neurophysiology and behavior: spike trains and fields...final thoughts •bridging the gap between...
TRANSCRIPT
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Neurophysiology and Behavior: Spike Trains and Fields
David Moorman
Psychological and Brain Sciences
Neuroscience and Behavior Graduate Program
University of Massachusetts Amherst
CCNS: Challenges in Functional Connectivity Modeling and Analysis 2016
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How to Read Character: A New
Illustrated Hand-Book of Phrenology
and Physiognomy, for Students and
Examiners; with a Descriptive Chart.
(New York, Fowler & Wells Co.,
Pubs., 1891)
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Neural circuitry presents a complex view of the brain
MGH Human connectome project acquisition team,
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Sanes, Lichtman, et al., Brainbow/Brainstorm Consortium
Cellular neural circuitry is even more complex
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Singh 2012
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Presentation Outline
• Brief background
• Biological basis of neural signals and how data are collected
• Different types of neural signals• Synaptic potentials/currents (briefly)
• Spikes/action potentials
• Local Field Potentials (LFP)
• EEG (briefly)
• Association of neural signals with behavior
• Relationship to BOLD signal
• Analysis of neural signals
• Future directions and challenges going forward
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Some caveats
• Pace of presentation (slow)
• My research focus (minimized)
• My areas of expertise (and lack thereof)
• Happy to look into anything that I can’t address here
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The problem
Information (sensory,
etc.)
Neural processing
Information transformation
Cognition
Action, behavior
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Which level of analysis?
Rosie Cowell
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Gazzaniga 2009
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General THM
• Information is conveyed through neuronal activity• Contributions of non-neuronal cells (glia, etc.)?
• Neuron ensemble activity encodes information• Within brain areas• Across brain areas
• Neural code is complex• Spikes?• Fields?
• Neural data sets can be enormous and heterogeneous
• Neurons/ensembles themselves are highly heterogeneous• Periodic “check-ins” with biology
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fMRI
Gazzaniga 2009
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“…it’s like looking down at the US from a satellite seeing the grid of lights at night. You can infer certain things: Here’s a city, here’s a city. But to really understand the interactions between those cities you need to get down to the level of individual people moving around in cars. It’s a matter of scale and resolution.” -- Bill Newsome, Wired, 2013
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“It makes no sense to read a newspaper with a microscope.” -- Valentino Braitenburg (quoted in Logothetis 2008)
Switfyscience.blogspot.com
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Lent et al., 2012
• Approximately 1000 Trillion synapses
• Approximately 10^5 “switches” per synapse (channels, receptors, transporters)
• So approximately 10^20 “switches” per brain
Stephen Smith
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How many neurons per voxel?
https://cfn.upenn.edu/aguirre/wiki/public:neurons_in_a_voxel
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Lent et al., 2012
• Approximately 1000 Trillion synapses
• Approximately 10^5 “switches” per synapse (channels, receptors, transporters)
• So approximately 10^20 “switches” per brain
Stephen Smith
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Each medium spiny neuron receives input from several thousand excitatory cortical neurons
Striatal medium spiny neuron
Lynn Raymond, UBC
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Logothetis 2008
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Logothetis 2008
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Presentation Outline
• Brief background
• Biological basis of neural signals and how data are collected
• Different types of neural signals• Synaptic potentials/currents (briefly)
• Spikes/action potentials
• Local Field Potentials (LFP)
• EEG (briefly)
• Association of neural signals with behavior
• Relationship to BOLD signal
• Analysis of neural signals
• Future directions and challenges going forward
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theremino.com
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http://www.mtchs.org/BIO/text/chapter28
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http://www.mtchs.org/BIO/text/chapter28
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http://www.mtchs.org/BIO/text/chapter28
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https://www.studyblue.com/notes/note/n/chapter-48-nervous-system/deck/4169450
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techlab.bu.edu
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http://www.interactive-biology.com/99/the-isoelectric-point-and-how-it-leads-to-an-action-potential/
Action potential (spikes) are the output from cell bodies/axons
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http://www.qbi.uq.edu.au/brain-facts/neuroscience-basics-action-potentials-and-synapses
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www.ruhr-uni-bochum.de
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syntheticneurobiology.org
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Extracellular electrophysiological recording
Moorman and Aston-Jones, 2010
Dendrite (input)
Axon (output)
Soma (cell body)
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newton.umsl.edu
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Rolston et al., 2009
Raw electrical signals are filtered into different types of neural activity
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http://lifesciences.ieee.org/publications/newsletter/april-2012 - M. Mollazadeh
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Einevoll et al., 2012
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Example of four neurons recorded from one electrode wire in rat prefrontal
cortex
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Courtesy of Lex Kravitz
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Recording the activity of more than one neuron or field location
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Chapter 1, State-of-the-Art Microwire Array Design for Chronic Neural Recordings in Behaving Animals
Methods for Neural Ensemble Recordings. 2nd edition.
Nicolelis MAL, editor.
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Electrodes are implanted in the brain and connected to amplifiers and
filters
Rolston et al., 2009
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Wireless recording of multiple neurons
Szutz et al., 2011
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From: Chapter 5, Chronic Recordings in Transgenic Mice
Methods for Neural Ensemble Recordings. 2nd edition.
Nicolelis MAL, editor.
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Stuart Layton, Wikipedia
Tetrodes are used to precisely isolate multiple neurons
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Voigts et al., 2013
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Einevoll et al., 2013
Local field potentials are a summation of synaptic
input (dendrites) and neuronal population activity
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Increases in LFP power at specific frequencies underlies different behavioral/cognitive functions
Wang 2010
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Hopkinsmedicine.org
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backyardbrains.org
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backyardbrains.org
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Adjamian 2014
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Presentation Outline
• Brief background
• Biological basis of neural signals and how data are collected
• Different types of neural signals• Synaptic potentials/currents (briefly)
• Spikes/action potentials
• Local Field Potentials (LFP)
• EEG (briefly)
• Association of neural signals with behavior
• Relationship to BOLD signal
• Analysis of neural signals
• Future directions and challenges going forward
![Page 54: Neurophysiology and Behavior: Spike Trains and Fields...Final thoughts •Bridging the gap between biology/behavior-based neuroscience and computational neuroscience •Train next](https://reader033.vdocuments.net/reader033/viewer/2022042921/5f6be503a692a751194f8780/html5/thumbnails/54.jpg)
How does fMRI relate to electrical activity?
Logothetis et al., 2001
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Using optogenetics to dissect BOLD signalsuggests spiking activity may contribute
Leopold – comment on Lee et al., 2010
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Optogenetics as a tool to study neuronal function
http://neurobyn.blogspot.se/2011/01/controlling-brain-with-lasers.html Deisseroth Lab
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Excitatory and inhibitory light-sensitive proteins
Deisseroth Lab
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Optogenetic excitation and inhibition of neurons
Jones et al., 2015
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Optogenetic control of behavior
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Liang et al., 2015
Optogenetic circuit mapping in awake and
anesthetized animals
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Presentation Outline
• Brief background
• Biological basis of neural signals and how data are collected
• Different types of neural signals• Synaptic potentials/currents (briefly)
• Spikes/action potentials
• Local Field Potentials (LFP)
• EEG (briefly)
• Association of neural signals with behavior
• Relationship to BOLD signal
• Analysis of neural signals
• Future directions and challenges going forward
![Page 62: Neurophysiology and Behavior: Spike Trains and Fields...Final thoughts •Bridging the gap between biology/behavior-based neuroscience and computational neuroscience •Train next](https://reader033.vdocuments.net/reader033/viewer/2022042921/5f6be503a692a751194f8780/html5/thumbnails/62.jpg)
Data analysis methods - spikes
• Action potentials = spikes
• Patterns of spikes = spike trains
• Spike trains are point processes• Though can be smoothed
• LFPs are continuous
• Most basic forms of analysis:• Peristimulus time histogram: PSTH
• Population averages
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backyardbrains.org
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Baranauskas 2015
Rate codes and temporal codes
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Relationship of spikes to behavior: perievent histograms and rasters
Hernandez and Moorman, in prep
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Multi-neuron population histograms
• Average together all the neurons that you recorded from to characterize what the brain area “does”
• This is unappealing for a number of reasons• Heterogeneity
Moorman and Aston-Jones, 2014
Moorman and Aston-Jones, 2015
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Even when we characterize populations of single neurons, we look for trends
Moorman and Aston-Jones, 2014
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Characterizing increased/decreased LFP power at specific frequencies during behavior
Ito et al., 2014
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Issues with averaging/combining neural signals
• Neurons in a brain area don’t all do the same thing
• The same neuron may do multiple things
• Neuronal activity varies from trial to trial
• Neurons work together in populations
• Ensemble encoding may provide more information
Harris and Mrsic-Flogel 2013
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What do we want to know?
• How do neurons encode information?• Reliably?• Flexibly?
• How do neurons interact with one another?
• How do ensembles of neurons interact with one another?
• Can we place a causal role on the relationships among neurons/ensembles?
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More sophisticated ways of characterizing neural interactions
• Correlation• Cross correlogram, JPSTH
• Pattern analysis• E.g., triplets, more complex
patterns (e.g., synfirechains)
• Frequency analysis
• Principal/independent components analysis
Brown et al., 2004
Cross correlation
JPSTH
Maximum
likelihood
models
Cross coherence
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Similarly LFPs can be analyzed for spatial and temporal correlation
Alain Destexhe and Claude Bedard (2013), Scholarpedia, 8(8):10713.
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Population decoding
• Population vectors
• Reverse correlation
• Bayesian decoding
• Pattern classifier
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Population vectors in the motor cortex
A. Georgopoulos
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Population vector decoding of reward value in orbitofrontal cortex
Van Duuren et al., 2008
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Pattern classifier decoding of prefrontal cortex information
Meyers et al., 2012
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Higher level analyses over distance and time
• Granger causality
• Graph theory/rich club analysis
• Dynamic correlation
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Coherence and Granger causality to show cross-structure relationships
Sirota et al., 2016
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Evidence of rich club networks in neuronal activity
Nigam et al., 2016
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Semiparametric models to dynamically characterize correlated activity across multiple
neurons
Shahbaba et al., 2014, Zhou et al., 2015
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Scaling analyses to cope with the frontier of big data
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Brain Activity Map
AKA BRAIN Initiative
Record from EVERY neuron in the brain
Neuron 2012
Science 2013
ACS Nano 2013
Neuroscience + Nanoscience
*all 86 billion of them
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Can we record from every neuron?
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Very large scale integration (VLSI) electrophysiology
Blanche et al., 2004Alivisatos et al., 2012
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1000+ recording sites
LeafLabs/Boyden
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Cellular optical imaging(2-photon calcium or voltage imaging)
http://biology.ucsd.edu/faculty/komiyama.html
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Cellular optical imaging
Alivisatos et al., 2012
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• Top: Conformational change
produced by Ca2+ binding.
• Middle: Diagram illustrating imaging
setup.
• Bottom: Blue lines plot movement of
an individual mouse in square and
circular arenas. Red dots mark the
animal’s position during Ca2+ events in
a specific CA1 neuron. Lower pairs of
figures show Gaussian-smoothed data.
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Imaging “all” neurons from a zebrafish larva
Ahrens and Keller 2013
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Nanomachines
• “Smart” wireless nanoscale transmitters
• Multiferroic antennas
• Ultrasmall nanoelectronicchips
• Nanoparticle labeling and reporting
Seo et al., 2013
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Neurophysiological signals are used to control sensory and motor neural prosthetics in humans
http://www.stanford.edu/~shenoy/GroupResearchOverview.htm
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Clinical applications of motor neural coding
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Data analysis?
• Large Hadron Collider • ~10 petabytes/year
• One recording study of the type proposed • ~ 1 gigabyte/sec
• 4 terabytes/hour,
• 100 terabytes/day
• Compressed, this equals ~3 petabytes/year
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Future Challenges
• Scale• understanding interactions among large numbers of
neurons simultaneously
• Coding• finding meaning in patterns of activity in single neurons
and ensembles of neurons
• note also modulatory signaling
• Plasticity• neuron function changes over time
• Heterogeneity• many types of neurons even within one brain area
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Final thoughts
• Bridging the gap between biology/behavior-based neuroscience and computational neuroscience
• Train next generation of multidimensional quantitative neuroscientists
• However, there is a need for translation between statistical models and experimental data sets – what do the results “mean”?• In the context of biology
• This is going to get even more complicated as data sets get larger and larger