23 (Ittner
et al., 2014)) changes in band power. Independent from any alterations in gamma
amplitude, we found a deficit in theta- low gamma coupling during wake, indicating that
phase amplitude-coupling may be one of the first detectable alterations in AD, in line
with results from in vitro studies (Goutagny et al., 2013; Mondragón-Rodríguez et al.,
2018). Coherent oscillations are believed to take part in network communication by
allowing opportunity windows for the exchange of information (Fries, 2005; Varela et al.,
2001). The decrease in coupling specific to the low gamma band, which is thought to
mainly reflect CA3-CA1 synchrony (Colgin, 2015a, 2015b), suggest a deficit in the
precisely timed communication between these two hippocampal areas, which is strongly
associated with consolidation and retrieval processes (Buzsáki, 2015; Daumas et al.,
2005; Montgomery and Buzsáki, 2007). The peak frequency of theta was also found to
be reduced in J20+, both during wake and REM sleep, potentially contributing to this
impairment in information flow (Buzsáki, 2002). Adding to the network abnormalities,
aberrant hypersynchronous activity was highly prevalent in J20+ during sleep,
especially during periods of REM. In fact, the incidence of spike-wave discharges
appeared to be particularly modulated by REM episode duration and segment, possibly
alluding to the contribution of homeostatic processes in their rate of generation. Finally,
the frequency of SWDs in REM inversely correlated with memory performance in two
hippocampal-dependent tasks, suggesting that consolidation processes such as
140
synaptic downscaling may be disrupted by these bursts of atypical hyperactivity
(Avanzini et al., 2013; Tononi and Cirelli, 2014).
In chapters 3 and 4, we demonstrated that hippocampal modulation through
fornix stimulation is able to enhance memory performance in two hippocampal tasks in
which J20+ showed early impairments. As discussed in section 4.4, the mechanisms
and brain areas recruited by deep brain stimulation, and that result in memory
enhancement or rescue are many and diverse. Through the use of optogenetics,
specific projections can be selectively stimulated and the direct effects on hippocampal
activity and memory, explored. Our results agree with other reports showing that
hippocampal theta peak power and frequency can be modulated, without altering overall
band power, by optogenetic stimulation of septo-hippocampal projections (Bender et al.,
2015; Robinson et al., 2016). Of importance for AD, this stimulation reduces the
probability of firing of single units before the peak of spike-wave discharges, as well as
the overall number of hyperactive events during REM sleep. Employing anti-epileptic
drugs also results in a similar reduction in the number of SWDs concurrent with an
improvement in memory performance (Bakker et al., 2012; Cumbo and Ligori, 2010;
Gureviciene et al., 2019; Nygaard et al., 2015; Sanchez et al., 2012b; Shi et al., 2013),
which we also observe in J20+ mice. In line with optogenetic manipulations in epileptic
models (Ladas et al., 2015; Lévesque et al., 2019; Shiri et al., 2017), our findings
suggest that artificially pacing the hippocampal network at lower frequencies may
override the occurrence of hypersynchronous events during periods important for
memory consolidation (i.e. REM sleep), thus enhancing memory performance.
141
How exactly these hyperactive events contribute to the disruption of memory
consolidation in AD is currently unknown and further investigation is needed. For
example, imaging different subpopulations of neurons in the hippocampus using
calcium indicators may show if specific interneurons or subsets of principal cells
abnormally increase their firing before SWD onset. This subpopulation could then be
characterized to elucidate if early changes in their activity or connectivity are specific to
AD. Optogenetic manipulations that target specific subpopulations could also be
employed to investigate how these neurons contribute to SWD rate and memory. For
instance, by experimentally manipulating the rate of SWD (Shatskikh et al., 2006) and
distinctly increasing and decreasing their incidence in REM and NREM sleep, we could
causally probe their role in memory. In regard to stimulation effects, whether the
decrease in hippocampal hyperactivity is due to pacing the network at any frequency or
instead due to entraining the network within a specific frequency range is unknown
(Chiang et al., 2014; Lévesque et al., 2019; Shiri et al., 2017). While manipulations
employing electrical stimulation at 5 Hz theta frequency effectively abate epileptiform
activity and are able to improve memory in epilepsy models (Kim et al., 2020; Rajdev et
al., 2011; Salam et al., 2015; Schiller and Bankirer, 2007) and patients (Koubeissi et al.,
2013; Miller et al., 2015), higher frequencies may also suppress seizures in certain
conditions (Boëx et al., 2007; Lothman and Williamson, 1993; Wyckhuys et al., 2010).
Why this is the case is not clearly understood (Albensi et al., 2007; Theodore and
Fisher, 2004), as these studies employ different models, stimulate different areas and
use different measures to assess excitatory-inhibitory imbalances.
142
In chapter 4, we compared the effect of high and low-frequency stimulation in
memory, by delivering electrical current to the fornix. We demonstrate that, when the
stimulation is delivered for the duration of the post-training period, low theta-burst
stimulation enhances memory performance, whereas high-frequency stimulation had no
effect. Our results differ from other studies using high frequencies, which have been
shown to improve memory, albeit at a lower level than by employing theta and theta-
burst pulses (Hescham et al., 2013; Miller et al., 2015; Shirvalkar et al., 2010; Sweet et
al., 2014; Titiz et al., 2017). One reason that may explain the difference between these
experiments is the period of stimulation. Whereas we stimulated after training, these
studies comparing low and high-frequency paradigms targeted encoding periods just
before the task and/or during the task. Therefore, we may have observed a comparable
effect of high-frequency stimulation on memory if we had focused our protocol to the
encoding phase, although some have reported that high-frequency stimulation during
the task can disrupt memory (Goyal et al., 2018; Hamani et al., 2010; Jacobs et al.,
2016; Natu et al., 2019; Talakoub et al., 2016).
One outstanding question that remains in the findings presented here is the
physiological mechanism by which these stimulation protocols differentially affect
memory. As discussed in section 4.4.1, electrical stimulation of the fornix engages many
areas and processes and disentangling which ones are essential for memory
modulation will inform future treatments. Our optogenetic experiments indicate that
stimulating septal projections is sufficient to enhance impaired memory and therefore,
concurrently modulating several other memory structures may not be necessary.
Probing this pathway using optogenetics at higher frequencies and different time points,
143
for example, while recording from the hippocampus, may offer further insight on the
mechanisms underlying specific stimulation protocols. We provide evidence indicating
that increased memory performance could be linked to decreased hippocampal
hyperactivity during REM, suggesting that anti-epileptic drugs and therapeutics
specifically targeting sleep have the potential to improve quality of life in AD patients
(Cumbo and Ligori, 2010) and should be explored further. Along the same line,
stimulation delivered during specific behavioral states has the potential of being
successful, given our results connecting memory impairments to events in sleep. DBS
experiments targeting a specific phase of sleep, such as REM, would be needed to
answer if stimulation during a particular stage is sufficient to restore memory
performance. Likewise, manipulations where, for example, AD transgenic rodents
receive stimulation before, during or in between active periods of a task, with at least
two different stimulation frequencies, would be ideal to understand if the modulatory
effect on cognition is time and/or frequency-dependent.
Lastly, we also found incipient impairments in specific hippocampal rhythms, that
have been linked to memory; these may further deteriorate and become more relevant
as the disease progresses. For future studies, it will also be important to assess how
restoring deficits in hippocampal theta and gamma may affect memory. Closed-loop
approaches, where the network is regulated only when needed and/or multi-modal
approaches, where several of these deficits are targeted simultaneously, will have to be
assessed as they may be necessary in later stages of the disease when networks are
severely dysregulated.
144
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