Striatal morphology correlates with frontostriatal
electrophysiological motor processing in Huntington’s
disease: an IMAGE-HD study
Lauren M. Turner1, David Jakabek2, Fiona A. Wilkes3, Rodney J. Croft4,†, Andrew Churchyard5,6,
Mark Walterfang7, Dennis Velakoulis7, Jeffrey C. L. Looi3,7,†, Deborah Apthorp1,2,† & Nellie
Georgiou-Karistianis5,†
1
Research School of Psychology, College of Medicine, Biology, & Environment, Australian National University, Canberra, Australian Capital
Territory, Australia
2
Graduate School of Medicine, University of Wollongong, Wollongong, New South Wales, Australia
3
Academic Unit of Psychiatry and Addiction Medicine, Australian National University Medical School, Canberra Hospital, Canberra, Australian
Capital Territory, Australia
4
School of Psychology & Illawarra Health & Medical Research Institute, University of Wollongong, Wollongong, New South Wales, Australia
5
School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Monash, Victoria, Australia
6
Calvary Health Care Bethlehem Hospital, Caulfield, Victoria, Australia
7
Neuropsychiatry Unit, Royal Melbourne Hospital, and Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
Keywords
Compensation, Huntington’s disease,
morphology, motor, motor response
potentials, striatum, structural
Correspondence
Lauren M. Turner, Research School of
Psychology, College of Medicine, Biology, &
Environment, Australian National University,
Canberra 0200, Australia. Tel: +61 2 6125
2795; E-mail: lauren.turner@anu.edu.au
Funding Information
This study was supported by the CHDI
Foundation (Grant/Award Number: A –
3433), the National Health and Medical
Research Council (Grant/Award Number:
606650), and the Royal Australian and New
Zealand College of Psychiatrists New
Investigator Grant.
Received: 6 December 2015; Revised: 5 May
2016; Accepted: 12 May 2016
Brain and Behavior, 2016; 0(0), e00511,
doi: 10.1002/brb3.511
†
Cosenior authors
Abstract
Background: Huntington’s disease (HD) causes progressive atrophy to the
striatum, a critical node in frontostriatal circuitry. Maintenance of motor function is dependent on functional connectivity of these premotor, motor, and
dorsolateral frontostriatal circuits, and structural integrity of the striatum itself.
We aimed to investigate whether size and shape of the striatum as a measure of
frontostriatal circuit structural integrity was correlated with functional frontostriatal electrophysiological neural premotor processing (contingent negative
variation, CNV), to better understand motoric structure–function relationships
in early HD. Methods: Magnetic resonance imaging (MRI) scans and electrophysiological (EEG) measures of premotor processing were obtained from a
combined HD group (12 presymptomatic, 7 symptomatic). Manual segmentation of caudate and putamen was conducted with subsequent shape analysis.
Separate correlational analyses (volume and shape) included covariates of age,
gender, intracranial volume, and time between EEG and MRI. Results: Right
caudate volume correlated with early CNV latency over frontocentral regions
and late CNV frontally, whereas right caudate shape correlated with early CNV
latency centrally. Left caudate volume correlated with early CNV latency over
centroparietal regions and late CNV frontally. Right and left putamen volumes
correlated with early CNV latency frontally, and right and left putamen shape/
volume correlated with parietal CNV slope. Conclusions: Timing (latency) and
pattern (slope) of frontostriatal circuit-mediated premotor functional activation
across scalp regions were correlated with abnormalities in structural integrity of
the key frontostriatal circuit component, the striatum (size and shape). This
was accompanied by normal reaction times, suggesting it may be undetected in
regular tasks due to preserved motor “performance.” Such differences in functional activation may reflect atrophy-based frontostriatal circuitry despecialization and/or compensatory recruitment of additional brain regions.
Introduction
Huntington’s disease (HD) is a progressive, autosomal
dominant disease which results in widespread
degeneration of cortical gray and white matter, as well as
localized atrophy of the striatum (Vonsattel et al. 1985;
Hobbs et al. 2010). Although symptom onset typically
begins around age 40 (Walker 2007), neuroimaging
ª 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
Brain and Behavior, doi: 10.1002/brb3.511 (1 of 13)
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
Striatal Morphology and Premotor Activation in HD
demonstrates progressive atrophy up to 20 years prior
(Tabrizi et al. 2009, 2011, 2013; van den Bogaard et al.
2011a,b; Aylward et al. 2012; Dominguez et al. 2013;
Georgiou-Karistianis et al. 2013). Involuntary motor dysfunction is a disease hallmark, reflecting damage to the
basal ganglia. Symptomatic (symp-HD) individuals present with motor symptoms such as chorea (Nance et al.
2011) as well as detriment to initiation, execution, and
termination of voluntary movement (Smith et al. 2000;
Quinn et al. 2001; Boulet et al. 2005; Lemay et al. 2008).
Despite neuronal degeneration, individuals in early stages
(pre-HD) present with minimal functional differences
compared with healthy persons (e.g., they react quickly,
and can perform simple tasks at the same speed). However, impairments have been detected during more complex motor tasks (e.g., Farrow et al. 2006; GeorgiouKaristianis et al. 2014b), or in subcomponents of performance efficiency (e.g., increased tapping variability)
within larger studies (e.g., Bechtel et al. 2010). Retention
of motor functionality in pre-HD in the context of progressive atrophy is not well understood. One suggestion
implicates functional connectivity: additional brain
regions are recruited through reorganization of frontostriatal circuitry to counter early structural changes, compensatory processes which may fail with additional task
demands and greater atrophy (Beste et al. 2007, 2009;
Kl€
oppel et al. 2009; Dominguez et al. 2013; GeorgiouKaristianis et al. 2013, 2014a; Koenig et al. 2014). Electroencephalography (EEG) shows promise in identifying
subtle changes in integrity of motor processing, and may
offer new insights into the mismatch between neural atrophy and successful motor performance (Turner et al.
2015).
Electroencephalography has been suggested to be more
sensitive to early disruption of cortical connectivity in
HD than typical clinical measures (Lefaucheur et al.
2002), and shows promise in providing a measure of disease progression prior to observable motor deficits. EEG
allows for the measurement of neural activity associated
with sensorimotor integration and motor planning via the
contingent negative variation (CNV; Ikeda et al. 1997).
Such premotor function reflects cortical sources including
the supplementary motor area (SMA; Macar et al. 2004),
primary motor area (M1), and basal ganglia (Rektor et al.
2004). The early component of the CNV indexes prefrontal and supplementary sensorimotor areas (SSMA),
the late component the prefrontal, M1, primary sensory,
temporal, and occipital and SSMA areas (Hamano et al.
1997). As an index of complex premotor-related activation, the CNV may be capable of detecting subtler
changes in motor processing resulting from changing
structural integrity. For example, we recently demonstrated abnormal CNV in pre-HD (early, overengagement
Brain and Behavior, doi: 10.1002/brb3.511 (2 of 13)
L. M. Turner et al.
of premotor processes that were less focused than controls
and occurred across electrode sites), despite intact motor
processing (as measured by the readiness potential) and
normal reaction times (Turner et al. 2015). This irregular
CNV activation may reflect changes in functional connectivity.
Premotor and motor activation rely on parallel and
converging circuitry traversing both brain hemispheres
(Alexander et al. 1989). The striatum represents a critical
node in a number of fronto-striato-pallido-thalamo-cortical re-entrant circuits which regulate cognitive, emotional,
and behavioral as well as motor functions (Draganski
et al. 2008). Based on the frontostriatal circuit model
(Alexander et al. 1986), a number of studies have shown
preferential localization of motor control to the dorsolateral prefrontal cortex (DLPFC) and premotor and motor
loop circuits (e.g., Taniwaki et al. 2003; Lehericy et al.
2004; Utter and Basso 2008). The DLPFC can be thought
of as a “cognitive loop,” which underpins executive functioning (Alexander et al. 1986; Cummings 1993). The
striatum is highly topographically organized based on
afferents from the cortex (Bohanna et al. 2011; see
Fig. 1). Structural changes in the striatum are thus one
possible mechanism by which the CNV may be affected
in HD, in that loss of structural integrity may disrupt
frontostriatal circuits involved in motor control, prior to
the onset of clinical motor sequelae. One avenue of
research that may clarify this issue is by linking electrophysiological measures of premotor activation (CNV)
with structural MRI measures of striatal morphology.
Recently, functional magnetic resonance imaging
(fMRI) studies have implicated functional connectivity
changes in pre-HD, including frontostriatal networks
implicated in motor control (Unschuld et al., 2012; Georgiou-Karistianis et al. 2013; Georgiou-Karistianis et al.
2014b; Koenig et al. 2014; Poudel et al. 2014, 2015).
Weakened M1 connectivity with SMA and DLPFC was
found only in pre-HD individuals temporally distant from
onset of symptoms (Wolf et al. 2012), suggesting that
reorganization may be a dynamic process depending on
disease stage and the availability of neural resources.
Kl€
oppel et al. (2009) identified increased compensation in
pre-HD individuals, involving flexible recruitment of premotor and parietal areas, dependent on the pace and
complexity of motor sequences. Reduced primary motor
activation has been accompanied by parietal overactivation in symp-HD (Bartenstein et al. 1997; Weeks et al.
1997; Gavazzi et al. 2007) and by excessive thalamo-cortical activation during motor sequence processing in
presymptomatic individuals (pre-HD; Feigin et al. 2006).
Furthermore, reduced sensorimotor network synchrony
occurs prior to cognitive impairments in dorsal networks
and has also been associated with performance deficits,
ª 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
L. M. Turner et al.
Striatal Morphology and Premotor Activation in HD
Figure 1. Striatal afferent connections
indicate surface regions of the striatum
which receive afferents from respective
regions of cortex. Reproduced with
permission from Looi et al. (2011).
such as imprecision during speeded self-tapping (Poudel
et al. 2014). Reduced connectivity of M1 both within and
outside the motor network has been associated with
reduced motor performance, greater motor symptoms,
and neostriatal atrophy, and may reflect compensatory
reorganization of function as well as decreased preferential connectivity (specialization) in pre-HD (Koenig et al.
2014). Combining an electrophysiological measure of premotor activation (CNV) with structural MRI will enable
investigation of subtle components of motor response,
and their relationship with striatal morphology. A better
understanding of the structural–functional integrity specific to the neural circuitry impaired during the disease will
inform our understanding of the function and pathophysiology of HD, such as possible network despecialization/
compensation through progressive atrophy.
In this study, we sought to explore neural substrates of
abnormal premotor activation in HD (as measured by the
CNV) identified in our previous paper (Turner et al.
2015). There are two key reasons to investigate the striatum, based on putative alterations in frontostriatal circuit
structure and function, as reflected by CNV. First, there is
a neuroanatomical rationale, based on striatal atrophy in
ª 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
HD and the localization of frontostriatal motor afferents
to the caudate head and body, as well as the medial and
lateral aspects of the putamen (Haber 2003; Draganski
et al. 2008). This rationale predicts that disease progression will undermine frontostriatal motor connectivity.
Second, there is preliminary evidence that changes in
frontostriatal functional connectivity occur early in disease processes and may enable compensation. Hence,
integrative examination of structure–function correlations
may yield new insights into mechanisms of maintenance
of function. Based on the preceding, we hypothesized: (1)
that an abnormal CNV would be associated with reduced
structural integrity of the key frontostriatal circuit components, caudate and putamen, as measured by morphology (Looi and Walterfang 2013); and (2) as the CNV
requires premotor and motor activation, morphometry
(quantification of the shape) of the striatum will likely
demonstrate atrophy (shape deflation) in the lateral posterior caudate and putamen structures, reflecting reduced
functional connectivity of the dorsolateral prefrontal,
motor, and premotor frontostriatal circuits.
In order to investigate our hypotheses, both CNV and
structural MRI data were obtained from a group of HD
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Striatal Morphology and Premotor Activation in HD
participants. To maximize statistical power and account
for within-group variability in disease burden score
(DBS), cytosine–adenine–guanine (CAG) repeats, and disease onset, we included both presymptomatic (pre-HD)
and symptomatic individuals (symp-HD) in our sample.
We hypothesized that increased CNV amplitude, representing neural premotor processing during a sensorimotor
integration task, would be associated with smaller volumes (indicating more atrophy) in the caudate and putamen.
Methods
Acquisition procedures
Participants
Participant data were obtained in 2008–2009 as part of
the IMAGE-HD project (Georgiou-Karistianis et al.
2013), with EEG and MRI data collected on different
days. Time periods between EEG testing and MRI were
computed for each individual (“time to scan”), and used
as a covariate in all analyses. Participants consisted of 7
genetically confirmed HD individuals (five males; sympHD) and 12 genetically confirmed prodromal individuals
with no clinical manifestations (seven males; pre-HD).
This represents the entire HD sample reported in Turner
et al. (2015) that also completed the imaging portion of
the study (and that had usable data).1 All participants
provided consent to participate in EEG and imaging studies and were assessed by a neurologist (A. C.). Ethics
approval was obtained from the Melbourne Health
Human Research Ethics Committee and for image analysis, the ANU Human Research Ethics Committee.
Research procedures complied with the Code of Ethics of
the World Medical Association (Declaration of Helsinki).
Clinical, neuropsychiatric, and cognitive measures
In addition to EEG and MRI data collection, a range of
clinical, neuropsychiatric, and cognitive measures were
collected. Participant demographics and clinical data are
displayed in Table 1; only measures relevant to the present paper are reported below. We display between-group
data to show subgroup characteristics are similar to those
reported in the literature, and discuss these briefly here.
In order to characterize the groups, HD individuals with
an UHDRS [Unified Huntington’s Disease Rating Scale
1
Supplementary date including electrophysiological data of participants without imaging data (excluded from analysis), along
with plots for the combined and paper sample, code for analysis,
and raw data for each participant are provided on Figshare (doi:
10.6084/m9.figshare.3168145).
Brain and Behavior, doi: 10.1002/brb3.511 (4 of 13)
L. M. Turner et al.
(1996)] total motor score of 5 or less were included in
the pre-HD group and those with scores greater than 5 in
the symp-HD group (Tabrizi et al. 2011). Nonparametric
Mann–Whitney U tests indicate that as expected, sympHD had right and left caudate and putamen volumes that
were significantly smaller than pre-HD. Symp-HD were
also significantly older (P = 0.045), and scored significantly more poorly on Trails B (P = 0.005; 158.00 and
73.96 msec), speeded finger tapping (P < 0.001; 335.53
and 215.86), reaction time during the CNV task
(P < 0.05; 1272.94 and 820.17), and the UHDRS total
motor score (P < 0.001). These results are as expected
given the level of disease progression in these individuals.
As our aim was to combine genetically confirmed individuals into one group for increased power, between-group
data will not be discussed elsewhere in the manuscript.
Electrophysiological recording and analysis
Presentation of stimuli and recording of behavioral
responses were controlled by Stim2 (Version 4.0; Compumedics, Neuroscan, TX). EEG data were recorded and
processed using Scan 4.1 (Compumedics, Neuroscan,
Charlotte, NC) software. A 40-channel Lycra EEG cap
with embedded tin surface electrodes was used, with 40
recording electrodes placed according to the international
10/20 system. The EEG was referenced to a point midway
between Cz and Pz, with a ground electrode located midway between Fz and Pz. Impedances were below 10 kΩ
for all electrodes at the start of the recording. Eye movements (EOG) were measured for subsequent EOG correction, with electrodes placed above and below the left eye,
and on the outer canthus of each eye. EEG and EOG signals were amplified using a NuAmps 40-channel DC
amplifier (Compumedics, Neuroscan) with a digital bandpass filter at 0.15–100 Hz, and sampled at 1000 Hz. Data
were stored offline for later processing.
CNV task
A Go/No-Go task was employed to elicit electrophysiological components of sensorimotor integration (CNV).
All participants were right handed and elected to use
their dominant hand during the motor task. A 500-msec
warning stimuli was presented (blue light flash; S1), followed 2.5 sec later by an “X” or “Y” visual cue (S2),
appearing randomly on either the right or left side of
the screen. Participants were instructed to respond only
to the “X” stimulus and to press a button corresponding
to its position on the screen. The No-Go stimuli
occurred in 20% of trials, and the intertrial interval was
randomly varied between 2500 and 4000 msec across a
total of 90 trials.
ª 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
L. M. Turner et al.
Striatal Morphology and Premotor Activation in HD
Table 1. Demographic data between groups and in overall sample (ALL-HD).
Mean SD
Demographics
Gender (M:F)
Age
Education (years)
CAG
ICV
Time to scan
Probability of diagnosis
Illness duration
DBS
UHDRS
IQ estimate
BDI-II
Trails B
Speeded tapping
HVLT total recall
HVLT delayed recall
HVLT % retention
HVLT recognition discrimination index
Reaction time (CNV)
Volume (mm3)
Right putamen
Left putamen
Right caudate
Left caudate
Pre-HD (n = 12)
Symp-HD (n = 7)
ALL-HD (n = 19)
7:5
42.16
11.83
42.16
1425.24
40.33
0.21
–
269.00
0.33
112.90
6.75
73.96
215.86
24.91
8.75
87.45
10.25
820.17
8.56 (41, 65)*
2.63
1.51
120.14
70.70 ( 364, 174)*
0.78
142.04**
8.32 (11, 33)
7.00
3.57
94.01**
100.04**
9.12
3.63
37.67
2.70
382.69*
12:7
46.58
11.73
42.31
1426
127.26
–
–
320.55
6.63
112.13
5.68
104.92
259.96
22.63
8.21
87.92
9.79
971.09
226.60**
192.96**
199.59**
156.22**
2317.44
2321.40
2865.74
2806.08
2692.88
2682.02
3265.68
3208.72
12.14 (24, 64)
2.28
2.72
170.82
108.73 ( 242, 105)
0.23
104.42
0.65 (0, 2)
5.48
7.60
23.63
12.65
4.29
1.91
10.46
1.21
268.94
5:2
51.57
11.57
42.57
1429.82
276.28
–
1.42
408.92
17.42
110.82
3.85
158.00
335.53
18.71
7.28
88.72
9.00
1272.94
505.79
579.64
662.22
711.01
1673.83
1703.18
2180.11
2115.84
12.25
2.35
2.31
150.50
150.22 ( 364, 105)
134.90
9.75
5.98
6.45
70.86
83.37
6.96
23.25
23.25
1.93
371.63
654.61
673.10
755.45
781.30
CAG, cytosine–adenine–guanine; ICV, intracranial volume. IQ (NART: National Adult Reading Test 2nd Edition). Time to scan computed in days
from EEG baseline to MRI. Probability of onset in 5 years calculated from Langbehn et al. (2004). Disease Burden Score (CAG-35.5) 9 age;
UHDRS, motor subscale score, Unified Huntington’s Disease Rating Scale (pre-HD, UHDRS <5; symp-HD, UHDRS ≥ 5); predicted Full Scale IQ converted from performance on the National Adult Reading Scale. Nonparametric Whitney–Mann U tests for differences between groups; *P < 0.05;
**P < 0.01.
Imaging
Structural MRI images were acquired using a Siemens
Magnetom Tim Trio 3 Tesla scanner (Siemens AG, Erlangen, Germany) with a 32-channel head coil at the Murdoch Children’s Research Institute (Royal Children’s
Hospital, Victoria, Australia). High-resolution T1weighted images were taken (192 slices, 0.9-mm slice
thickness, 0.8-mm in-plane resolution, TE = 2.59 msec,
TR = 1900 msec, flip angle = 9°).
Data analysis
Electrophysiological data
The CNV was filtered using a 0.03–35 Hz bandpass zero
phase shift filter (24 dB roll-off) consistent with literature
and our previous study (De Tommaso et al. 2007; Turner
et al. 2015). The CNV was epoched to the period of
3500 to 1000 msec respective to S2 (Go-No-Go) cue
ª 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
presentation. Epochs were baseline corrected (relative to
3500 to 3000 msec) and averaged separately for electrode site. We used automated artifact rejection procedures (150 lV; excluding EOG channels), accompanied
by rejection of contaminated trials by visual inspection.
Both Go and No-Go trials were included in the grand
average, and participants with less than 15 epochs were
excluded from analysis.
Early and late CNV values were obtained from sites Fz,
Cz, and Pz. Selection of electrode sites was made a priori,
and aimed to mirror both our previous study (Turner
et al. 2015) and prior studies, suggesting possible abnormal activation across these sites (Bartenstein et al. 1997;
Ikeda et al. 1997; Weeks et al. 1997; Feigin et al. 2006;
Gavazzi et al. 2007; Kl€
oppel et al. 2009). We derived early
and late CNVs as peak amplitudes during the epoch
2450 to 2250 msec, and 200 to 0 msec, respectively
(De Tommaso et al. 2007). The relative amplitude of the
CNV was computed using peak-to-peak values which
comprised the difference between the peak during the
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Striatal Morphology and Premotor Activation in HD
early CNV and that of the late CNV. We used the polyfit
function in MatlabTM (MATLAB and Statistics Toolbox,
The MathWorks, Inc., Natick, MA) (RRID:SCR_001622)
to fit a regression slope for each participant, calculating
slope average and goodness of fit. Regression slopes were
calculated for main electrode sites Fz, Cz, and Pz.
Volume analysis
Neostriatal volumes (caudate and putamen) were
obtained by a single trained researcher (F. A. W.) by
manual tracing following a validated protocol (intrarater
intraclass correlation 0.88–0.98) using ANALYZE 11.0
(Mayo Foundation, Rochester, MI; RRID:SCR_005988)
software. Protocol details are available elsewhere (Looi
et al. 2008). This manual tracing yields binary shapes for
the structures. Intracranial volume (ICV) was calculated
using FSL’s Brain Extraction Tool (Smith et al. 2002).
FSL (RRID:SCR_002823) is freely available for noncommercial use and can be obtained from FMRIB’s Software
Library (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/).
Shape analysis
Shape analysis procedure replicated a previous study
(Macfarlane et al. 2015). Shapes were analyzed in a
semiautomated manner using the University of North
Carolina shape analysis toolkit (http://www.nitrc.org/projects/spharm-pdm/), full details of which are available
elsewhere (Styner et al. 2006; Levitt et al. 2009). Segmented three-dimensional binaries are initially processed
to ensure interior holes are filled, followed by morphologic closing and minimal smoothing. These are then
subjected to spherical harmonic shape description,
whereby boundary surfaces of each shape are mapped
onto the surface of a sphere and the surface coordinates
were represented through their spherical harmonic coefficients (Brechb€
uhler et al. 1995). The correspondence
between surfaces is established by parameter-based rotation, based on first-order expansion of the spherical harmonics. The surfaces are uniformly sampled into a set
of 1002 surface points and aligned to a study-averaged
template for each structure (left and right caudate and
putamen) using rigid body Procrustes alignment (Bookstein 1996). Scaling normalization was performed to
remove the effect of head size/ICV, using a surface scaling factor: fi, where fi = (mean [ICV]/ICV I1/3) (Styner
et al. 2007).
Statistical analyses
All statistical analyses were performed using SPSS statistical software version 20.0 (IBM SPSS Statistics,
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L. M. Turner et al.
Chicago, IL). The data were initially divided into preHD and symp-HD samples for the purposes of volumetric comparisons to describe the characteristics of the
subgroups. Nonparametric Mann–Whitney U tests were
used to examine between-group differences in volume
estimates of the left and right caudate and putamen
regions. Nonparametric Wilcoxon signed-rank tests were
used to examine within-group differences in caudate
and putamen volumes between hemispheres for each
group (pre-HD, symp-HD) and the combined group
(ALL-HD).
Correlational analyses for volume and shape (morphology) were conducted on the combined group (ALL-HD)
to increase sample size, statistical power, and account for
individual differences in disease progression. All correlational analyses used age, gender, ICV, and time to scan as
covariates. For volume, Pearson’s partial correlations were
used to examine the relationship between volume estimates (left and right caudate and putamen) and electrophysiological, clinical, and behavioral variables. For shape,
Pearson’s partial correlation was used to determine variable relationships between morphology (left and right
caudate and putamen) based on distance of surface points
from an average shape (Styner et al. 2006; Levitt et al.
2009). Electrophysiological variables consisted of amplitude, relative amplitude, latency, and slope estimates of
the CNV at Fz, Cz, and Pz. Clinical variables included
DBS, CAG repeats, and Trails B scores. Behavioral variables included reaction time derived from the CNV task.
Results
The CNV
Grand average waveforms of the CNV are shown in Figure 2. Typical grand mean distribution is observed with
CNV maximal at central scalp sites (Cz), and beginning
approximately 1500 msec prior to presentation of stimulus 2 (S2).
Volume and shape analyses
Age, gender, and ICV were used as covariates in volume
and shape analyses. Symp-HD demonstrated significantly
smaller volumes than pre-HD of both caudate (right and
left P < 0.001) and putamen (right P = 0.004, left
P = 0.010). Nonparametric Wilcoxon signed-rank tests
failed to demonstrate any significant differences in volumes between hemispheres for any group, or the combined group (ALL-HD). Relationships between neostriatal
shape and electrophysiological and behavioral variables
were examined; to account for sample size and interindividual variability in disease progression, the pre-HD and
ª 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
L. M. Turner et al.
Striatal Morphology and Premotor Activation in HD
Figure 2. Grand average waveforms of
the contingent negative variation (CNV) at
Fz, Cz, and Pz; S1 and S2 are the warning
and stimulus onset times, respectively. Early
CNV refers to the period 550–750 msec
following presentation of the warning light
(stimulus 1); late CNV refers to the period
200 msec prior to the onset of the Go/NoGo cue for button press (stimulus 2).
symp-HD group were combined in analyses to increase
statistical power (ALL-HD).2
Volume correlations with CNV latency, amplitude, and
relative amplitude are shown in Table 2. For behavioral
variables, reaction time was significantly correlated with
right putamen volume only ( 0.605, P = 0.022). Speeded
tapping, a clinical test, was significantly correlated with
right caudate volume ( 0.540, P = 0.046). Trails B score
was significantly correlated with right ( 0.593, P = 0.025)
and left caudate volumes ( 0.555, P = 0.039), as well as
the right putamen ( 0.540, P = 0.046).
Shape correlations for the early CNV latency at Cz and
the CNV slope at Pz are presented in Figures 2 and 3,
respectively. Latency of the early CNV component at Cz
2
A table of partial correlations between groups (pre-HD and
symp-HD) is included for the reader’s interest in Table S1.
ª 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
was negatively correlated with shape in both medial (centralized) and lateral (anterior and posterior; patchy)
aspects of the right caudate. Shape deflation was most
notable in negative correlations between CNV slope at Pz
in the medial and lateral aspects of the right putamen
(medial, central, lateral, anterior and posterior), and left
putamen (medial and lateral, widespread); most of this
shape deflation was seen on the dorsal aspect of the putamen on both medial and lateral sides (Fig. 4).
Discussion
This study examined the relationship between neural premotor processing during a sensorimotor integration task
(CNV) and neostriatal morphology, to better understand
the relationship between impaired frontostriatal circuitmediated motor function and structural integrity in HD.
In these findings, we highlight implications for neural
Brain and Behavior, doi: 10.1002/brb3.511 (7 of 13)
Striatal Morphology and Premotor Activation in HD
L. M. Turner et al.
Table 2. Partial correlations between caudate and putamen volume
and amplitude, relative amplitude (slope), and latency of electrophysiological motor component (CNV).
Caudate
Right
Amplitude
Early
Fz
0.424
Cz
0.340
Pz
0.0709
Late
Fz
0.313
Cz
0.240
Pz
0.041
Latency
Early
Fz
0.666**
Cz
0.691**
Pz
0.535
Late
Fz
0.685**
Cz
0.440
Pz
0.221
Difference/Slope
Fz
0.041
Cz
0.049
Pz
0.286
Putamen
Left
Right
Left
0.506
0.410
0.035
0.264
0.194
0.088
0.269
0.151
0.058
0.292
0.329
0.013
0.387
0.015
0.344
0.262
0.077
0.175
0.436
0.651*
0.614*
0.704**
0.032
0.275
0.629*
0.282
0.530
0.747**
0.547
0.405
0.314
0.063
0.104
0.446
0.025
0.102
0.039
0.025
0.241
0.286
0.056
0.781**
0.154
0.169
0.846**
Partial correlations controlled for age, gender, ICV, and time to scan.
Time to scan computed in days from EEG baseline to MRI; N = 18;
df = 12; *P < 0.05; **P < 0.01.
sources of the CNV, and contributions of frontostriatal
functional connectivity to premotor performance. All correlational analyses for volume and shape were conducted
on the combined group (ALL-HD) to increase statistical
power and account for individual differences in disease
progression.
Volumetric analyses
Volume associations with integrity of circuits
Contingent negative variation amplitude across scalp
regions and component (early, late) showed no relationship with volume. However, relative activation (slope) of
the CNV over parietal regions correlated with right and
left putamen volume, with greater slope (indicating
appropriate premotor preparation) associated with more
volume in these regions. Larger putamen volumes (bilaterally) have been significantly associated with connectivity
of M1 with nonmotor regions, including the right precentral gyrus, and the right and left cuneus (Koenig et al.
2014), suggesting that frontostriatal projections to the
putamen overlap across multiple, preferential functional
network loops. Based on these results, putaminal volume,
as an index of structural integrity, seems to be related to
the ability to recruit additional compensatory networks.
With further decreasing putaminal volumes in symp-HD,
this ability may be lost; larger studies will be needed to
confirm this hypothesis.
Figure 3. Correlations between caudate and putamen shape and latency of the electrophysiological motor component (early CNV at Cz).
Pearson’s partial correlations are shown in the left pane, raw P-values in the middle pane, and FDR corrected P-values in the right pane. Medial/
lateral denotes viewpoint of the caudate or putamen presented.
Brain and Behavior, doi: 10.1002/brb3.511 (8 of 13)
ª 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
L. M. Turner et al.
Striatal Morphology and Premotor Activation in HD
Figure 4. Correlations between caudate and putamen shape and slope of the electrophysiological motor component at Pz. Pearson’s partial
correlations are shown in the left pane, raw P-values in the middle pane, and FDR corrected P-values in the right pane. Medial/lateral denotes
viewpoint of the caudate or putamen presented.
Volumetric associations with timing of premotor
activation
Timing of neural premotor activation (latency) was correlated with volume in the right caudate (early Fz, Cz, late
Fz), left caudate (early Cz, late Fz) and right and left
putamen (early Fz). Additionally, motor execution variables of reaction time and speeded tapping were correlated with volume in the right putamen and caudate,
respectively. Lower volumes in these regions, representing
decreased structural integrity, were associated with
delayed neural premotor activation and execution (slower
overall reaction times, fewer taps in speeded trial).
Although the groups were combined in morphological
analyses, it should be noted that in our previous work,
only symp-HD, but not pre-HD, demonstrated significantly slower reaction times and fewer taps during the
task compared with healthy controls (Turner et al. 2015).
Such volumetric correlations combined with normal
task performance (reaction times) may support a threshold of maintained performance prior to significant degeneration, with progressive failure of frontostriatal networks
first resulting in heightened and prolonged premotor activation prior to the onset of performance deficits. Indeed,
other studies have demonstrated motor performance (tapping variability) correlates with bilateral gray matter atrophy of caudate and putamen, white matter loss, cortical
thinning (Bechtel et al. 2010), and approach of symptom
onset (Hinton et al. 2007), suggesting degeneration
ª 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
eventually impedes motor precision and possible compensation. In maintaining motor function in the interim,
functional specificity of frontostriatal networks and/or
compensation is a complex process which likely varies
with disease progress. For example, tapping precision in
pre-HD, but not symp-HD, has been positively correlated
with synchrony in the medial primary motor cortex (Poudel et al. 2014), and weakened M1 connectivity with the
SMA and dysfunction in the DLPFC using fMRI in preHD, but not symp-HD (Wolf et al. 2011, 2012). Early
structural pre-HD changes are also suggested to weaken
M1 connectivity with premotor regions and the caudate
nucleus itself (Unschuld et al. 2012), which may elicit
compensation, with decreased caudate and increased SMA
and anterior cingulate activation identified in pre-HD
individuals more than 12 years, but not less than 12 years
from onset (Paulsen et al. 2004). Within the striatum,
motor relay loops connect the M1 and the pallidum
(Koenig et al. 2014) as well as premotor regions and the
pallidum (Draganski et al. 2008), and thus may circumvent early atrophy. Such temporary recruitment fallible to
degeneration is consistent with clinical observation in
HD, where by and large pre-HD individuals do not
demonstrate observable motor impairment. This process
is likely mediated by individual differences such as cognitive reserve (Papoutsi et al. 2014). These studies highlight
the complex nature of motor processing in a degenerative
context, with preservation of motor performance dependent upon functional connectivity and available resources.
Brain and Behavior, doi: 10.1002/brb3.511 (9 of 13)
Striatal Morphology and Premotor Activation in HD
Shape analyses
Shape associations with integrity of circuits
Shape deflation, representing atrophy of the surface of the
striatum, was most notable in negative correlations with
CNV slope at Pz (where a flattened slope indicates unregulated premotor activation) corresponding to deflation to
the medial and lateral aspects of the right (medial, central, lateral, anterior and posterior) and left putamen
(medial and lateral, widespread). This was particularly
widespread on the dorsal aspect of the putamen, with
greater shape deflation associated with a flatter slope at
Pz across the CNV period. Integrity of the putamen is
associated with direct motoric functionality (Draganski
et al. 2008), with dorsolateral regions responsible for
motor and sensorimotor control (Alexander et al. 1986).
Successful sensorimotor integration in the CNV requires
coordination of motor and nonmotor functionality (planning and execution), with the early component of the
CNV theorized to reflect prefrontal and SSMA and the
late component prefrontal, M1, S1, temporal, occipital,
and SSMA activation (Hamano et al. 1997). Hence, the
slope of the CNV may reflect more widespread connectivity of frontostriatal circuits during premotor functions. A
flatter Pz slope during CNV may suggest activation is
inflexible and does not reflect the demands of the task
(early and late), perhaps indicating disruption of premotor circuitry. The link between direct motor functionality
and the putaminal integrity (as measured by shape) was
supported by negative correlations between reaction time,
Pz slope, and volume of the right putamen. Greater atrophy was associated with slower motor execution and flatter slope, respectively. Atrophy to the dorsal putamen
may compromise frontal motor projection areas, potentially reflecting a limited ability to recruit dorsolateral circuitry and regulate motoric function.
Shape associations with timing of premotor
activation
Timing of activation (latency) in the early CNV was
related to morphology in both medial (centralized) and
lateral (anterior and posterior; patchy) aspects of the right
caudate. Greater shape deflation, that is atrophy, in the
right caudate was associated with delayed neural premotor activation. Structural integrity of the caudate has been
associated with prefrontal motor control; neostriatal
motor projections occupy the dorsolateral caudate, connecting premotor and M1 areas and implicating the
region in motor control (Haber 2003; Taniwaki et al.
2003). In pre-HD, structural changes have been associated
with weakened connectivity of M1 and premotor regions
Brain and Behavior, doi: 10.1002/brb3.511 (10 of 13)
L. M. Turner et al.
with the caudate (Unschuld et al. 2012). In older adults,
shape deflations in corresponding posterolateral aspects of
the left caudate have previously been associated with
motor dysfunction in age-related white matter hyperintensities (Macfarlane et al. 2015). Therefore, based on our
results and previous findings, degenerative changes to the
(in our case, the right) caudate may impair motor planning and execution, resulting in irregular neural premotor
activation and delayed motor execution.
In our previous paper, we highlighted abnormal CNV
activation in pre-HD individuals (Turner et al. 2015). In
this follow-up paper, we aimed to clarify the contribution
of the striatum to abnormal premotor activation. Here,
we demonstrate that premotor activation is dependent on
structural integrity of the striatum (caudate and putamen). We propose that disease-related atrophy progressively impedes functional connectivity of critical
frontostriatal circuits involved in motor control, resulting
in subtle premotor changes to the CNV profile. We are
not necessarily implying that there is a linear loss of function; however, we have demonstrated there is a correlation of volume loss (atrophy) in the striatum with
abnormal CNV at the stages measured. The abnormal
CNV consists of delayed and flattened neural premotor
activation in the context of normal execution, and from
this we draw two conclusions. First, degenerative changes
to the right caudate morphology are associated with significantly delayed neural premotor activation and execution, which likely reflects impaired planning and
execution of simple movements. Second, atrophy to the
dorsolateral putamen is associated with a significantly
flattened CNV slope at Pz. Due to circuit organization,
progressive putaminal atrophy appears to impede access
to crucial dorsolateral circuitry used to regulate motor
activation and additional compensatory networks which
support motor performance. These results provide strong
evidence to support the compromise of various frontostriatal networks through progression of HD, as well as
despecialization during early stages of the disease to support motor performance. Taken together, the correlations
between the CNV and morphology of the striatum support the utility of concurrent measurement toward localizing neural premotor pathways and developing
complimentary biomarkers in HD, which can potentially
be implemented in clinical assessment and prognostication.
Acknowledgments
F. A. Wilkes was supported by Royal Australian and New
Zealand College of Psychiatrists New Investigator Grant.
D. Velakoulis and M. Walterfang provided computer
infrastructure and software to perform the MRI analysis
ª 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
L. M. Turner et al.
through the Melbourne Neuropsychiatry Centre, University of Melbourne. J. C. L. Looi self-funded computer
infrastructure, software, travel, and accommodation costs
to coordinate this research through the Australian US
Scandinavian/Spanish Imaging Exchange (AUSSIE) network. We thank the Royal Children’s Hospital for the use
of their 3T MR scanner. We are grateful to the CHDI
Foundation Inc., New York (USA) (Grant Number A –
3433) and to the National Health and Medical Research
Council (NHMRC) (Grant Number 606650) for their
support in funding this research.
Conflict of Interest
None declared.
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Supporting Information
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Table S1. Partial correlations between caudate and putamen volume and amplitude, relative amplitude (slope),
and latency of electrophysiological motor component by
gene status.
Brain and Behavior, doi: 10.1002/brb3.511 (13 of 13)