A JOURNAL OF NEUROLOGY
Exploring the cortical and subcortical functional
magnetic resonance imaging changes associated
with freezing in Parkinson’s disease
James M. Shine,1Elie Matar,1Philip B. Ward,2Samuel J. Bolitho,1Moran Gilat,1Mark Pearson,1
Sharon L. Naismith1and Simon J. G. Lewis1
1 Parkinson’s Disease Research Clinic, Brain and Mind Research Institute, The University of Sydney, NSW 2050, Australia
2 School of Psychiatry, University of New South Wales, Sydney/Schizophrenia Research Unit, South Western Sydney Local Health District, Australia
Correspondence to: Simon J. G. Lewis
Parkinson’s Disease Research Clinic
94 Mallett Street, Camperdown
Sydney, NSW, Australia, 2050
Freezing of gait is a devastating symptom of advanced Parkinson’s disease yet the neural correlates of this phenomenon remain
poorly understood. In this study, severity of freezing of gait was assessed in 18 patients with Parkinson’s disease on a series of
timed ‘up and go’ tasks, in which all patients suffered from episodes of clinical freezing of gait. The same patients also underwent
functional magnetic resonance imaging with a virtual reality gait paradigm, performance on which has recently been shown to
correlate with actual episodes of freezing of gait. Statistical parametric maps were created that compared the blood oxygen level-
dependent response associated with paroxysmal motor arrests (freezing) to periods of normal motor output. The results of a random
effects analysis revealed that these events were associated with a decreased blood oxygen level-dependent response in sensori-
motor regions and an increased response within frontoparietal cortical regions. These signal changes were inversely correlated with
the severity of clinical freezing of gait. Motor arrests were also associated with decreased blood oxygen level-dependent signal
bilaterally in the head of caudate nucleus, the thalamus and the globus pallidus internus. Utilizing a mixed event-related/block
design, we found that the decreased blood oxygen level-dependent response in the globus pallidus and the subthalamic nucleus
persisted even after controlling for the effects of cognitive load, a finding which supports the notion that paroxysmal increases in
basal ganglia outflow are associated with the freezing phenomenon. This method also revealed a decrease in the blood oxygen
level-dependent response within the mesencephalic locomotor region during motor arrests, the magnitude of which was positively
correlated with the severity of clinical freezing of gait. These results provide novel insights into the pathophysiology underlying
freezing of gait and lend support to models of freezing of gait that implicate dysfunction across coordinated neural networks.
Keywords: functional magnetic resonance imaging; freezing of gait; Parkinson’s disease; basal ganglia; subthalamic nucleus
Abbreviation: BOLD = blood oxygen level-dependent
Freezing of gait is a common symptom of Parkinson’s disease in
which patients experience the feeling that their feet become glued
to the floor whilst walking (Giladi et al., 2001). Although the
pathophysiology remains poorly understood (for reviews see
Nutt et al., 2011; Shine et al., 2011a), it is well recognized that
a number of specific triggers can either provoke freezing, such as
doi:10.1093/brain/awt049 Brain 2013: Page 1 of 12 |
Received November 11, 2012. Revised December 31, 2012. Accepted January 22, 2013
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the clinical ‘OFF’ state (Almeida et al., 2007), walking through a
narrow doorway (Almeida and Lebold, 2010) and dual-task walk-
ing (Hausdorff et al., 2005) or relieve freezing, such as visual and
auditory cues (Nieuwboer et al., 2007). In addition, freezing be-
haviour in Parkinson’s disease is not limited to gait, with a number
of studies showing that it also affects other domains, including
upper limb movements and speech (Giladi et al., 1992; Almeida
et al., 2002; Nieuwboer et al., 2009; Naismith and Lewis, 2010),
suggesting that the mechanism underlying freezing in Parkinson’s
disease is because of dysfunction across neural regions supporting
more general functions (Nutt et al., 2011; Shine et al., 2011a).
Identifying the neural correlates of freezing of gait has been
limited by the restrictions that accompany the neuroimaging of
gait per se (Bakker et al., 2007). Previous research has relied on
temporally insensitive techniques, such as PET, or on the combin-
ation of functional MRI with non-motor tasks such as imagined
walking paradigms (Jahn et al., 2004; Snijders et al., 2011) or
watching a first-person perspective video recording of an actor
walking (Wang et al., 2007). Alternatives to modelling gait in
the neuroimaging setting are seen in studies that have utilized
bimanual lower limb tasks that successfully stimulate the ongoing
interhemispheric coordination requisite for normal gait (Kapreli
et al., 2006, 2007). While by definition these tasks cannot meas-
ure some important variables in the production of gait such as
balance and gravity, they provide a unique insight into the prep-
aration and execution of bipedal motor tasks.
In spite of these limitations, much insight into freezing of gait
has been gained from neuroimaging techniques (Bartels and
Leenders, 2008). A number of studies of functional metabolism
and the role of specific modulatory neurotransmitters have pro-
posed that freezing of gait is likely to be because of dysfunction
within a distributed network of frontal and parietal cortical regions
(Wu and Hallett, 2005; Bartels et al., 2006; Bartels and Leenders,
2008; Wu and Hallett, 2008; Wu et al., 2010; Tessitore et al.,
2012a, b). Such a formulation is consistent with a number of
previously proposed freezing of gait hypotheses (Almeida et al.,
2005; Hallett, 2008; Lewis et al., 2009). In addition to these
corticostriatal networks, a recent functional MRI study using
motor imagery in a group of patients with freezing of gait has
shown preferential activation in localized areas of the brainstem
(such as the mesencephalic locomotor region), that have been
previously implicated in models of locomotor dysfunction (Jacobs
and Horak, 2007; Snijders et al., 2011).
Although much of the symptomatology observed in Parkinson’s
disease relates to the relative lack of dopamine in the basal gang-
lia, to our knowledge no neuroimaging study has specifically
explored the role of subcortical dysfunction in freezing of gait.
Indeed, subcortical regions are likely to play an important role in
the pathophysiology of freezing of gait, either through the striatal
integration of sensory and motor corticothalamic activity during
gait (Almeida et al., 2007), the effective switching of activity be-
tween competing yet complimentary neural networks (Lewis and
Barker, 2009; Naismith et al., 2010), the mediation of ongoing
activity within cortical regions such as the pre-supplementary
motor area (Jacobs et al., 2009) and/or by exerting top-down
control over the caudal brainstem structures controlling gait
(Jahn et al., 2004; Snijders et al., 2011).
To investigate the neural correlate of freezing behaviour, our
group has developed a novel (virtual reality) paradigm in which
subjects navigate a non-immersive, yet realistic 3D environment
using footpedals to control their ‘walking’. The virtual reality task
requires bipedal motor activity whilst processing cognitive and en-
vironmental information. Performance on this virtual reality task
has previously been correlated with self-reported freezing of gait
symptoms (Naismith and Lewis, 2010) and more recently, with the
severity of actual recorded episodes of freezing of gait (Shine
et al., 2012b). In addition, the task has recently been successfully
combined with functional MRI in a proof-of-concept study in a
single patient with freezing of gait (Shine et al., 2011b). Together,
these results suggest that the virtual reality task represents an
The current study utilized this virtual reality gait paradigm to
determine the specific cortical and subcortical neural correlates
associated with freezing behaviour in a group of patients with
Parkinson’s disease who experience significant freezing of gait
while in their clinical OFF state.
the freezingphenomenon in
Materials and methods
The 18 patients in this study were all males with idiopathic Parkinson’s
disease and an average age of 66.8 years (see Table 1 for further
demographics). All patients were assessed on section III of the
Unified Parkinson’s Disease Rating Scale (Goetz et al., 2007) immedi-
ately prior to scanning in their clinically defined OFF state, having
withdrawn from dopaminergic medications overnight. This ‘washout’
period may not be sufficient to exclude all of the effects from longer
actingmedications; however,onlythree patientswere taking
Table 1 Demographic, neuropsychiatric and virtual reality
n = 18
Hoehn and Yahr stage
FOG-Q (total score)
Disease duration (years)
Time frozen on timed up and go tasks (%)
Number of freezing
episodes during timed up and go
Virtual reality measures
Motor arrests, total
Motor arrests: low cognitive load block
Motor arrests: high cognitive load block
Motor arrests following indirect cues
Motor arrests following narrow doorways
UPDRS-III = Unified Parkinson’s Disease Rating Scale (Part III); FOG-Q = Freezing
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dopamine agonists. The University of Sydney Human Research and
Ethics Committee approved the study and written informed consent
was obtained from each patient. Patients were initially selected for the
study by answering positively to item three of the Freezing of Gait
Questionnaire [‘Do you feel that your feet get glued to the floor while
walking, making a turn or when trying to initiate walking (freezing)?’]
(Giladi et al., 2000). This question has previously been shown to be
a reliable screening toolfor
(Giladi et al., 2009).
Patients were confirmed as experiencing freezing of gait during the
performance of six timed up and go trials in which they were required
to make tight 180?turns to the left and right. Freezing of gait was
defined as having one or more episodes of foot movement cessation
during this brief assessment (Schaafsma et al., 2003). Similar to a
recent study (Shine et al., 2012a), each timed up and go trial was
video-recorded and analysed post hoc for the number and duration of
freezing episodes. In addition, the percentage of time spent frozen
during the timed up and go trials was also calculated for each patient
(Shine et al., 2012a), as this measure has been shown to represent a
more robust measure of freezing severity (Morris et al., 2012).
patients with freezingof gait
Virtual reality paradigm
A single 10-min virtual reality paradigm was performed in the scanner.
Patients were positioned so that they could clearly view the screen on
which the virtual reality task was displayed, with their feet resting on a
pair of magnetic resonance-compatible foot pedals (Shine et al.,
2011b). To complete the task, patients were required to navigate a
first-person view of a realistic 3D corridor with environmentally salient
features (such as doorways) by the use of the foot pedals that were
fixed to a board at the base of the MRI scanner. Forward progression
within the virtual reality environment was accomplished by the alter-
nate depression of left and right foot pedals at a rhythm consistent
with their normal gait (?2–4Hz). This action required that the patient
plantar flex the ankle of one foot ?30?below parallel, activating a
binary trigger mechanism, whilst performing simultaneous dorsiflexion
of the contralateral ankle. The use of foot pedals here, in contrast to
the use of hand buttons in earlier studies (Naismith and Lewis, 2010),
is in keeping with previous work that has reported that alternate step-
ping in place is a sensitive and specific method for capturing arrests in
motor output in freezers (Nantel et al., 2012). Navigation of the vir-
tual reality could only be achieved with alternating ‘physiological’ foot-
step sequences (i.e. left-right-left-right) and forward progression did
not occur during ‘out of sequence’ steps (i.e. left–left or right–right),
thus ensuring that movement through the virtual reality environment
was only associated with alternating left–right sequences. All foot
pedal responses were recorded for further analysis.
Walking and stopping in the virtual reality environment was initiated
by cue words that were displayed on the screen. These cue words
were arranged into alternating blocks that carried low or high cogni-
tive load (Fig. 1). In the low cognitive load blocks, patients were in-
structed to respond to ‘WALK’ cues that were displayed in green text,
hereafter referred to as ‘direct’ cues (Fig. 1). Direct cues to stop walk-
ing during these low cognitive load blocks were signaled by a ‘STOP’
cue that appeared in red text (Fig. 1).
Task difficulty was manipulated by introducing inter-leaved blocks of
high cognitive load, which contained ‘indirect’ cues for walking and
stopping. These indirect cues utilized colour-word pairings based upon
a modified version of the Stroop task (Stroop, 1935) (Fig. 1). In these
high cognitive load blocks, the direct ‘WALK’ and ‘STOP’ cues were
replaced with the presentation of either congruent (e.g. ‘RED’ written
in red; Fig. 1) or incongruent (e.g. ‘BLUE’ written in red) colour
word-pairings (i.e. ‘indirect’ cues). Prior to the experiment, patients
were taught that a congruent colour-word pairing either represented
a cue to ‘WALK’ or ‘STOP’. For example, if congruent colour-word pair-
ings represented ‘WALK’, then incongruent pairings represented ‘STOP’
and vice versa (Fig. 1). Conditions were randomly counterbalanced
across patients, such that the congruent colour-word pairings repre-
sented ‘WALK’ for half of the group and ‘STOP’ for the other half.
Patients were generally ‘active’ throughout the task walking through
the virtual reality environment and only stopped in response to a direct
or indirect stop cue. After stopping appropriately to a stop cue for
1.5s, a direct ‘WALK’ cue was presented after a delay of between 4
and 6s, informing the patient to begin ‘walking’ again. All cues were
presented for 1s in the bottom third of the screen at pseudorandom
intervals so that patients were unable to predict the cue-onset. The
virtual reality paradigm was programmed such that cues appeared
with a variable interval of between 5 and 40 steps (minimum 2s). If
a patient stopped inappropriately (either intentionally or otherwise) the
cue was re-presented on the screen after a delay of 3s. Similarly, if a
patient did not appropriately respond to a stop cue, the cue was
represented every 3s until the patient responded by ceasing foot
movements. Between 2 and 4 direct or indirect walk cues were pre-
sented prior to each direct or indirect stop cue and this pattern was
repeated an average of three times per block. Prior to scanning, all
participants were trained on the paradigm until they demonstrated
that they understood the rules (495% correct response to cue pres-
entations during 2min of practice). There were a total of 10 blocks in
Figure 1 The experimental paradigm. Patients used a set of
foot pedals to navigate a virtual corridor while lying supine in a 3
T MRI scanner. While stationary, the patient received a WALK
cue, at which time they commenced tapping the foot pedals
with alternate feet in a steady rhythm. Whilst walking, the pa-
tient was presented with a series of cues, which they interpreted
in order to continue walking (‘WALK’ cue) or to stop (‘STOP’
cue). There were two alternating blocks within the experiment: a
low cognitive load block, in which patients responded to direct
cues (e.g. WALK = the word ‘WALK’ written in the colour green
and STOP = the word ‘STOP’ written in the colour red); and a
high cognitive load block, in which patients responded to indir-
ect cues (e.g. WALK = congruent colour-word cues, such as the
word ‘RED’ written in the colour red; and STOP = incongruent
colour-word cues, such as the word ‘GREEN’ written in the
colour red). Patients were asked to interpret these cues and
determine whether to continue walking or to stop and await the
next cue based on a prelearned rule.
Neural correlates of freezing in Parkinson’s disease Brain 2013: Page 3 of 12 |
at University of Sydney on March 13, 2013
the experiment (five each of both low and high cognitive load), so that
each patient responded to an equal number of direct and indirect cues.
This meant that each patient performed a minimum of 800 foot pedal
responses in the virtual reality.
Definition of motor arrests:
As a primary outcome measure, we explored paroxysmal episodes of
normal footstep cessation despite the intention to walk. Based on pre-
vious methodology (Naismith and Lewis, 2010), we identified all
occasions when a patient suffered a motor arrest, which was defined
as a period in time when a patient suffered from an abnormally long
between-footstep latency (Fig. 2). To identify these epochs, we first
determined the modal footstep latency for each patient by determin-
ing the most frequent between-footstep latency (within bins of 0.1s)
occurring throughout the virtual reality paradigm. The modal footstep
latency was assumed to be a more robust measure of normal walking
cadence than the mean footstep latency, which could be skewed by
the presence of prolonged footstep latencies associated with motor
arrest. Any epochs greater than a threshold of twice the modal foot-
step latency were defined as a motor arrest. This measure of behav-
ioural freezing in the virtual reality has recently been correlated with
the frequency and duration of actual clinical freezing of gait
events suffered by patients whilst performing timed-up-and-go walk-
ing tasks (Shine et al., 2012b). The cessation of the motor arrest was
defined by the re-initiation of the normal walking pattern using the
Imaging was conducted on a General Electric 3T MRI. T2*-weighted
echo planar functional images were acquired in sequential order with
repetition time = 3s, echo time = 32ms, flip angle = 90?, 32 axial slices
covering the wholebrain, field
gap = 0.4mm, and raw voxel size = 3.9mm ? 3.9mm ? 4mm thick.
High-resolution 3D T1-weighted, anatomical images (voxel size
0.4 ? 0.4 ? 0.9mm) were obtained for coregistration with functional
of view = 220mm,interslice
Statistical parametric mapping software (SPM8, Wellcome Trust Centre
for Neuroimaging, London, UK, http://www.fil.ion.ucl.ac.uk/spm/soft-
ware/) was used for image processing and analysis. Functional images
were pre-processed according to a standard protocol: (i) scans were
slice-time corrected to the median (17th) slice in each scan; (ii) scans
were then realigned to create a mean realigned image and measures
of 6?of rigid head movements were calculated for later use in the
correction of minor head movements; (iii) images were normalized to
the echo planar image template; and (iv) scans were then smoothed
using an 8-mm full-width at half-maximum isotropic Gaussian kernel.
Due to the increased risk of head movements in this clinical popu-
lation, each trial was subsequently analysed using ArtRepair (Mazaika
et al., 2007) and trials with a large amount of global drift or
scan-to-scan head movements 41.5mm (i.e. approximately half the
size of the voxel collected in the echo planar images) were corrected
using the Interpolation method. This correction was provided to ensure
Figure 2 Explanation of the experimental paradigm and the methods employed to determine the onset time and durations of the
regressors used in the functional MRI models. The top section of the figure is an example of the timing onsets of the indirect cues as a
patient navigated a high cognitive load block in the experiment. The two lines below display the approximate onsets of each left and right
foot pedal depression. The section below the footstep lines depicts the different ‘coding’ categories used to define the pattern of footstep
response, including periods of effective ‘walking’, appropriate response to a STOP cue, motor arrests and arrests immediately following a
WALK cue, which were discarded from the final analyses. The bottom section contains a depiction of the selection of time points and
epochs for each regressor used in the functional MRI analyses: (a) epochs of motor arrest began at the last effective footstep prior to a
long latency and end at the next effective footstep; (b) epochs of effective ‘walking’ were sampled from periods of the paradigm when a
patient walked with a consistent ‘modal’ between-footstep latency without the presence of any motor arrests or WALK cues; and (c) the
entire high cognitive load block was estimated from the first WALK cue until the final STOP cue in the block. The epoch analysis estimated
the BOLD response differences between regressors (a) and (b) and the mixed analysis estimated the differences between regressors (a)
Brain 2013: Page 4 of 12 J. M. Shine et al.
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that regular head movements throughout the task did not lead to
spurious results; however, the sporadic nature of the motor arrests
meant that a systematic relationship between head movements and
the events of interest was extremely unlikely. Trials with 43mm or 3?
of scan-to-scan movement were considered an a priori exclusion cri-
terion however, no patients exceeded this threshold.
Individual first-level statistical parametric maps were calculated for
each subject using a general linear model analysis within an
epoch-related design (Grinband et al., 2006) in a fixed-effects analysis
using SPM8 software. The design matrix for each patient was created
by entering two regressors for each trial (Fig. 2): a regressor that
modelled the specific onset time and duration of a motor arrest (i.e.
the entire epoch of time between two successful footsteps that lasted
for greater than twice the modal footstep latency) and a regressor that
modelled a period of time when the patient had successfully com-
pleted a modal footstep, with no external cues (such as a WALK
cue or a doorway) or long latency footsteps within three ‘steps’ in
either direction. Contrast images from the first-level analyses were
then entered into a second-level random-effects design in order to
determine the group-level effects of the condition of interest. The
group-level brain maps were assessed at P50.001 uncorrected with
cluster size 410.
Mixed block-event analysis
A mixed model was utilized to determine the blood oxygen level-
dependent (BOLD) response pattern that was uniquely associated
with freezing behaviour (Visscher et al., 2003; Petersen and Dubis,
2012). This technique allows the parsing of sustained, task-related
effects from the transient patterns associated with the event of interest
(Laurienti et al., 2003). That is, the results of the analysis can test for
the presence of patterns of BOLD response that are specific to motor
arrests, rather than to the mechanism that may have triggered the
phenomenon (such as increased cognitive load in our experimental
design). These analytic techniques are well known to over-ascribe sig-
nificance to blocked regressors, as unmodelled variance in the
event-related time course might create a spurious ‘sustained’ signal
associated with the block regressor (Visscher et al., 2003; Petersen
and Dubis, 2012). Practically, this means that results that survive the
block correction are especially relevant to the event in question. To
achieve this aim, a regressor modelling the duration of each High
Cognitive Load block and a separate regressor modelling each motor
arrest epoch were entered into a new fixed-event analysis for each
patient (Fig. 2). A contrast image representing the direct statistical
comparison of motor arrests and the block effects was extracted
for each patient and placed into a random effects analysis at the
second level. Resultant brain maps from these analyses were inter-
preted at P50.001, uncorrected for multiple comparisons; how-
ever, witha cluster threshold
Cunningham, 2009). Due to the interrelated nature of the two
imaging analyses, we could not directly compare the results of
the two tests statistically.
Region of interest analysis
To investigate the specific a priori hypothesis from a previously
proposed model of freezing of gait (Lewis and Barker, 2009),
images from the first-level analysis were subsequently explored
using a predefined regions of interest analysis. To avoid introducing
bias, the coordinates of each region of interest were defined inde-
pendently from the whole brain analyses. Spherical regions of
interest were drawn in the following regions: the precentral sulcus,
dorsal premotor area and the dorsal caudal putamen for the motor
loop; the dorsolateral prefrontal cortex, posterior parietal cortex and
the head of caudate for the cognitive loop; and the anterior insula,
dorsal anterior cingulate cortex, medial prefrontal cortex and the
ventral striatum for the affective loop, the globus pallidus internus,
the subthalamic nucleus, the anterior thalamus and the mesenceph-
alic locomotor regions (see Fig. 3 and Table 3 for the model and
The coordinates for cortical regions of interest were defined using
the peak clusters of activity from the Brain-Maps database (brainma-
p.org; also see Smith et al., 2009) and were created using the WFU
Pickatlas template (fmri.wfubmc.edu/cms/software). The coordinates
for the subcortical regions of interest were defined based on a study
that traced basal ganglia regions of interest using an echo planar
image template similar to the template used to normalize the func-
tional scans in our study (Prodoehl et al., 2008). The coordinates for
the mesencephalic locomotor region of interest were taken from the
peak loci of activation from a recent paper that used imagined walking
to explore deficits in patients with freezing of gait (Snijders et al.,
2011). Care was taken to ensure that there was no overlap present
Figure 3 Model of frontostriatal loop function. Lines with
arrows denote excitatory (glutamatergic) input and lines with
spherical ends denote inhibitory (GABAergic) input. The cortical
regions within the cognitive loop [comprising the dorsolateral
prefrontal cortex (DLPFC) and posterior parietal cortex (PPC)],
the motor loop [comprising the precentral gyrus (Motor) and the
dorsal premotor area (dPMA)] and the Affective Loop [com-
prising the medial prefrontal cortex (mPFC) and the anterior
insulae (AI)] activate specific striatal nuclei [the head of caudate,
the putamen and the ventral striatum (Vent. Str.), respectively],
leading to the deactivation of the tonically-active globus pallidus
internus (GPi). This releases the inhibition on relay nuclei in the
thalamus (Thal) and the brainstem [including the mesencephalic
locomotor region (MLR)] allowing normal corticothalamic in-
formation processing and activation of central pattern gener-
ators (CPGs), respectively. Hubs within the frontal regions of the
cognitive loop also have direct connections with the subthalamic
nucleus (STN), allowing for a direct and timely increase in the
outflow of the globus pallidus internus, effectively decreasing
thalamic and brainstem signalling.
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between the individual regions of interest. All regions of interest were
defined in Montreal Neurological Index space.
The MarsBar toolbox in SPM8 (Brett et al., 2002) was used to ex-
tract contrast values for each region of interest for each contrast.
These values were then imported into Statistical Package for the
Social Sciences software version 19 (SPSS Inc.) for group-level statis-
tical testing. A two-sided paired sampled t-test was performed to de-
termine whether each region of interest was significantly associated
with a positive or negative contrast value in the motor arrest contrast
when compared with the walking contrast. In addition, a similar ana-
lysis was used to compare the motor arrest contrast with the block
effects in the mixed block-event analysis. Alpha levels were set to 0.05
and P-values were corrected for multiple comparisons using a family
wise error correction. Finally, contrast values from each of the region
of interest analyses were compared with the results from the clinical
timed up and go tests using bivariate Spearman’s rank-order correl-
ations. Alpha levels were treated in a similar fashion to those from the
rest of the region of interest analysis.
All patients in the study suffered from freezing episodes during
both the timed up and go tests (16.0 ? 4.2) and the virtual reality
paradigm (55.7 ? 40.1). As such, all patients in the study
self-reported freezing behaviour and were also observed to experi-
ence freezing clinically. A greater number of events occurred
during high cognitive load blocks (32.4 ? 22.3) than during low
cognitive load blocks (23.3 ? 27.5), with 58% of the events
occurring in the high cognitive load blocks (t = 2.3; P50.05).
Table 1 contains further behavioural results from the virtual reality
Epoch design analysis: motor arrests versus
When comparing the BOLD response between motor arrests and
walking effectively, significant activation was observed in the
bilateral posterior parietal cortex, the dorsolateral prefrontal
cortex bilaterally and the ventrolateral prefrontal cortices along
with the bilateral dorsal anterior cingulate regions and the bilateral
anterior insula (see Table 2 for peak voxel co-ordinates and
T-values and Fig. 4). In addition, there was a significant decrease
in BOLD response in the bilateral sensorimotor regions, along with
the head of caudate bilaterally.
Epoch design analysis: region of interest analysis
During the contrast comparing motor arrests and walking, there
was a significant reduction in the BOLD response observed in the
motor region of interest and the bilateral putamen of the motor
loop; however, there was only a trend towards a decreased BOLD
response in the right dorsal premotor area. In the cognitive loop,
there was a significant increase in the dorsolateral prefrontal
cortex and the posterior parietal cortex, with the right dorsolateral
prefrontal cortex showing a strong inverse correlation with the
severity offreezing behaviour
P = 0.008).
In contrast there was a significant decrease in activation within
the head of caudate nuclei. In the limbic loop, only the medial
prefrontal cortex, the left anterior insula and the left ventral stri-
atum showed significantly decreased activation. There was an
additional decrease in the BOLD response within the bilateral
globus pallidus internus and the bilateral anterior thalamus.
There was also a significant decrease seen in the bilateral
(Spearman’srho = -0.600,
Table 2 Brain regions displaying decreased BOLD response in the epoch analysis
Neural regionHemispherexyz Cluster
Head of caudate§
Dorsolateral prefrontal cortex§
Posterior parietal cortex§
MNI coordinates for neural regions that displayed decreased BOLD response in the epoch analysis. The coordinates represent the peak
voxel within a cluster that was present above the statistical threshold in the whole-brain analysis. T-statistics are presented for clusters with
P50.001 and 410 contiguous voxels.
§Denotes a member of the putative cognitive control network (Cole and Schneider, 2007).
*The anterior insula can also be viewed as a member of the Salience Network (Seeley et al., 2007).
Brain 2013: Page 6 of 12 J. M. Shine et al.
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subthalamic nucleus and a trend for an increase in the bilateral
mesencephalic locomotor region.
Mixed block-event design analysis: region of interest
When controlling for the activation that was related to the
increased cognitive load in the blocks with the indirect walk and
stop cues, no clusters survived in either contrast at P50.001 in a
global brain analysis. However, the region of interest analysis
revealed that motor arrests were associated with a significant
decrease in the BOLD response in the right posterior parietal
cortex, the left globus pallidus internus, the subthalamic nucleus
bilaterally along with a significant decrease in BOLD response
within the mesencephalic locomotor region, bilaterally. The
BOLD response within the left mesencephalic locomotor region
region was also strongly correlated with an increase in the severity
of freezing behaviour (rho = 0.616, P = 0.008).
The results presented here demonstrate the BOLD correlates of
motor arrests that were provoked by a virtual reality gait task
and have recently been shown to correlate with actual episodes
of freezing of gait (Shine et al., 2012b). During these motor
arrests, there was a significant increase in the BOLD response in
the bilateral dorsolateral prefrontal cortex, posterior parietal cor-
tices and anterior insulae and a concomitant decrease in BOLD
response within the bilateral sensorimotor cortices (Fig. 4). In
addition, a significant decrease in BOLD response was seen in a
number of subcortical nuclei, including the bilateral caudate head,
the anterior thalamus, the globus pallidus internus and the sub-
thalamic nucleus. These findings are consistent with a recent
review of the neuroimaging literature, which concluded that freez-
ing behaviour in Parkinson’s disease was likely to be because of
impaired processing in frontal and parietal regions (Bartels, 2008).
After correction for multiple comparisons, there was a significant
relative decrease in the BOLD response within the motor cortex
during motor arrests when compared with effective walking. As
the two contrasts both used one effective footstep, the relative
decrease in BOLD response seen in our experiment is unlikely to
be due to a simple motor mismatch but rather to the relative
inability to recruit neural activity in the cortical regions that are
responsible for the movement of the lower limbs (Shayoun et al.,
2004; Wang et al., 2007). The lack of a significant decrease in the
activation within the motor cortex in the mixed analysis, is likely
to be due to the nature of the virtual reality task, which contains
a large number of periods in which the subject must ‘STOP’, ef-
fectively negating the activity within motor regions during each
The prefrontal and parietal cortical regions with increased rela-
tive BOLD signal during motor arrests are well known to mediate
a number of executive functions (Spreng et al., 2010) as well as
affordance-based responses (Riddoch et al., 2006), both of which
are well-aligned with the literature in freezing of gait (Nutt et al.,
2011; Shine et al., 2011a). Indeed, these regions, including the
dorsolateral prefrontal and posterior parietal cortices, as well as the
head of caudate nucleus, have previously been shown to
co-activate as a functional network, described as the cognitive
control network (Coleand Schneider,
Activation Ensemble (Seeley et al., 2007). Interestingly, this net-
work is presumed to be responsible for the more domain-general
2007) or theTask
Table 3 Results from the region of interest analysis for the epoch design and the mixed block-event design
Regionxyz Size (mm)EpochMixed
Dorsal premotor area
Dorsolateral prefrontal cortex§
Posterior parietal cortex§
Head of caudate
Medial prefrontal cortex
Mesencephalic locomotor region†
MNI coordinates for neural regions that displayed decreased BOLD response in the epoch and the mixed block-event analyses. The coordinates
represent the peak voxel within a cluster that was present above the statistical threshold in the whole-brain analysis. The values in the epoch and
mixed columns reflect the average contrast value difference from the region of interest analysis for the left and right region, respectively.
Significance levels: ns = not significant;nsP40.1;#P50.1; *P50.05; **P50.001; ***P50.0001.
§negative correlation with the severity of clinical freezing of gait in the Epoch analysis.
†Positive correlation with the severity of clinical freezing of gait in the Mixed analysis.
Neural correlates of freezing in Parkinson’s disease Brain 2013: Page 7 of 12 |
at University of Sydney on March 13, 2013
role of the processing of novel information, rather than merely
activating during tasks requiring increased cognitive demand
(Cole and Schneider, 2007).
While regions within the cognitive control network have been
previously implicated in freezing of gait (Bartels and Leenders,
2008; Shine et al., 2011a), it is not yet clear what role these
cortical hubs play in the freezing phenomenon. The increased
BOLD signal within the cognitive control network during motor
arrests may represent a compensatory adaptation with the recruit-
ment of regions not typically involved in ‘effective walking’. This
observation is consistent with a number of hypotheses regarding
the pathophysiology underlying freezing of gait, particularly those
that highlight the complex interplay between these anatomically
distinct yet functionally connected regions (Strauss and Sherman,
2006; Jacobs and Horak, 2007; Hallett, 2008; Lewis and Barker,
2009). It has also previously been proposed that ‘breaking a
freeze’ relies on the generation of a goal-directed behaviour
(Lewis and Barker, 2009) and thus activation of the cognitive con-
trol network may represent the recruitment of regions that are
attempting to focus behaviour in order to overcome a freezing
episode. This interpretation is consistent with the demonstration
of a strong inverse relationship between clinical freezing of gait
and cognitive control network activation during motor arrests in
the virtual reality task. Therefore, although the cognitive control
network appears to play a supportive role in freezing of gait, pa-
tients with severe freezing of gait are unable to effectively recruit
activity within this network, predisposing their brains to freezing
In view of the strong link between cognitive impairment and
freezing of gait (Hausdorff et al., 2005), it is possible that the
increased BOLD response seen in the cognitive control network
may be simply reflective of the neural activity required to com-
plete the cognitively complex portions of the virtual reality task.
Indeed, this network of neural regions has previously been shown
to co-activate in response to dual-task performance in patients
with Parkinson’s disease (Wu and Hallett, 2008). However,
whilst motor arrests were more likely to occur during the experi-
mental blocks with additional cognitive load, any events occurring
within three steps of an indirect cue were removed from the ana-
lysis. Thus an alternative viewpoint is that dual-task performance
and freezing of gait may share a similar neural substrate. As such,
freezing of gait may reflect the transient ‘overload’ of the infor-
mation processing capacity of this neural network, leading to a
breakdown in motoric function. Though speculative, this interpret-
ation is supported by a wealth of data from experiments spanning
neuropsychological (Amboni et al., 2007; Naismith et al., 2010;
Shine et al., 2012a), clinical (Hausdorff et al., 2005) and imaging
modalities (Bartels et al., 2008).
Regions within the cognitive control network are known to
co-activate with other neural networks to mediate goal-directed
behaviour (Spreng et al., 2010) and recent research has implicated
the basal ganglia in the mediation of these capacities (Bartels
et al., 2006; Redgrave et al., 2010). Indeed, when compared
with walking, periods of motor arrest were associated with a sig-
nificant decrease in the BOLD in the bilateral head of caudate
nucleus, the bilateral putamen and the right ventral striatum,
along with bilateral globus pallidus internus. These regions have
previously been shown to mediate the shift between competing
neural networks (Kimura et al., 2004; Robbins et al., 2007), thus
the decreased activation seen during motor arrests may represent
a transient functional disconnection between subcortical and cor-
tical regions (Jacobs et al., 2009; Lewis and Barker, 2009) thereby
impairing information transfer between the neural hubs critical for
the functional integrity of the neural networks.
Previously, it has been proposed that paroxysmal over-activity in
the globus pallidus internus causes freezing of gait through inhib-
ition of the thalami and the brainstem structures controlling gait
(Lewis and Barker, 2009). This model hypothesizes that freezing of
gait is due to an abrupt synchronization in the neuronal firing
pattern within the basal ganglia outflow circuitry, which is trig-
gered by complementary yet competing neural inputs. Therefore,
the reduction in BOLD signal seen in the globus pallidus internus
(the major outflow nucleus of the basal ganglia) may reflect a
decrease in energy requirement due to the involvement of the
globus pallidus internus in low frequency (525Hz) synchronized
oscillatory circuits, activity that is negatively correlated with the
BOLD signal (Zumer et al., 2010). This interpretation is consistent
with the known function of the globus pallidus internus, which
exists in a low-energy oscillatory state unless directly inhibited
Figure 4 Comparison of BOLD activation and deactivation
patterns during the contrast of the motor arrests and ‘walking’.
(A) Increased BOLD in the left dorsolateral prefrontal cortex and
posterior parietal cortex with concomitant decrease in the sen-
sorimotor cortices; (B) bilateral decreased BOLD in the caudate
nuclei with increased BOLD in the bilateral insula and left
dorsolateral prefrontal cortex; and (C) decreased BOLD in the
sensorimotor cortices. Statistical maps were created with a
voxel-level of P50.001 and a cluster threshold of 10 voxels.
One cluster in the mesial precentral sulcus survived multiple
comparisons correction at P50.05 using the theory of Gaussian
Brain 2013: Page 8 of 12J. M. Shine et al.
at University of Sydney on March 13, 2013
by GABAergic input from the striatum (Frank, 2006). By this
means, the lower relative BOLD response in the globus pallidus
internus may in fact reflect an increase in oscillatory neural activ-
ity, a state that has far lower energy requirements than asyn-
chronous neural activity (Buzsaki and Draguhn, 2004).
The only subcortical regions that were significantly decreased
during the mixed block-event analysis were the left globus pallidus
internus and the bilateral subthalamic nucleus. Previous studies
have demonstrated that the subthalamic nucleus is involved in
the production of tonic oscillatory behaviour (Magill et al., 2001;
Bevan et al., 2002) and as such, the BOLD response within the
subthalamic nucleus likely follows a similar energetic pattern to
that seen in the globus pallidus internus (Timmerman et al.,
2007). Long presumed to be a hub within the ‘indirect’ pathway
of the basal ganglia, the subthalamic nucleus has recently been
shown to communicate with the pre-supplementary motor area
(and other regions of the cortex) in the ‘hyper-direct’ pathway
of the basal ganglia (Aron and Poldrack, 2006; Frank, 2006;
Miller, 2008; Wiecki and Frank, 2010). While the exact functions
of this pathway remain under investigation, it is clear that the
subthalamic nucleus heavily influences response selection in both
motor and cognitive tasks by effectively accelerating the activity
within the globus pallidus internus, and thus inhibiting the down-
stream targets of the basal ganglia (Frank, 2006). As the
pre-supplementary motor area was one of the only major hubs
of the cognitive control network not activated in the epoch
design, freezing may be due in part to impaired cortical commu-
nication within the hyper-direct pathway, ultimately leading to
increased subthalamic nucleus firing and a subsequent increase
in the inhibitory output of the basal ganglia. This interpretation
is consistent with elements of current clinical practice, as deep
brain stimulation surgery often targets the subthalamic nucleus
(Modolo and Beuter, 2009), leading to improvements in freezing
behaviour (Davis et al., 2006; Moreau et al., 2008). While this has
yet to be confirmed, the results here suggest that freezing behav-
iour may be due to an overwhelming increase in the inhibitory
output of the basal ganglia, leading to a paroxysmal decrease in
firing within the efferent targets of the globus pallidus internus,
such as the thalamic relay nuclei and the brainstem structures
controlling gait, including the mesencephalic locomotor region
The results from the mixed design also help to make sense of
the heretofore poorly understood finding of lower limb oscillatory
activity in the 5–7Hz range during episodes of freezing of gait
(Moore et al., 2008). The classic parkinsonian tremor occurs in
this same frequency range and studies using computational mod-
elling have shown that the tremor can be explained mechanistic-
ally by emergent rhythmic activity between the subthalamic
nucleus and the globus pallidus externus in the presence of a
dopaminergically depleted basal ganglia (Frank, 2006). As our ex-
periments revealed subcortical BOLD changes consistent with
those proposed in the models of tremor, freezing behaviour may
therefore be due to the paroxysmal occurrence of a similar mech-
anism. However, the emergent oscillatory pattern of rhythmic
basal ganglia outflow would occur transiently during motor func-
tion rather than at rest, as is the case with the traditional tremor
of Parkinson’s disease. This interpretation is consistent with the
finding that tremor and freezing are commonly present in separ-
ate parkinsonian phenotypes (tremor dominant and akinetic
rigid, respectively; Lewis et al., 2005) and rarely co-occur in indi-
vidual patients until advanced disease states. This suggests that
the phenotypic differences within Parkinson’s disease may be
explained by thespecificneural
whether the basal ganglia circuits oscillate at rest (patients with
Figure 5 A graphical representation of the proposed mechanism underlying freezing behaviour. Lines with arrows denote excitatory
(glutamatergic) input and lines with spherical ends denote inhibitory (GABAergic) input. In the healthy basal ganglia system (left), cortical
input to the striatum leads to disinhibition of the thalamus (Thal) and the mesencephalic locomotor region (MLR), leading to efficient
corticothalamic processing and normal motor output. In the presence of a dopaminergically-depleted basal ganglia (right), an over-
whelming increase in cortical processing causes a transient overload of the striatum, leading to a loss of its inhibition over the globus
pallidus internus (GPi). The globus pallidus internus, along with the subthalamic nucleus (STN), then begins firing in a low energy state
secondary to synchronized oscillations, leading to overwhelming inhibition of the thalamic relay nuclei and the mesencephalic locomotor
region, leading to cessation of motor output and ‘freezing’. The increased activation within the subthalamic nucleus may be due to a
decrease in communication within the hyper-direct pathway of the basal ganglia.
Neural correlates of freezing in Parkinson’s disease Brain 2013: Page 9 of 12 |
at University of Sydney on March 13, 2013
tremor-dominant disease) or with motoric output (patients with
freezing of gait). Further work will be required to elucidate the
validity of this interpretation.
Interestingly, when accounting for the effects of the high cog-
nitive load in the experiment, the heads of caudate nuclei were
not significantly decreased, as was seen during the epoch-related
design. This result may be due to the ongoing involvement of the
caudate nuclei in the ‘switching’ between attentional sets (Kimura
et al., 2004), a skill that is constantly probed by the virtual reality
task (Naismith and Lewis, 2010). It is also possible that the relative
lack of decreased BOLD response in the caudate nuclei is reflective
of the ‘breaking’ of a freezing episode, which has been hypothe-
sized to involve the reassociation of the striatum with the cortical
structures from which they became disconnected during freezing
(Lewis and Barker, 2009). Indeed, these results highlight an im-
portant caveat associated with functional MRI analyses, as the
nature of the BOLD response enforces temporal smoothing (on
the order of 5-6s) on time-series data. As such, although this
experiment helps to identify the pattern of disturbances associated
with freezing behaviour in Parkinson’s disease, it is not possible to
identify the temporal sequence that gives rise to freezing events.
Techniques such as dynamic causal modelling (Friston et al.,
2003), seed-based functional connectivity analysis (Fox et al.,
2005) or direct neuronal monitoring from deep brain electrodes
(Brown and Williams, 2005) may help to address these issues.
Alternatively, approaches with a higher temporal resolution and
the capacity to analyse oscillatory waveforms (such as electroen-
cephalography or magnetoencephalography) would potentially
assist in establishing the temporal sequence of neural events asso-
ciated with freezing behaviour.
There were no significant differences observed in the mesen-
cephalic locomotor region in the contrast between effective walk-
ing and freezing, however we did see a significant decrease in the
BOLD signal within the mesencephalic locomotor region during
the mixed block-event analysis, suggesting that the mesencephalic
locomotor region is actively involved in the mechanism underlying
freezing of gait. These results are in contrast with a previous
neuroimaging study, which used an imagined walking paradigm
to explore the pathological basis of freezing of gait and found that
patients with the disorder were more likely to show activation in
the mesencephalic locomotor region of the brainstem rather than
decreased activity (Snidjers et al., 2011). The increased activity in
that study was taken to reflect a compensatory mechanism, with
the mesencephalic locomotor region increasing its firing rate to
offset the relative lack of cortically driven gait. The results of
our study suggest that the mesencephalic locomotor region was
less active during freezing as may be expected in the setting of
overwhelming inhibition from the globus pallidus internus (Lewis
and Barker, 2009). However the degree of activation within the
left mesencephalic locomotor region was strongly correlated with
the severity of clinical freezing of gait, suggesting that in patients
with worse freezing of gait, the mesencephalic locomotor region
may indeed be playing a compensatory role during freezing. The
differing results of the two studies may be explained by the fact
that tasks using motor imagery may be unable to accurately model
complex motor tasks as evidenced by a study showing significant
differences between characteristics of perceived and actual
walking in patients with Parkinson’s disease with freezing of gait
(Cohen et al., 2011). Indeed, while the virtual reality task em-
ployed in this study does not accurately model gait in its entirety,
it is an ecologically valid bipedal task shown to provoke freezing
behaviour in a cohort of susceptible patients (Naismith and Lewis,
2010; Shine et al., 2012a). Therefore, the virtual reality task can
be considered an effective task for robustly probing the neural
correlates of freezing of gait in Parkinson’s disease.
The findings presented here suggest that the combination of vir-
tual reality and functional MRI has the potential to elucidate the
neural correlates underlying the freezing phenomenon, which can
ultimately manifest as freezing behaviour in a number of distinct
activities aside from gait. Using functional imaging methods, we
were able to provide evidence that freezing behaviour in
Parkinson’s disease is associated with increased basal ganglia in-
hibitory output, leading to a decrease in thalamic and brainstem
information processing. To our knowledge, this is the first study to
show BOLD response changes in a task that is both ecologically
valid and related to actual clinical freezing of gait. Further studies
will explore the specific roles of the multiple neural regions pre-
ceding and during an episode of freezing in order to better under-
stand the specific neural correlates of the freezing phenomenon as
they occur in real time.
We would like to thank Matthew Brett for his assistance with the
region of interest analysis using MarsBar. We would also like to
thank Dr Nathan Spreng for his help with the implementation of
Caret software and Mr Josh Roberts and Prof Ulrich Schall for their
critical review of the manuscript. We would like to thank Mr.
Simon Wadsworth of Turambar Software for his assistance in
developing the Virtual Reality paradigm. We would finally like to
thank our patient volunteers for their participation in this study.
The study was funded in it’s entirety by the Michael J.
Fox Foundation (http://www.michaeljfox.com/foundation/grant-
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