Altered PDE-10A expression detectable early before symptomatic onset in

Huntington’s disease

Flavia Niccolini,1,2 Salman Haider,3 Tiago Reis Marques,4 Nils Muhlert,5 Andri C.

Tziortzi,6 Graham E. Searle,6 Sridhar Natesan,4 Paola Piccini,2 Shitij Kapur,4 Eugenii

A. Rabiner,6,7 Roger N. Gunn,2,6 Sarah J. Tabrizi,3 Marios Politis1,2

1Neurodegeneration Imaging Group, Department of Clinical Neuroscience, Institute

of Psychiatry, Psychology and Neuroscience, King’s College London, London,

United Kingdom

2Division of Brain Sciences, Department of Medicine, Imperial College London,

London, United Kingdom

3Huntington's Disease Research Group, Department of Neurodegenerative Disease,

Institute of Neurology, University College London, London, United Kingdom

4Department of Psychosis Studies, Institute of Psychiatry, Psychology and

Neuroscience, King’s College London, London, United Kingdom

5School of Psychology, Cardiff University, Cardiff, United Kingdom

6Imanova Ltd., Centre for Imaging Sciences, Hammersmith Hospital, London, United

Kingdom

7Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience,

King’s College London, London, United Kingdom.

Correspondence to: Marios Politis, Department of Clinical & Basic Neuroscience

P043 Institute of Psychiatry, Camberwell, London SE5 8AF, United Kingdom. E-

mail: marios.politis@kcl.ac.uk.

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Running title: PDE-10A in premanifest Huntington’s disease

ABSTRACT

There is an urgent need of early biomarkers and novel disease-modifying therapies in

Huntington’s disease. Huntington’s disease pathology involves the toxic effect of

mutant huntingtin primarily in striatal medium spiny neurons, which highly express

phosphodiesterase 10A (PDE-10A). PDE-10A hydrolyses cAMP/cGMP signaling

cascades, thus having a key role in the regulation of striatal output, and in promoting

neuronal survival. PDE-10A could be a key therapeutic target in Huntington’s

disease. Here, we used combined PET and multimodal MR imaging to assess PDE-

10A expression in vivo in a unique cohort of 12 early premanifest Huntington’s

disease gene carriers with a mean estimated 90% probability of 25-years before the

predicted onset of clinical symptoms. We show bidirectional changes in PDE-10A

expression in premanifest Huntington’s disease gene carriers, which are associated

with the probability of symptomatic onset. PDE-10A expression in early premanifest

Huntington’s disease was decreased in striatum and pallidum and increased in motor-

thalamic-nuclei, compared to a group of matched healthy controls. Connectivity-

based analysis revealed prominent PDE-10A decreases confined in the sensorimotor-

striatum and in striatonigral and striatopallidal projecting segments. The ratio between

higher PDE-10A expression in motor-thalamic-nuclei and lower PDE-10A expression

in striatopallidal projecting striatum was the strongest correlate with higher

probability of symptomatic conversion in early premanifest Huntington’s disease gene

carriers. Our findings demonstrate in vivo a novel and earliest pathophysiological

mechanism underlying Huntington’s disease with direct implications for the

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development of new pharmacological treatments, which can promote neuronal

survival and improve outcome in Huntington’s disease gene carriers.

Keywords

Premanifest Huntington’s disease gene carriers; PDE-10A; PET; MRI

Abbreviations

cAMP= cyclic adenosine monophosphate; cGMP= cyclic guanosine monophosphate;

DARPP-32= Dopamine- and cAMP-regulated phosphoprotein; MSNs= medum spiny

neurons; PDE-10A= phosphodiesterase 10A; UHDRS= Unified Huntington’s Disease

Rating Scale.

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INTRODUCTION

Huntington’s disease is a monogenic, progressive and fatal neurodegenerative

disorder affecting motor, cognitive and neuropsychiatric functions (Walker, 2007).

Currently, there is no cure or disease modifying therapy for Huntington’s disease,

while symptomatic treatment is very limited. There is an urgent need for new

therapies and robust pharmacodynamics measures for the development of novel

therapeutic interventions and for allowing the monitoring of disease progression at the

time that matters the most: before the development of overt symptoms (premanifest

stage).

Phosphodiesterase 10A (PDE-10A) is a dual substrate enzyme highly expressed in the

striatal medium spiny neurons (MSNs) (Fujishige et al., 1999; Coskran et al., 2006).

Preclinical research in transgenic Huntington’s disease animal models suggests a

direct effect of mutant huntingtin on PDE-10A expression via the alteration of

transcription, synthesis and trafficking (Hu et al., 2004; Leuti et al., 2013). At a

molecular striatal level, PDE-10A regulates cAMP and cGMP downstream signaling

cascades (e.g. cAMP/PKA/DARPP-32) that control the phosphorylation state and

activity of several physiological effectors including gene transcription factors such as

CREB, and various neurotransmitter receptors and voltage-gated ion channels (Nishi

et al., 2008; Girault, 2012). Thus, toxic huntingtin effects on PDE-10A could be

detrimental for neuronal survival and for the regulation of basal ganglia functions

through the dopamine-D1 direct and dopamine-D2 indirect pathways (Hebb et al.,

2004; Giampà et al., 2009, 2010).

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PDE-10A could be a molecular target of critical therapeutic interest in Huntington’s

disease with possible application in other basal ganglia disorders (Siuciak et al., 2006;

Giampà et al., 2009, 2010; Leuti et al., 2013; Piccart et al., 2013). Previous work

explored PDE-10A expression in transgenic Huntington’s disease mice, showing

decreased PDE-10A protein and mRNA levels in the striatum (Hebb et al., 2004;

Leuti et al., 2013), and increased PDE-10A levels in the perikarya of striatal MSNs

(Leuti et al., 2013). In humans, decreased PDE-10A levels were found in post-mortem

striatal tissue (Hebb et al., 2004). Recent PET studies found reductions in the striatal

PDE-10A binding in manifest Huntington’s disease patients with significant striatal

atrophy (Ahmad et al., 2014; Russell et al., 2014) and premanifest Huntington’s

disease gene carriers who were a mean of 12 years from disease onset (Russell et al.,

2014). Experimental studies have suggested that PDE-10A inhibition could be

beneficial in Huntington’s disease (Giampà et al., 2009, 2010; Leuti et al., 2013).

Although previous data suggest an important role of PDE-10A enzyme in the

pathophysiology of Huntington’s disease, it is unclear how the alteration of PDE-10A

expression is related to the neuropathological salient networks, and whether the

changes in PDE-10A are functionally significant.

Here, we hypothesized that altered PDE-10A expression could be one of the earliest

changes in Huntington’s disease due to its immediate link with primary Huntington’s

disease pathology. If this is true PDE-10A could be of crucial importance in

mechanisms modulating motor, cognitive and neuropsychiatric functions. We

combined state-of-the-art PET molecular imaging and multimodal MR-based

structural imaging in vivo to study a unique cohort of early premanifest Huntington’s

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disease gene carriers. Our investigations led to a discovery of the earliest reported

neurochemical abnormality in Huntington’s disease, linking altered PDE-10A

signaling with predicted risk of imminent symptomatic conversion and with potential

implications for the development of new treatments.

MATERIALS AND METHODS

Participants

We identified and studied 12 early premanifest Huntington’s disease gene carriers,

who were the furthest from the predicted disease onset (90% probability to symptoms

onset=25±6.9 years, mean±SD; range: 17-43 years; Table 1), from the Huntington’s

disease gene carrier registry database of National Hospital of Neurology &

Neurosurgery, Queen Square, London. To estimate time to symptoms onset we used a

validated variant of the survival analysis formula described by Langbehn (2004). This

formula can be transformed into a probability distribution for age of diagnosis and

subsequently years from symptomatic onset that depends on both the subject’s CAG

expansion length and current age (Paulsen et al., 2008). To estimate time from

symptomatic onset, the probability distribution was truncated to account for the fact

that a subject has reached their current age without yet receiving a clinical diagnosis.

Then, the mean of this revised distribution was calculated (Paulsen et al., 2008).

Details of this revised formula and original conditional probability of symptomatic

onset derived from the survival analysis formula of Langbehn (2004) are provided in

Supplementary Materials (Supplementary Table 1). All early premanifest

Huntington’s disease gene carriers were asymptomatic based on the standardized total

motor score subscale (TMS=0) of the Unified Huntington Disease Rating Scale

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(UHDRS) with a diagnostic confidence level of 0 (The Huntington Study Group,

1996). Twelve healthy individuals, matched for age and gender, who served as the

control group, were recruited by public advertisement. All participants screened

successfully to undertake PET and MRI scanning under standard criteria, had no

history of other neurological or psychiatric disorders, and were not under treatment

with substances with known actions in PDEs (Supplementary Table 2 and 3). The

study was approved by the institutional review boards and the research ethics

committee. Written informed consent was obtained from all study participants.

Clinical assessments

Motor function was assessed with the UHDRS TMS (The Huntington Study Group,

1996). Functional capacity was assessed with clinician-based [(Total Functional

Capacity scale (TFC), Independence Scale (IS), UHDRS functional assessment)] (The

Huntington Study Group, 1996) and participant self-reported [(36-Item Short Form

Health Survey (SF-36)] (Ware et al., 1993) functional and quality-of-life measures

and assessments. Neuropsychiatric symptoms were assessed with the shortened form

of the problem behaviour assessment (PBA) (Craufurd et al., 2001), the Beck

Depression Inventory-II (BDI-II) (Beck et al., 1993), and the Hamilton Depression

Rating Scale (HDRS) (Hamilton, 1960). Cognitive assessments were carried out using

the Cambridge Neuropsychological Test Automated Battery (CANTAB®) and

included assessments related to episodic memory (Paired Associate Learning), visual

memory (Pattern Recognition Memory), attention (Reaction Time), executive

function (One Touch Stockings of Cambridge) and language processing (Graded

Naming Test).

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Imaging assessments

PET and MR imaging was performed at Imanova Ltd, London, UK. All participants

were scanned on Siemens Biograph Hi-Rez 6 PET-CT scanner (Erlangen, Germany)

following the injection of an intravenous bolus mean dose of 258 MBq [11C]IMA107

(SD: ± 56.5) [mean mass injected: 3.8 ug (SD: ± 2.2)]. All participants were scanned

after withholding consumption of caffeinate beverages for 12 hours (Fredholm et al.,

1999). Dynamic emission data were acquired continuously for 90 minutes following

the injection of [11C]IMA107. The dynamic images were reconstructed with in-house

software, into 26 frames (8 x 15 s, 3 x 60 s, 5 x 120 s, 5 x 300 s, and 5 x 600 s), using

a filtered back projection algorithm (direct inversion Fourier transform) with a 128

matrix, zoom of 2.6 producing images with isotropic voxel size of 2 x 2 x 2 mm3, and

a transaxial Gaussian filter of 5 mm.

MRI scans were acquired with a 32-channel head coil on a Siemens Magnetom Verio,

3-T MRI scanner and included a T1-weighted magnetization prepared rapid gradient

echo sequence [MPRAGE; time repetition (TR) = 2300 ms, time echo (TE) = 2.98

ms, flip angle of 9°, time to inversion (TI) = 900 ms, matrix = 240 x 256] for co-

registration with the PET images and for voxel based morphometry (VBM) analysis;

fast grey matter (GM) T1 inversion recovery (FGATIR; TR = 3000 ms, TE = 2.96 ms,

flip angle of 8°, TI = 409 ms, matrix = 240 x 256) (Sudhyadhom et al., 2009) and

fluid and white matter (WM) suppression (FLAWS; TR = 5000 ms, TE = 2.94 ms,

flip angle of 5°, TI = 409/1100 ms, matrix = 240 x 256) (Tanner et al., 2013)

sequences for improving delineation of subcortical brain regions. All sequences used

a 1 mm3 voxel size, anteroposterior phase encoding direction, and a symmetric echo.

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Diffusion-weighted data were acquired for performing a two-layered probabilistic

tractography and connectivity-based functional parcellation of the striatum using echo

planar imaging (EPI; TR = 8000 ms, TE = 96 ms, flip angle of 90° and voxel size of 2

x 2 x 2 mm3). The diffusion weighting was isotropically distributed along the 30

directions (b-value = 1000 s/mm2), and a non-DWI (B0) was acquired at the

beginning of each scan. EPI acquisitions are prone to geometric distortions that can

lead to errors in tractography. To minimize this, two image sets were acquired with

the phase-encoded direction reversed—“blip-up” and “blip-down”—resulting in

images with geometric distortions of equal magnitude but in the opposite direction

allowing for the calculation of a corrected image (Andersson et al., 2003). Before

correcting geometric distortions, each image set—blip-up and blip-down—was

corrected for motion and eddy-current-related distortions. Diffusion data analysis was

performed with the FSL tools (FMRIB Centre Software Library, Oxford University;

http://www.fmrib.ox.ac.uk/fsl/).

Data processing

Voxels-based morphometry

Images were segmented into GM, WM matter and cerebrospinal fluid tissue classes

using the statistical parametric mapping (SPM) version 8 software package

(Wellcome Department of Imaging Neuroscience, London, United Kingdom,

http://www.fil.ion.ucl.ac.uk/spm/). GM and WM images were then normalized to a

GM and WM population template, generated from the complete image set using the

diffeomorphic anatomical registration using exponentiated lie-algebra (DARTEL)

registration method (Ashburner, 2007). This nonlinear warping technique minimizes

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between-subject structural variations. All images were checked following spatial

normalization to ensure registration accuracy. The final voxel resolution was 1 x 1 x 1

mm. Spatially normalized images were modulated by the Jacobian determinants so

that intensities represent the amount of deformation needed to normalize the images,

and then smoothed with an 8-mm full-width at half-maximum Gaussian kernel.

Voxel-based multiple regression analysis (based on the general linear model) was

carried out using SPM8 with voxel-wise GM and WM volume as the dependent

variables. Age and gender were added as nuisance covariates and total intracranial

volume, calculated by summing the values of the native space tissue segmentations

using the ‘get_totals’ function in SPM8, were added as a global measure. Multiple

regression analysis was then performed to assess for changes in GM and WM

volumes between premanifest Huntington’s disease gene carriers and healthy controls.

The threshold for statistical significance was set at P<0.05 after family wise error

(FWE) correction for multiple comparisons.

Freesurfer MRI volumetric analysis

The FreeSurfer image analysis suite (version 5.3.0 http://surfer.nmr.mgh.harvard.edu)

processing pipeline was used to derive measures of subcortical volumes. The

automated procedures for volumetric measures of these different brain structures have

been previously described (Fischl et al., 2002). This procedure automatically assigns a

neuroanatomical label to each voxel in an MRI volume based on probabilistic

information automatically estimated from a manually labeled training set. In brief, the

segmentation is carried out as follows: first, an optimal linear transform is computed

that maximizes the likelihood of the input image, given an atlas constructed from

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manually labeled images. Next, a nonlinear transform is initialized with the linear

one, and the image is allowed to further deform to better match the atlas. Finally, a

Bayesian segmentation procedure is carried out, and the maximum a posteriori

estimate of the labeling is computed. The segmentation uses three pieces of

information to disambiguate labels: (1) the prior probability of a given tissue class

occurring at a specific atlas location, (2) the likelihood of the image intensity given

that tissue class, and (3) the probability of the local spatial configuration of labels

given the tissue class. This technique has previously been shown to be comparable in

accuracy to manual labeling (Fischl et al., 2002). Adjustments for intracranial volume

were calculated for each ROI using validated methods within the FreeSurfer toolkit

(Buckner et al., 2004).

[11C]IMA107 PET data

Movement correction

Subjects were positioned supine with their transaxial planes parallel to the line

intersecting the anterior-posterior commissure line. Head position was maintained

with the help of individualized foam holders, monitored by video and was

repositioned if movement was detected. Subjects were in a resting state with low

light. Intrascan notes for participant’s movement were acquired during scanning.

Minimal head movements were noted only in three out of 24 subjects (12.5%).

After reconstruction of the dynamic [11C]IMA107 image volumes, we applied regions

of interest (ROIs) to the dynamic data set. We tested correction for movement using a

frame-by-frame realignment procedure as previously described (Montgomery et al.,

2006) with in-house software (c-wave) implemented in Matlab 8.2 (The MathWorks

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Inc.). Non–attenuated corrected (non-AC) images were used for realignment, to

provide additional information by reducing the influence of redistribution of

radiotracer producing erroneous realignments (Dagher et al., 1998). The non-AC

images were denoised using a level 2, order 64 Battle Lemarie wavelet filter

(Turkheimer et al., 1999). The denoised frames were then realigned using a mutual

information algorithm (Studholme et al., 1997). Frames of the original time series

were then resliced and reassembled into a movement-corrected dynamic scan.

The decay-corrected time-activity curves (TACs) were computed and compared to

those without movement correction for all subjects including the three subjects with

minimal head movement. Amount and timing of any movement were assessed

graphically and compared with intrascan records. Visual inspection of TACs pre- and

post-correction determined that no correction for movement needed to be applied in

these datasets.

Parametric images

Parametric images of [11C]IMA107 non-displaceable binding potential (BPND) were

generated from the dynamic [11C]IMA107 scans using a basis function

implementation of the simplified reference tissue model, with the cerebellum as the

reference tissue for nonspecific binding using an in-house software (c-wave)

implemented in Matlab 8.2 (Gunn et al., 1997). Previous PET studies have shown

lower PDE-10A uptake in the cerebellum (Plisson et al., 2011, 2014; Barret et al.,

2014) and [11C]IMA107 binding in the cerebellum not changed after the

administration of PDE-10A selective blocker (Imanova internal data), confirming the

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suitability of the cerebellum as a reference region for the determination of the regional

estimation of BPND.

Anatomically defined regions-of-interest

To facilitate anatomical delineation of regions-of-interest (ROIs), PET images were

anatomically co-registered and resliced to the corresponding volumetric FLAWS and

FGATIR MR images and spatially normalized into the T1-weighted Montreal

Neurologic Institute (MNI) 152 template using the Mutual Information Registration

algorithm in SPM8 software package implemented in Matlab 8.2. ROIs were

delineated manually on the co-registered MRIs using ANALYZE version 11 (Mayo

Foundation) medical imaging software package by the assessor who was blinded to

groups allocation. We manually delineated basal ganglia structures due to the poor

performance of automated parcellation techniques on structures such as the substantia

nigra, largely due to poor contrast in these regions on structural T1-weighted MR

images. To compensate for this, we acquired state-of-the-art FGATIR and FLAWS

MRI scans for each individual, which use T1-nulling to minimise white matter signal

and, by improving contrast, increase the definition of basal ganglia structures. We

have then used a reliable, robust and repeatable technique for manual delineation of

basal ganglia structures (Tziortzi et al., 2011). ROIs included caudate, putamen,

ventral striatum, globus pallidus, substantia nigra and motor thalamic nuclei. These

brain regions express higher PDE-10A levels (Seeger et al., 2003; Coskran et al.,

2006).

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Connectivity-based parcellations of ROIs according to cortico-striatal projections

Probabilistic tractography was performed on each subjects’ diffusion data to

functionally parcellate striatum into limbic, cognitive and sensorimotor areas. The

methods applied have been described previously (FSL library:

http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases/striatumconn) (Tziortzi et al., 2014). In

brief, tractography was performed in the subjects’ continuous space, and the results

were output in the subjects’ structural space. To register the diffusion data to the T1-

weighted images the epi_reg script was employed (Jenkinson et al., 2002). FMRIB’s

diffusion toolbox (FTD, http://www.fmrib.ox.ac.uk/fsl/fdt) was used to perform

probabilistic tractography with a partial volume model allowing for up to two fiber

directions in each voxel (Behrens et al., 2007). From each striatal voxel 10 thousand

sample tracts were generated to enable estimates of the striatal connectivity profile

with each of the cortical target.

Five cortical regions, which correspond to the limbic, cognitive, rostral motor, caudal

motor and parietal lobes defined on the Montreal Neurological Institute template

(MNI) were used (Tziortzi et al., 2014). The rostral motor, caudal motor and parietal

regions were merged and constituted the sensorimotor target. Thereafter the FMRIB’s

linear image registration tool (FLIRT) and FMRIB’s nonlinear image registration tool

(FNIRT) functions (Jenkinson et al., 2002) were sequentially applied to normalize the

MNI template to each subjects’ T1-weighted image. To tailor the cortical ROIs to the

subjects’ individual anatomy, the subjects’ segmented GM was employed to mask the

ROIs. The lower threshold for the GM mask was set at 0.25. The striatum was

manually defined on the subjects’ T1-weighted image following published guidelines

(Tziortzi et al., 2011).

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The connectivity maps of each cortical target were processed in two different ways:

(a) For each subject, exclusive connectivity-based functional ROIs were created

following the procedures described by Johansen-Berg and colleagues (2005) where

each voxel is assigned to the cortical target with the highest connection probability;

and (b) each subject’s connectivity maps were thresholded at 5% of the maximum

connectivity value to minimize noise and voxels with low connectivity values (Fig.

2A). This allowed functional subdivisions to have a certain degree of overlap.

Subsequently, cortical-striatal connectivity maps were applied to [11C]IMA107 BPND

images to calculate the regional estimates of BPND.

Connectivity-based parcellations of ROIs according to striatal projections to

substantia nigra, globus pallidus internus and globus pallidus externus

Striatal parcellations according to striatonigral, striatopallidal internal and

striatopallidal external projections were carried out using the following steps: (a) the

substantia nigra, external and internal segments of the globus pallidus were defined on

each participant’s FLAWS/FGATIR image. Their FLAWS/FGATIR image was then

linearly registered to their diffusion images using FLIRT, and the transform was then

applied to the substantia nigra, globus pallidus internus and externus mask to move it

into diffusion space. (b) FDT was used to determine the connectivity of each putamen

voxel (from the striatal mask, as defined for the striatal parcellations according to

cortical-connectivity) with the substantia nigra/globus pallidus internus and globus

pallidus externus targets in diffusion space. Ten thousand sample tracts were used.

Distance correction was applied since connectivity distribution drops with distance

from the seed mask and substantia nigra is located further from the putamen than the

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globus pallidus. (d) The resulting connectivity maps of each subcortical target

(substantia nigra/globus pallidus internus and globus pallidus externus) were then

processed by creating exclusive connectivity-based functional ROIs and 5%

thresholded connectivity maps (as described for the striatal parcellations according to

cortical-connectivity). (e) The striatal-sensorimotor maps were then masked using the

exclusive connectivity-based functional ROIs and 5% thresholded connectivity maps

from ‘step d’. This generated maps of striatal voxels specifically involved in

sensorimotor networks, which projected to the substantia nigra/globus pallidus

internus or globus pallidus externus, and so reflect the major parts of the direct or

indirect pathways, respectively (Fig. 2B). Our striatonigral/striatopallidal internal and

striatopallidal external maps are consistent with findings obtained with tract-tracing

methods in primates indicating that dorsolateral striatal neurons project mainly to the

substantia nigra and dorsomedial striatal neurons form bundles that run caudally

toward the pallidal external complex (Parent and Hazrati, 1995). Moreover,

overlapping fibers with those located in the more medial bundles reflecting the

nigrostriatal tract were carefully excluded (Hedreen et al., 1991).

Statistical analysis

Statistical analysis and graphical representations of data were performed with

GraphPad Prism (version 6.0c) and SPSS (version 22) for MAC OS X. For all

variables, variance homogeneity and Gaussianity were tested with Bartlett and

Kolmogorov-Smirnov tests, and we proceeded with parametric tests since our PET

and clinical data were normally distributed. Multivariate analysis of variance

(MANOVA) was used to assess the main effects of clinical and imaging variables

between the group of early premanifest HD gene carriers and healthy controls. If the

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overall multivariate test was significant, P values for each variable were calculated

following Bonferroni’s multiple-comparisons test. We interrogated correlations

between PET and clinical data using Pearson’s r and applied the Hochberg multiple-

comparison correction using PPLot (version 1.0) in Matlab 8.2 (Turkheimer et al.,

2001). All data are presented as mean±SD, and the level α was set for all comparisons

at P<0.05, corrected.

RESULTS

All subjects underwent a battery of clinical assessments, which showed no deficits in

motor and functional (P>0.10), neuropsychiatric (P>0.10), and cognitive (P>0.10)

performance in early premanifest Huntington’s disease gene carriers compared to

healthy controls (Supplementary Tables 4-6). Neuropsychiatric assessments showed

small non-significant differences between healthy controls and premanifest

Huntington’s disease gene carriers, however the neuropsychiatric burden in our cohort

of early premanifest Huntington’s disease gene carriers was minimal.

Since GM and WM volumetric brain changes have been reported as one of the earliest

changes in the course of Huntington’s disease (Tabrizi et al., 2009), we wanted to

explore whether this was also true for the earlier cohort of premanifest Huntington’s

disease gene carriers studied here. We applied both VBM and volumetric analysis

with Freesurfer. We found no volume changes at a voxel level in any brain regions,

and averaged volumes of Freesurfer subcortical and ventricle volumetric measures did

not show significant differences between healthy controls and early premanifest

Huntington’s disease gene carriers (P>0.05; Supplementary Table 7).

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Then, we assessed the expression of PDE-10A enzyme in vivo with [11C]IMA107

PET, which is a specific and highly potent PDE-10A selective PET radioligand for

human use (Plisson et al., 2014). We performed a three-layered analysis of the

[11C]IMA107 PET BPND in: (a) MR-based anatomically defined ROIs (caudate,

putamen, ventral striatum, globus pallidus, substantia nigra and motor thalamic

nuclei); (b) connectivity-based parcellations of ROIs (limbic, cognitive and

sensorimotor striatum) according to cortical-striatal connectivity profiles; (c)

connectivity-based parcellations of ROIs (striatonigral/striatopallidal internal and

striatopallidal external projecting segments of striatum) based on striatal connections

with globus pallidus externus and substantia nigra/globus pallidus internus, and so

reflect the major parts of the indirect and direct pathways, respectively.

PDE-10A expression in anatomically defined brain regions

We found significant group differences in anatomical [11C]IMA107 BPND between the

early premanifest Huntington’s disease gene carriers and healthy controls (P<0.001).

[11C]IMA107 BPND was decreased in caudate (P<0.001; -33%), putamen (P<0.001; -

30.5%) and globus pallidus (P=0.01; -25.6%), and increased in motor thalamic nuclei

(P=0.01; +34.5%), with no significant changes observed in substantia nigra (P>0.10;

+9%) and ventral striatum (P>0.10; -16.9%) in early premanifest Huntington’s

disease gene carriers compared to healthy controls (Fig. 1A and 1B; Table 2).

Individual [11C]IMA107 BPND analysis was also performed in the early premanifest

Huntington’s disease gene carriers. The normal range was defined as including values

two standard deviation below or above the healthy controls mean [11C]IMA107 BPND.

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Ten out of 12 early premanifest Huntington’s disease gene carriers (83.3%) had

abnormally reduced striatal and abnormally increased motor thalamic nuclei

[11C]IMA107 BPND values (Fig. 1C-1F).

PDE-10A expression in the functionally defined striatum

We calculated [11C]IMA107 BPND in functional striatal subdivisions following

probabilistic tractography and connectivity-based functional striatal parcellation (Fig.

2A and 2B). Cortico-striatal connectivity-based functional analysis showed 28-30%

decreases in sensorimotor (P=0.003) and cognitive (P=0.02) striatum [11C]IMA107

BPND in early premanifest Huntington’s disease gene carriers compared to healthy

controls (Table 2). Connectivity maps of each cortical target were then thresholded at

5% of the maximum connectivity value to minimize noise and voxels with low

connectivity values, therefore allowing functional subdivisions to have a certain

degree of overlap (Fig. 2A). Following thresholding, group differences remained

significant (P=0.002), however post-hoc analysis showed [11C]IMA107 BPND

decreases only in the sensorimotor (P<0.001; -34%) but not in limbic (P>0.10) and

cognitive (P>0.10) striatum in early premanifest Huntington’s disease gene carriers

compared to healthy controls (Fig. 2C and Table 2).

Hence, we used the sensorimotor striatum as a seed mask since it was the only

subdivision with significant PDE-10A decreases, to perform striatonigral and

striatopallidal connectivity-based functional analysis (Fig. 2B). We found 23-28%

[11C]IMA107 BPND decreases in striatonigral/striatopallidal internal and striatopallidal

external projecting sensorimotor striatal segments (P=0.008) (Table 2), which further

decreased to a mean of 33-34% after thresholding at 5% of the maximum connectivity

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value in early premanifest Huntington’s disease gene carriers compared to healthy

controls (P=0.002) ( Fig. 2D and Table 2).

Correlations between PDE-10A expression and clinical characteristics

We found correlations between: (a) higher motor-thalamic-nuclei/striatal

[11C]IMA107 BPND ratio and higher two (r=0.59; P=0.044) and five (r=0.59; P=0.043)

year probability for symptomatic conversion (Fig. 3A); (b) higher motor-thalamic-

nuclei/sensorimotor [11C]IMA107 BPND ratio and higher two (r=0.66; P=0.020), five

(r=0.66; P=0.019) and 10 (r=0.63; P=0.027) year probability for symptomatic

conversion (Fig. 3B), and; (c) higher motor-thalamic-nuclei/striatopallidal

[11C]IMA107 BPND ratio and higher two (r=0.85; P=0.001), five (r=0.82; P=0.001), 10

(r=0.76; P=0.004) and 15 (r=0.65; P=0.023) year probability for symptomatic

conversion (Fig. 3C). All other interactions between clinical and imaging data were

not significant following the Hochberg multiple-comparison correction.

DISCUSSION

Here we report altered PDE-10A expression detectable with non-invasive imaging

decades before the predicted development of symptoms. We have studied a unique

cohort of early premanifest Huntington’s disease gene carriers, with normal clinical

functions, who were on average 25 years before the predicted symptomatic onset

(90% probability), and had no brain atrophy. We have used combined PDE-10A PET

molecular and MR-based structural imaging, and demonstrated bidirectional changes

in PDE-10A expression within the brain networks, which are linked to symptomatic

conversion in premanifest Huntington’s disease gene carriers.

20

Previous studies have reported significant changes in premanifest Huntington’s

disease gene carriers that appear years before onset of clinical symptoms and include

neurovascular and metabolic brain changes (eight years) (Unschuld et al., 2012; Hua

et al., 2014), brain volume loss (up to 10 years) (Tabrizi et al., 2009), increased

interleukin-6 levels in the plasma and cerebrospinal fluid (16 years) (Björkqvist et al.,

2008), and loss of striatal dopamine D2-receptors (up to 19 years) (Politis et al.,

2008). Our individual analysis showed that significant changes in PDE-10A

expression are detectable several years before the predicted development of overt

symptoms, which is to our knowledge, the earliest abnormality identified in

Huntington’s disease gene carriers.

We have quantified PDE-10A expression in the brains of early premanifest

Huntington’s disease gene carriers using both anatomical and functional criteria.

Analysis based on brain anatomy showed PDE-10A signal loss in the striatum and

globus pallidus, increased PDE-10A signal in motor thalamic nuclei, and no

significant PDE-10A signal changes in substantia nigra and ventral striatum. Then, we

used probabilistic tractography to parcellate each subject’s striatum into functional

segments according to cortical connections. Our findings showed that striatal PDE-

10A signal loss was mostly confined in the sensorimotor striatum, whereas PDE-10A

signal was not significantly altered in limbic and cognitive striatum at this stage of the

disease and with the sample examined. Subsequently we examined the sensorimotor

striatum, which was parcellated on each subject according to segments projecting to

substantia nigra and globus pallidus internus and globus pallidus externus. We found

PDE-10A signal loss in both striatonigral/striatopallidal internal and striatopallidal

external projecting segments of striatum, though decreased PDE-10A expression in

21

the striatopallidal external segment was one level more significant. The latter

observation is in line with previous studies indicating preferential degeneration of

striatopallidal external projection neurons in Huntington’s disease (Albin et al., 1992;

Richfield et al., 1995). We found substantial overlap between

striatonigral/striatopallidal internal and striatopallidal external projecting regions of

the striatum, in line with the known densities of D1 and D2 receptors throughout the

striatum (Sun et al., 2012), albeit with marginally greater prevalence of striatonigral

projecting regions in lateral and dorsal regions of the striatum, as predicted by animal

studies (Villalba and Smith, 2013).

We looked for correlations between PET and clinical data. The balance between PDE-

10A signal loss in the striatum and increased PDE-10A signal in the motor thalamic

nuclei correlated with higher probability for symptomatic conversion. Within the

striatal denominator of this ratio, PDE-10A signal loss in the striatopallidal projecting

segments of the sensorimotor striatum was the most critical for this correlation.

Previous studies using similar samples have reported correlations at a 0.05 α level

between decreased D2-receptors (van Oostrom et al., 2009) and increased activated

microglia (Politis et al., 2011) in the striatum, and predicted 5-year probability of

Huntington’s disease onset. In our study, the balance between decreased PDE-10A in

striatopallidal projections and increased PDE-10A in motor thalamic nuclei correlated

with the risk for symptomatic conversion up to a 0.001 α level with power to associate

with predicted onset up to 15 years. To our knowledge, this is the strongest reported

correlation with the risk of symptomatic Huntington’s disease conversion.

Nonetheless, this is a cross-sectional study and further longitudinal studies are needed

22

to elucidate the predictive value of [11C]IMA107 PET for determining symptomatic

onset and its utility for determining disease progression rates.

Decreased levels of PDE-10A have been reported in transgenic Huntington’s disease

mice and in post-mortem tissue of three patients with Huntington’s disease (Hebb et

al., 2004). A pilot PDE-10A PET study reported 60-70% decreases in striatal PDE-

10A expression of five manifest Huntington’s disease patients with significant striatal

atrophy (Ahmad et al., 2014). Using PET with [18F]MNI-659, Russell and colleagues

(2014) have found 47.6% decreases in striatal and pallidal PDE-10A expression in

eight early manifest Huntington’s disease patients and lower striatal [18F]MNI-659

binding was associated with disease severity and disease burden of pathology. Three

premanifest Huntington’s disease gene carriers, who were a mean of 12 years from

predicted onset, displayed decreases in striatal PDE-10A expression with a lesser

degree compared to the group of manifest Huntington’s disease patients (Russell et

al., 2014). Our anatomical analysis demonstrated 31-33% loss of striatal PDE-10A

expression at early premanifest stages indicating that striatal PDE-10A loss is an early

phenomenon, which may progresses over the course of the disease. Altered PDE-10A

expression may be due to altered transport of this protein (Leuti et al., 2012). Given

the significant body of data suggesting that mutant huntingtin affects intracellular

transport (Ross and Tabrizi, 2011), it would then be likely that altered PDE-10A

expression is a secondary event in the pathogenesis of Huntington’s disease. Also, our

group of early premanifest Huntington’s disease gene carriers had no brain atrophy

indicating that decreases in PDE-10A do not reflect loss of MSNs. However, it is

possible that the small number of subjects studied here is underpowered to detect

volumetric differences. A larger study in the future would be able to elucidate whether

23

at this early stage, premanifest Huntington’s disease gene carriers show significant

subcortical atrophy.

We did not find significant decreases in PDE-10A expression in the substantia nigra

of early premanifest Huntington’s disease gene carriers. Previous data from animal

model of Huntington’s disease have shown significant decreases in PDE-10A

immunoreactivity in the substantia nigra of R6/2 mice (Leuti et al., 2012). This

discrepancy could be explained by the fact that PDE-10A decreases were observed in

9-13 weeks of age R6/2 mice, which were fully symptomatic with severe motor

impairment. In our study, we have assessed a cohort of early premanifest

Huntington’s disease gene carriers who were clinically normal, thus we cannot

exclude the possibility that significant decreases in substantia nigra PDE-10A

expression may be present only in symptomatic Huntington’s disease patients, or at

least not in the early premanifest stages studied here.

Our functional analysis revealed that the striatal PDE-10A signal loss was mostly

confined to the dorsal striatum, which receives sensorimotor information from the

cortex (Choi et al., 2012). Previous MRI studies have shown that striatal atrophy in

Huntington’s disease follows a topographical dorso-ventral and caudo-rostral gradient

(Kassubek et al., 2004; Douaud et al., 2006). Since PDE-10A expression is important

for neuronal survival our findings are consistent with the pattern of volume loss that

can be detected at later stages of Huntington’s disease with MRI.

Mutant huntingtin by gaining a toxic effect on PDE-10A (Hu et al., 2004; Leuti et al.,

2013) would impair its function for regulating the striatonigral and striatopallidal

24

downstream signaling cascades, which work in a coordinated manner for the fine-

tuning of movement (Nishi et al., 2008; Girault, 2012; Cui et al., 2013). Imbalance of

striatal output would lead to abnormal thalamo-cortical input, which contributes to the

development of Huntington’s disease symptoms (André et al., 2010). Our findings

show 33-34% PDE-10A signal loss in striatonigral/striatopallidal internal and

striatopallidal external projecting striatal segments and 35% increases of PDE-10A

signal in the motor thalamic nuclei. Previous [18F]FDG PET studies have reported

increases in motor thalamic nuclei metabolism associated with decreases in striatal

metabolism in premanifest HD gene carriers (Feigin et al., 2001, 2007). Premanifest

HD gene carriers, who converted to symptomatic HD, showed a progressive loss of

thalamic metabolism activity at four (Feigin et al., 2007) and seven year follow-up

PET scan (Tang et al., 2013). The ventral anterior and lateral motor thalamic nuclei

receive GABAergic signals from the striatopallidal and striatonigral neurons and

convey their input to the cortex (Parent and Hazrati, 1995). Therefore, our findings

might reflect a compensatory thalamic mechanism to counterbalance the decreased

inhibitory incoming signals and consequently, physiologically stimulate the cortex

(Fig. 4A). As the disease progresses this compensatory mechanism could gradually

fail leading to an overflow of excitatory glutamatergic activity in the cortex, and

consequently manifestation of chorea (Fig. 4B) (Albin et al., 1991). However, it is

also possible that the observed thalamic increases in PDE-10A might reflect an up-

regulatory reaction to an ongoing loss of inhibitory striatal output. The importance of

thalamic signaling in motor execution is further justified as motor thalamic nuclei

provide feedback to the sensorimotor striatum through direct glutamatergic

projections (McFarland and Haber, 2000), which if impaired contribute to the

development of Huntington’s disease symptoms (Deng et al., 2013). Thus, the

25

interplay of signals between striatopallidal and motor thalamic nuclei could explain

the critical importance of PDE-10A expression in these brain areas for the

development of symptoms as indicated by our results.

Preclinical studies have postulated arguments in order to determine the use of PDE-

10A inhibitors in the clinical management of Huntington’s disease (Giampà et al.,

2009, 2010; Leuti et al., 2013). PDE-10A inhibition with TP-10 was able to reduce

the loss of striatal and cortical neurons, to increase striatal and cortical CREB

phosphorylation, and to delay the development of neurological deficits in

Huntington’s disease animal models (Giampà et al., 2009, 2010). Thus, PDE-10A

may be a target to ameliorate both pathogenesis by promoting neuronal survival and

pathophysiology by delaying the onset of symptoms of Huntington’s disease. In

premanifest Huntington’s disease stages, PDE-10A inhibition would activate the

cAMP/PKA/DARPP-32 signaling cascade by simultaneously potentiating adenosine

A2A receptor signaling and inhibiting dopamine D2-receptor signaling in

striatopallidal external neurons (Nishi et al., 2008). In the striatonigral/striatopallidal

internal pathway, PDE-10A inhibitors would potentiate the dopamine D1-receptor-

induced increase in DARPP-32 phosphorylation (Nishi et al., 2008). PDE-10A

inhibitors do not decrease PDE-10A levels but the enzyme activity (Leuti et al.,

2013). Thus, they could restore cellular mechanisms and the expression of several

transcriptional factors and genes by increasing cAMP/PKA/DARPP-32 signaling

cascade (Giampà et al., 2009, 2010; Schmidt et al., 2008; Threlfell et al., 2009).

However, it is unclear whether this increased signal would be beneficial in

Huntington’s disease. If decreased striatal PDE-10A expression is a pathological

26

effect of mutant huntingtin, PDE-10A inhibitor therapy by further reducing the

residual low-activity of PDE-10A may not be beneficial.

In conclusion, our findings highlight very early neurochemical changes in pre-

manifest Huntington’s disease gene carriers. PDE-10A expression could be a

biomarker of striatal MSNs integrity and [11C]IMA107 PET may be a useful tool for

future trials of disease-modifying therapeutics aiming to delay the onset and slow the

progression of Huntington’s disease. However, these data need to be extended in a

larger cohort over different stages of the disease in order to confirm the relationship

between PDE-10A and clinical symptomatology.

Acknowledgments

We thank all participants and their families, the PET technicians and radiochemists,

the MRI radiographers, and the clinical research nurses at Imanova Ltd for their

cooperation and support to this study. We also thank Nicola Robertson and Clare

Loane for assisting with the recruitment of Huntington’s disease gene carriers and for

demonstrating CANTAB®, respectively. Flavia Niccolini is supported from

Parkinson’s UK. Marios Politis research is supported by Parkinson’s UK, Edmond J.

Safra Foundation, Michael J Fox Foundation (MJFF), and NIHR BRC.

Funding

This research was financially supported by the National Institute for Health Research

(NIHR) Mental Health Biomedical Research Centre at South London and Maudsley

NHS Foundation Trust and King's College London, and the NIHR Biomedical

27

Research Centre at Imperial College London. The views expressed are those of the

authors and not necessarily those of the NHS, the NIHR or the Department of Health.

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Figure legends

Fig. 1 Altered PDE-10A expression in anatomically defined brain regions of early

premanifest Huntington’s disease gene carriers. (A) Mean [11C]IMA107 BPND

parametric images derived from 12 healthy controls (top) and 12 early premanifest

Huntington’s disease gene carriers (bottom) in stereotaxic space overlaid onto the T1

weighted MNI template showing significant loss of striatal and pallidal PDE-10A

signal in the early premanifest Huntington’s disease gene carriers. (B) Column bar

graph showing mean [11C]IMA107 BPND in anatomically defined brain regions

between early premanifest Huntington’s disease gene carriers and healthy controls.

(C-D) Scatterplots showing individual [11C]IMA107 BPND values in the striatum and

motor thalamic nuclei for healthy controls (HC) and early premanifest Huntington’s

disease gene carriers (pHD). Solid lines represent mean [11C]IMA107 BPND for

healthy controls and early premanifest Huntington’s disease gene carriers, and dotted

lines represent healthy controls mean minus two SDs for striatal and mean plus two

SDs for motor thalamic nuclei [11C]IMA107 BPND, respectively. (E) Axial summed

[11C]IMA107 PET images co-registered and fused with 3-T MRI images for the

striatum of a 33-year-old healthy male showing normal striatum [11C]IMA107 binding

(BPND = 2.24) (left); a 35-year-old male premanifest Huntington’s disease gene carrier

(CAGr: 40; DBS: 153; 43 years from predicted onset) showing mild to moderate

decreases in striatal [11C]IMA107 binding (BPND = 1.45) (middle-left); a 33-year-old

female premanifest Huntington’s disease gene carrier (CAGr: 43; DBS: 247.5; 25

years from predicted onset) showing moderate decreases in striatal [11C]IMA107

binding (BPND = 1.32) (middle-right); and a 52-year-old male early premanifest

Huntington’s disease gene carrier (CAGr: 41; DBS: 282.2; 21 years from predicted

37

onset) showing severe decreases in striatal [11C]IMA107 binding (BPND = 0.57)

(right). (F) Coronal summed [11C]IMA107 PET images co-registered and fused with

3-T MRI images for the thalamus of a 33-year-old healthy male showing normal

thalamus [11C]IMA107 binding (BPND = 0.43) (left); a 38-year-old male premanifest

Huntington’s disease gene carrier (CAGr: 44; DBS: 323; 17 years from predicted

onset) showing high increases in thalamic [11C]IMA107 binding (BPND = 0.93)

(middle-left); a 35-year-old male premanifest Huntington’s disease gene carrier

(CAGr: 40; DBS: 153; 43 years from predicted onset) showing moderate increases in

thalamic [11C]IMA107 binding (BPND = 0.64) (middle-right); and a 37-year-old male

premanifest Huntington’s disease gene carrier (CAGr: 43; DBS: 277.5; 22 years from

predicted onset) showing mild to moderate increases in thalamic [11C]IMA107

binding (BPND = 0.58) (right). Color bar reflects range of [11C]IMA107 BPND intensity.

Error bars represent mean ± SD. **P≤0.01; ***P<0.001.

Fig. 2 Altered PDE-10A expression in the connectivity-based parcellations of

striatum of early premanifest Huntington’s disease gene carriers, and method

overview. (A) Striatal subdivisions according to cortical-connectivity: striatum was

manually defined on the subjects’ MRI image. To obtain subdivisions of the striatum

in each subject the projections from cortical regions associated with limbic (red),

cognitive (yellow) and sensorimotor (light blue) functional specialization, to the

striatum were calculated, and the striatal areas associated with cortical area were

established. Each subject’s connectivity maps were thresholded at 5% of the

maximum connectivity value to minimize noise and voxels with low connectivity

values. Subsequently, cortical-striatal connectivity maps were applied to [11C]IMA107

BPND images to calculate the regional estimates of BPND. (B) Striatal connectivity-

38

based subdivisions according to striatonigral/striatopallidal internal and striatopallidal

external projections: in each subject the projections between the sensorimotor-striatal

map with the substantia nigra/globus pallidus internus and globus pallidus externus

were estimated after thresholded at 5% as previously. We generated maps of putamen

voxels specifically involved in sensorimotor networks, which projected to the

substantia nigra/globus pallidus internus or globus pallidus externus, and so reflect the

major parts of the direct or indirect pathways, respectively. (C-D) Column bar graphs

showing mean [11C]IMA107 BPND differences in connectivity-based defined brain

regions between early premanifest Huntington’s disease gene carriers and healthy

controls. **P<0.01; ***P<0.001.

Fig. 3 Correlations between PDE-10A expression and probability for symptomatic

conversion in early premanifest Huntington’s disease gene carriers. (A) Higher

thalamic motor nuclei (MN)/striatal [11C]IMA107 BPND ratio correlated with higher

two (r=0.59; P=0.044) and five (r=0.59; P=0.043) year probability for symptomatic

conversion, (B) higher motor-thalamic-nuclei/sensorimotor [11C]IMA107 BPND ratio

correlated with higher two (r=0.66; P=0.020), five (r=0.66; P=0.019) and 10 (r=0.63;

P=0.027) year probability for symptomatic conversion, and (C) higher motor-

thalamic nuclei/striatopallidal [11C]IMA107 BPND ratio correlated with higher two

(r=0.85; P=0.001), five (r=0.82; P=0.001), 10 (r=0.76; P=0.004) and 15 (r=0.65;

P=0.023) year probability for symptomatic conversion. Probabilities for symptomatic

conversion were calculated according with the survival analysis formula described by

Langbehn (2004).

39

Fig. 4 Schematic illustration of PDE-10A mediated signaling within the striato-

thalamo-cortical circuit in Huntington’s disease. (A) In early premanifest

Huntington’s disease gene carriers PDE-10A levels are decreased in the striatonigral

(direct) and striatopallidal (indirect) neurons potentiating dopamine D1 and D2

receptors signaling and leading to disinhibition of motor thalamic nuclei. Increased

PDE-10A levels in the motor thalamic nuclei may counterbalance the decreased

inhibitory signals incoming from globus pallidus and substantia nigra and

consequently, physiologically stimulate the cortex. (B) In manifest Huntington’s

disease, ongoing and significant degeneration of striatopallidal projecting neurons and

further PDE-10A decreases would lead to an even more unbalanced

striatonigral/striatopallidal signaling. Motor-thalamic-nuclei compensatory

mechanisms could gradually fail leading to an overflow of glutamate activity to the

cortex, which will result in expression of Huntington’s disease symptoms. Solid bold

lines represent increased signal, dotted lines represent decreased signal. A2A receptor:

adenosine 2A receptor; cAMP: cyclic adenosine monophosphate; DARPP-32:

Dopamine- and cAMP-regulated phosphoprotein; GPe: globus pallidus externus; GPi:

globus pallidus internus; PKA: protein kinase A; PP-I: phosphoprotein phosphatase;

SNr: substantia nigra pars reticulata.

TABLES

Table 1 Clinical Characteristics of early premanifest Huntington’s disease (HD) gene

carriers and healthy control subjects

Healthy controls Premanifest HD

No 12 12

Sex 8 M/4 F 7 M/5 F

40

Age (years ± SD) 40.0 (±6.2) 41.1 (±7.5)

CAG repeats (± SD) [range] - 41.8 (±1.3) [40-44]

Disease Burden Score1 (± SD)

[range]- 254.4 (± 46.8) [153-323]

90% p to onset2 (years ± SD)

[range]- 25 (±6.9) [17-43]

UHDRS TMS3 (±SD) 0 (±0) 0 (±0)

UHDRS DCL4 - 0 (±0)

1Disease burden score: age×(CAG length–35·5); 290% p to onset: predicted years to

Huntington’s disease symptoms onset (90% probability) calculated on the basis of the variant

of the survival analysis formula described by Langbehn (Paulsen et al., 2008); 3UHDRS TMS:

Total Motor Score (TMS) of the Unified Huntington Disease Rating Scale (UHDRS);

4UHDRS DCL: UHDRS diagnostic confidence level.

Table 2 [11C]IMA107 BPND in the groups of early premanifest HD gene carriers and

healthy controls.

Healthy

controls

Premanifest

HD

P value* % change

A-ROIs1

Caudate (mean±SD) 1.37 (±0.2) 0.92 (±0.2) <0.001 -33.0%

Putamen

(mean±SD)2.21 (±0.3) 1.55 (±0.3) <0.001 -30.5%

Ventral Striatum

(mean±SD)1.01 (±0.1) 0.84 (±0.2) >0.10 -16.9%

Substantia Nigra 0.49 (±0.1) 0.54 (±0.1) >0.10 +9.0%

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(mean±SD)

Globus Pallidus

(mean±SD)1.56 (±0.2) 1.18 (±0.3) 0.01 -25.6%

Motor thalamic

nuclei (mean±SD)0.44 (±0.07) 0.60 (±0.1) 0.01 +34.5%

Exclusive cortico-striatal CB-ROIs Striatum2

Limbic (mean±SD) 1.02 (±0.2) 0.80 (±0.2) >0.10 -21.6%

Cognitive

(mean±SD)1.48 (±0.3) 1.07 (±0.3) 0.02 -27.6%

Sensorimotor

(mean±SD)1.77 (±0.3) 1.24 (±0.3) 0.003 -29.6%

Cortico-striatal CB-ROIs Striatum with overlaps

Limbic (mean±SD) 0.83 (±0.3) 0.65 (±0.2) >0.10 -11.3%

Cognitive

(mean±SD)1.30 (±0.3) 1.06 (±0.3) >0.10 -18.5%

Sensorimotor

(mean±SD)1.95 (±0.3) 1.29 (±0.4) <0.001 -34.0%

Exclusive striatonigral and striatopallidal CB-ROIs Striatum

Striatonigral and

Striatopallidal

internal

[direct pathway]

(mean±SD)

1.49 (±0.3) 1.08 (±0.3) 0.004 -27.9%

Striatopallidal

external [indirect

1.47 (±0.4) 1.13 (±0.3) 0.036 -23.2%

42

pathway]

(mean±SD)

Striatonigral and striatopallidal CB-ROIs Striatum with overlaps

Striatonigral and

Striatopallidal

internal

[direct pathway]

(mean±SD)

1.61 (±0.3) 1.09 (±0.3) 0.008 -32.6%

Striatopallidal

external [indirect

pathway]

(mean±SD)

1.73 (±0.4) 1.15 (±0.3) <0.001 -33.9%

*P value are Bonferroni corrected; 1A-ROIs: anatomical regions of interest; 2CB-ROIs

Striatum: striatal connectivity-based regions of interest.

43