Accounting for movement increases sensitivity in detecting brain activity in Parkinson's disease.

Abstract

Parkinson's disease (PD) is manifested by motor impairment, which may impede the ability to accurately perform motor tasks during functional magnetic resonance imaging (fMRI). Both temporal and amplitude deviations of movement performance affect the blood oxygenation level-dependent (BOLD) response. We present a general approach for assessing PD patients' movement control employing simultaneously recorded fMRI time series and behavioral data of the patients' kinematics using MR-compatible gloves. Twelve male patients with advanced PD were examined with fMRI at 1.5T during epoch-based visually paced finger tapping. MR-compatible gloves were utilized online to quantify motor outcome in two conditions with or without dopaminergic medication. Modeling of individual-level brain activity included (i) a predictor consisting of a condition-specific, constant-amplitude boxcar function convolved with the canonical hemodynamic response function (HRF) as commonly used in fMRI statistics (standard model), or (ii) a custom-made predictor computed from glove time series convolved with the HRF (kinematic model). Factorial statistics yielded a parametric map for each modeling technique, showing the medication effect on the group level. Patients showed bilateral response to levodopa in putamen and globus pallidus during the motor experiment. Interestingly, kinematic modeling produced significantly higher activation in terms of both the extent and amplitude of activity. Our results appear to account for movement performance in fMRI motor experiments with PD and increase sensitivity in detecting brain response to levodopa. We strongly advocate quantitatively controlling for motor performance to reach more reliable and robust analyses in fMRI with PD patients.

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Accounting for Movement Increases Sensitivity in
Detecting Brain Activity in Parkinson’s Disease
Sˇtefan Holiga1*, Harald E. Mo ¨ller1, Toma ´s ˇ Sieger2,3, Matthias L. Schroeter1,4., Robert Jech2.,
Karsten Mueller1.
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Department of Neurology and Center of Clinical Neuroscience, Charles University in
Prague, Prague, Czech Republic, 3Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic, 4Clinic for Cognitive Neurology and Leipzig
Research Center for Civilization Diseases, University of Leipzig, and FTLD Consortium, Leipzig, Germany
Abstract
Parkinson’s disease (PD) is manifested by motor impairment, which may impede the ability to accurately perform motor
tasks during functional magnetic resonance imaging (fMRI). Both temporal and amplitude deviations of movement
performance affect the blood oxygenation level-dependent (BOLD) response. We present a general approach for assessing
PD patients’ movement control employing simultaneously recorded fMRI time series and behavioral data of the patients’
kinematics using MR-compatible gloves. Twelve male patients with advanced PD were examined with fMRI at 1.5T during
epoch-based visually paced finger tapping. MR-compatible gloves were utilized online to quantify motor outcome in two
conditions with or without dopaminergic medication. Modeling of individual-level brain activity included (i) a predictor
consisting of a condition-specific, constant-amplitude boxcar function convolved with the canonical hemodynamic
response function (HRF) as commonly used in fMRI statistics (standard model), or (ii) a custom-made predictor computed
from glove time series convolved with the HRF (kinematic model). Factorial statistics yielded a parametric map for each
modeling technique, showing the medication effect on the group level. Patients showed bilateral response to levodopa in
putamen and globus pallidus during the motor experiment. Interestingly, kinematic modeling produced significantly higher
activation in terms of both the extent and amplitude of activity. Our results appear to account for movement performance
in fMRI motor experiments with PD and increase sensitivity in detecting brain response to levodopa. We strongly advocate
quantitatively controlling for motor performance to reach more reliable and robust analyses in fMRI with PD patients.
Citation: Holiga Sˇ, Mo ¨ller HE, Sieger T, Schroeter ML, Jech R, et al. (2012) Accounting for Movement Increases Sensitivity in Detecting Brain Activity in Parkinson’s
Disease. PLoS ONE 7(5): e36271. doi:10.1371/journal.pone.0036271
Editor: Natasha M. Maurits, University Medical Center Groningen UMCG, The Netherlands
Received November 18, 2011; Accepted April 3, 2012; Published May 1, 2012
Copyright: ? 2012 Holiga et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Czech Science Foundation: grant project 309/09/1145, by the Czech Ministry of Education: research project MSˇM
0021620849, by the research project PRVOUK-P26/LF1/4 and by the Czech Technical University in Prague: grant project SGS10/279/OHK3/3T/13. Sˇtefan Holiga is
supported by a stipend from the International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity (IMPRS
NeuroCom). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: holiga@cbs.mpg.de
. These authors contributed equally to this work.
Introduction
Parkinson’s disease (PD) is a progressive neurodegenerative
disorder causing basal ganglia (BG) dysfunction [1]. It is
characterized by a large number of motor and non-motor deficits,
which significantly contribute to reduced quality of life. Despite
the definition of the broad spectrum of clinical characteristics and
criteria for diagnostics [2], mechanisms triggering illness, the
nature of its progression, and the character of therapeutic effects
are still a matter of debate [1,3–6]. Motor symptoms are key
features of clinical criteria and are essential for the diagnosis of PD
and its differentiation from related disorders. Bradykinesia,
tremor, rigidity, and postural instability are regarded as cardinal
symptoms and are associated with difficulties with planning,
initiating and executing movements, performing sequential and
simultaneous tasks, progressively reduced magnitude of sequential
movements, involuntary choreatic and dystonic movements,
hesitation in initiation, or finishing voluntary movements [2].
For the last two decades, positron emission tomography (PET)
and functional magnetic resonance imaging (fMRI) have been
used to investigate the neural substrates of motor deficits in PD
[7,8]. More recently, fMRI studies have tended to outnumber
PET studies due to their advantages of greater temporal and
spatial resolution and non-invasiveness [8]. Frequently used block-
design paradigms primarily utilize the upper limbs, in particular,
various hand and finger movement sequences, to investigate the
neural basis of PD patients’ motor performance. This is motivated
by a higher degree in limitations of potential movement
complexity and larger hand cortical representation [9]. Prevalent
tasks in investigating the brain motor circuitry in PD are sequential
finger movements, which are also part of the widely used Unified
PD Rating Scale (UPDRS) [10]. This clinical scoring system rates
the symptomatic severity of the disease. It is easily accessible and
suitable for providing a direct comparison between subjects or use
in longitudinal studies.
To assess the correctness of task execution and confirm
comparable performance among PD participants, previous imag-
ing studies have used push buttons [11–13], video-camera
recordings [14–16], observers/raters [17], custom-built systems
[18], or no specific arrangements [19]. Prior training sessions have
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also been employed [15,17,20–23] to practice the task and obtain
adequate performance. A recent study [24] assessed the motor
outcome of PD patients quantitatively using electromyography
(EMG) and fMRI. In particular, measured fluctuations of the
tremor amplitude were used to identify tremor-related brain
activity. However, no study has explicitly considered the effect of
accounting for the motor outcome on the sensitivity in detecting
task-related brain activity in fMRI experiments with PD patients
so far.
The analysis and interpretation of task-dependent fMRI data is
highly dependent on the choice of the hemodynamic response
(HR) model. Hemodynamic timing variability limits the interpre-
tation of fMRI data because of its relatively rapid time scale
ranging from milliseconds to seconds [25]. Accurate modeling of
motor-related brain responses in fMRI investigations therefore
relies on the experimental timing to a great extent. Another
important characteristic is the movement amplitude. Large-
amplitude movements seem to lead to an increased blood-
oxygenation level dependent (BOLD) response in several brain
regions as compared to small-amplitude movements [26]. Other
factors influencing the HR include force [27], reaction time [28],
and movement rate [29,30]. Due to this high sensitivity of the
BOLD response, modeling of fMRI data should ideally consider
all potential factors affecting the shape or timing. The fine-grained
nature of the BOLD signal is further underlined by its relatively
tenuous changes of ,1% in BG [31], regions that are
predominantly affected by PD. In investigations of PD subjects,
this becomes even more apparent due to motion variability
because of bradykinesia and hypokinesa, impaired initiation of the
movements after internal or sensory cues, frequent hesitation or
freezing of the movements, resting tremor and dyskinesias while
performing motor tasks. The motor abnormalities in PD are not
always easily differentiable and may even manifest as a mixture of
several symptoms with a great degree of variability between
patients. Unfortunately, all this may compromise the correct
interpretation of the functional imaging of motor tasks in general.
In investigations of therapeutic effects, for example medication or
deep brain stimulation, requiring repeated fMRI sessions and
where the motor outcome of participants may even dramatically
change between the sessions intra-individually, experimental
accuracy and reproducibility is a critical issue. Therefore, it is
impossible to achieve favorable accuracy in modeling the BOLD
response without explicitly and quantitatively assessing partici-
pants’ motor outcomes. Without such knowledge, statistical tests
relying on the standard BOLD model will be degraded by
inappropriate estimates of partial regression coefficients, ß, and
thus result in biased, unreliable, and potentially invalid conclu-
sions. The goals of the current study were to confirm this
statement and present a robust solution to problems arising from
it. To emphasize the possibility of within-subject, but also
between-subject, task-related deviations caused by the broad
motor heterogeneity of the disease, we designed the study with
repeated measurement sessions, by employing experimental
manipulation (levodopa medication) having a radical, and more
importantly, individual task-dependent effect on the response of
the participants. With this experimental setup we hypothesized
that in contrast to generic fMRI statistics, accounting for
deviations in task execution quantitatively in BOLD modeling
improves the accuracy of detecting individual motor activity by
reducing ‘type II’ errors. Consequently, this should result in
increased sensitivity of detecting the patients’ brain responses to
levodopa medication on the group level.
Several attempts to record kinematic information on-line during
motor tasks for consideration in hemodynamic modeling have
already been proposed. Most prevalent and established techniques
employ simultaneous fMRI and EMG recordings in healthy
subjects to validate brain activation by their relation to the EMG
recordings [32–35]. An optoelectronic motion capture system
monitoring a stroke patient [36] or a custom-built sensing system
[37] have also been utilized in clinical studies. Recently,
instrumented, MRI-compatible gloves for capturing movements
during fMRI investigations have been introduced. Specifically,
gloves were used in investigations of correlates of finger
movements and brain activity [38] or for qualitative control in a
study in stroke patients [39]. In the current work, the gloves were
employed during simultaneous recordings of fMRI and kinematics
in serial investigations of PD patients to evaluate potential
improvements in the statistical data analysis.
Materials and Methods
Patients
Twelve right-handed male patients with advanced akinetic-rigid
type of PD (Hoehn-Yahr stages II–III, 45–64 years of age) [40]
were recruited for this study. As basis for the diagnosis, the UK PD
Society Brain Bank Criteria [41] were used. All patients included
in the study met the criteria. Each of them gave written informed
consent prior to participation in accordance with the declaration
of Helsinki. Ethics Committee of the General University Hospital
in Prague, Czech Republic approved the protocol of the study.
Severity of patients’ motor symptoms was clinically assessed using
the motor examination (part III) of the UPDRS. UPDRS-III score
sheets were used to evaluate hemibody scores comprising
information about dominant lateral involvement of PD by
summing rigidity (sum of item 22), akinesia (sum of items 19,
23–26, 31), and tremor (sum of items 20, 21) for each hemibody
separately [10,42]. Patients’ clinical and demographic character-
istics are summarized in Table 1. For detailed individual
information see Table S1.
Patients were measured in two conditions, once after overnight
withdrawal of levodopa (‘levodopa OFF’ condition) and once one
hour after administration of 250 mg of levodopa/25 mg carbido-
pa (‘levodopa ON’ condition) (Isicom 250, Desitin Arzneimittel,
Hamburg, Germany). Any other anti-parkinson’s medication
(dopamine agonists, selegiline, amantadine, anticholinergics) was
not administered for four days before the medication-free
condition measurement.
Table 1. Demographic and clinical summary of studied
patients (N=12).
Characteristic Mean (SD)Range
Age (years)56.0 (7.0)45–64
Gender (M/F) 12/0-
Disease duration (years)12.4 (2.0)9–15
Levodopa treatment duration
(years)
9.3 (3.0)5–13
Motor complications duration
(years)
5.0 (3.0)2–12
UPDRS*-III: levodopa OFF 33.5 (9.0)20.5–47.0
UPDRS*-III: levodopa ON9.6 (4.0) 1.5–20.5
MMSE{
28.9 (1.0)28–30
UPDRS* - Unified Parkinson’s Disease Rating Scale. MMSE{- Mini Mental State
Examination.
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MRI data acquisition
Functional imaging was performed on a 1.5T MAGNETOM
Symphony scanner (Siemens, Erlangen, Germany) using a
birdcage head coil. A T2*-weighted gradient-echo echo-planar
imaging (EPI) sequence (flip angle 90u; repetition time, TR=1 s;
echo time, TE=54 ms) was used for BOLD fMRI. Ten oblique
slices (thickness 3 mm; 1-mm slice separation; nominal in-plane
resolution 363 mm2) were acquired. The slices were oriented
along the central sulcus, covering the primary sensorimotor cortex
and the basal ganglia. Additionally, a three-dimensional T1-
weighted dataset was acquired with a magnetization prepared
rapid acquisition gradient echo (MP-RAGE) sequence in 160 axial
slices1.65-mmthickwith
0.960.9 mm2and field of view (FOV) 238 mm covering the
entire brain and cerebellum (inversion time, TI=1100 ms;
TR=2140 ms; flip angle 15u; TE=3.93 ms) for registration and
display of fMRI results.
nominalin-plane resolution
Movement monitoring set-up
Patients’ motor outcome was recorded using instrumented,
MRI-compatible, bilateral sensory gloves (5thDimension Tech-
nologies, Irvine, CA, USA). The glove contains no ferromagnetic
parts and communicates with a control box placed outside the
scanner room via optical cable. The control box is connected with
a remote computer via USB or serial port. The glove is made of a
stretch lycra material (fits to many hand sizes) with embedded
proprietary fiber-optic-based flexor technology sensors. Two
sensors per finger measure flexion of its knuckle and first joint.
One sensor quantifies the abduction between particular fingers. A
set of 14 sensors allows the complexity of various finger movement
patterns or gestures to be captured with a maximum sampling rate
of 100 Hz and amplitude resolution of 8 data bits. The gloves were
linked to an in-house-built EVSENG system (J. Wackermann, T.
Sieger) for synchronization with the MRI scanner and on-line
recording of the information from the glove. EVSENG was
written to communicate with the glove on the low-level (i.e.
reading data directly from port), but a high-level interface is also
possible via libraries and routines supported by the producer.
Experimental paradigm
A block-based motor paradigm was conceived to investigate the
brain activity associated with the motor performance. Consecutive
movement and rest epochs, each lasting 10 s, recurred 25 times,
resulting in 50 blocks with a total session length of 500 s. During
rest epochs, a visual ‘rest signal’ (centered static red fixation cross
on a black background) was presented on a projection screen,
whereas during movement epochs, 10 pacing ‘movement cues’
(yellow square behind the fixation cross displayed for 100 ms) were
presented with a frequency of 1 Hz. While viewing the ‘rest
signal’, patients were instructed to retain motionless with their
arms in a resting position. During movement epochs, patients had
to perform a unilateral index finger-thumb opposition whenever
the ‘movement signal’ appeared. For ideal performance, a session
would consist of a total of 250 distinct unilateral movements. The
first measurement session started with right-hand movements and
was subsequently repeated for the contralateral hand in the
particular medication condition. Prior to the fMRI experiment,
patients had to perform a calibration gesture (fully clenched fist
followed by one index finger-thumb opposition) to allow the flexor
sensors to reach their peak values and accommodate the amplitude
dynamic range.
A two-by-two factorial design with within-subject factors ‘Hand’
(RIGHT/LEFT) and ‘Levodopa medication’ (OFF/ON) resulted
in four scanning sessions for each patient.
Glove recording processing
For each session, a 14-dimensional kinematic signal was
recorded with the glove with a sampling rate of 64 Hz. Processing
was performed using MatlabH (R2010b, The MathWorks Inc.,
Natick, MA, USA) and subroutines of the SPM8 package
(Wellcome Trust Centre for Neuroimaging, UCL, London, UK).
To consider potential inter/intra-individual differences in the
dynamic range of the finger movements, a normalization
procedure was conducted to obtain consistent scaling of the signal
amplitude. The calibration sequence was used to detect peak and
baseline of the movement, and the signal was then normalized
accordingly by adjusting the peak amplitude to one. Removal of
low frequency fluctuations and drifts was achieved by subtracting
the output of a fast one-dimensional median filter [43] with a 20-s
window from the original signal. Substantial amount of high
frequency quantization noise was primarily present in signals
recorded from less active sensors with the restrained dynamic
range. Wavelet-based de-noising [44] was applied in order to
remove the noise using the global thresholding and a ‘db3’ wavelet
filter family.
Glove waveforms were first analyzed independently on a
behavioral level. Data-driven filtering using principal component
analysis (PCA) [45] was performed to tease apart the global/
deterministic and residual/stochastic features of movement. For a
comprehensive description of data-driven filtering using PCA, see
Daffertshofer et al. [46]. The global pattern represented a
coherent, dominant pattern (the finger tapping movement itself)
and was calculated for each session by reconstructing the 14-
dimensional dataset with principal components explaining more
than 90% of the data. Remaining principal components were used
to reconstruct the residual part of the data. The residual part
reflected movement deviations in participants’ performance.
Variance of each filtered waveform was calculated and averaged
across sensors to obtain the average variance of a session for both
the global and residual movement pattern. The variances of a
session were separated and averaged across particular levels of
experimental factors (RIGHT, LEFT, OFF, ON) and statistically
analyzed inter-individually using analysis of variance with
repeated measures (rmANOVA) with IBM SPSS Statistics 19.
Pre-processed glove recordings were first synchronized with the
timing of MR images acquisition. To build a personalized
regressor as input to the individual-level fMRI design matrix,
time-courses from 14 glove sensors in a session were merged using
two distinct approaches based on linear Gaussian models. (i) The
‘mean approach’ resulted in a waveform computed from the
average of all 14 session-specific waveforms. (ii) The ‘eigenvariate
approach’ reflected the session-specific movement recordings in
terms of the projection of glove data on the first principal
component, which explained the highest proportion of variance of
the input observations. This calculation was based on PCA. Both
mean and eigenvariate versions of waveforms were corrected for
outliers by replacing them with maximal/minimal values in the
non-outliers range. Outliers were defined as data points, which
were more than 1.5 times the interquartile range above the third
or below the first quartile. Frequency spectra of all waveforms
were observed, to verify the absence of any restlessness and
rhythmic motions in frequency band of 4–7 Hz (i.e. resting
tremor/dyskinesias) during resting phase of the task. Furthermore,
when no significant peaks of movement performance were
detected within data values of resting periods, they were adjusted
to zero. Besides spectral analysis of the resting periods we also
focused on motor periods of the task. No peaks suggesting presence
of low (4–6 Hz), intermediate (6–8 Hz) or higher (8–20 Hz)
frequency of tremor in any patient were observed. Finally, for both
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approaches the envelope curve of the resulting waveform was
calculated. To investigate the effect of movement amplitude on the
brain response, amplitude-invariant versions of the mean and
eigenvariate predictors were additionally formed by adjusting
amplitudes of the waveforms in movement periods to unity. This
adjustment provided time series sensitive to the timing of the
movement execution, but invariant in terms of the amplitude of
movement performance. Finally, signals were resampled to match
the number of acquired fMRI time points. Four distinct
waveforms – mean; mean amplitude-invariant (AI); eigenvariate;
eigenvariate AI – were constructed for each participant this way
and used in further analyses in SPM.
fMRI time series analyses
Data pre-processing and analysis was performed using SPM8 in
MatlabH for every session separately. To correct for head
movement artifacts, fMRI data were spatially realigned to the
first image. The individual T1-weighted MP-RAGE dataset was
co-registered with the functional images, segmented with the
unified segmentation approach (UnSA) [47] and normalized to the
Montreal Neurological Institute (MNI) [48] T1template. Normal-
ization parameters from UnSA were then used to normalize all
remaining images. In the final stage of pre-processing, the
functional volumes were smoothed using an isotropic Gaussian
kernel of 8-mm full width at half maximum [49].
Fixed-effects, first-level statistics were performed using a general
linear model (GLM). Two separate types of models were used: (i) a
standard model incorporating a constant baseline term and a
predictor containing a condition-specific, constant-amplitude
boxcar function characterized by onsets and durations of task-
related epochs. This generic model was generated by a conven-
tional procedure standard to SPM that assumed movements
coinciding precisely with cues presented on-screen. (ii) a kinematic
model including the constant term and a custom-made predictor –
one possible alternative to mean, eigenvariate or their amplitude-
invariant/amplitude-sensitive approaches, calculated from kine-
matic recordings as described in ‘Glove recording processing’.
Further processing was conventional and common to both the
standard and kinematic approaches. It involved high-pass filtering
of a 32-s cutoff for removing the most possible amount of low-
frequency fluctuations while sustaining no loss of experimental
power, first-order autoregressive AR(1) model [50,51] for estimat-
ing the intrinsic correlations between residual errors, and a linear
time-invariant (LTI) convolution model [52] based on a linear
approximation of the BOLD response. The session-specific
predictor of each modeling approach was convolved with a
canonical hemodynamic response function (HRF) modeled as the
first-order Volterra kernel [53,54]. Parameters of Gaussians
modeling were determined by timing characteristics of the
experiment. Since amplitude-sensitive varieties of kinematic
predictors had no baseline-to-peak unique range, they were
additionally standardized by scaling the values (5th to 95th
percentile) range of each to unity to consequently ensure valid
comparisons of ß-estimates between different modeling approach-
es. Fixed-effects analysis was performed by fitting the mass-
univariate GLM to calculate parameter estimates and residual
errors. The standard model and four distinct kinematic models
were estimated for each patient and session. Figure 1 illustrates
standard and one of the kinematic predictors for a particular
patient’s session. Contrasting the effect of interest (non-constant
Figure 1. Comparison of session-specific predictors of standard and kinematic approaches in individual-level modeling. (left, blue:
standard approach, generated using experimental timing information and no movement assumptions; right, orange: kinematic, mean amplitude-
sensitive approach, constructed using average of recorded kinematics from all sensors). Dashed lines indicate the most pronounced movement
deviations in a measurement session of a particular patient. Standard modeling is not able to capture this variability and reflects it in the error term in
GLM, likely resulting in biased statistics.
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session-specific regressor of the design matrix) resulted in contrast
images which were used as input for random-effects group
analyses.
To evaluate the effect of dopaminergic medication on brain
correlates of finger motion in the group of PD patients
investigated, both medication conditions were studied separately
for each hand by estimating one-sample t-test random-effects
models. Four models for each level of experimental factor were
estimated for each (standard, kinematic) modeling approach. The
group maps were thresholded at an uncorrected rate of p,0.001,
with the threshold extended to 150 voxels to preserve only clusters
corrected for multiple comparisons using the family-wise error
(FWE) [55] rate of p,0.05 at the peak level. The amplitude of
activity was inspected in regions of interest (ROI) in the left and
right precentral gyrus. The ROIs were generated using automated
anatomical labeling (AAL) atlas [56] with Marsbar SPM toolbox
[57]. Proper scaling of predictors (unit values) and setting contrast
levels (absolute sum of unity) in individual-level models ensured
their maximal interpretability and comparability; the contrast
images calculated approximated the percent signal change (PSC)
directly [58]. We formed the ‘group-level mean’ and ‘group-level
standard error’ PSC image for each levodopa medication
condition and extracted the values from ROI and displayed their
average for every modeling approach, for every level of
experimental factors separately. In order to take inter-individual
differences into account, PSC values were extracted and averaged
from the ROI inter-individually. IBM SPSS Statistics was then
used to calculate rmANOVA with factors ‘Hand’ and ‘Modeling
approach’ for both levodopa ON and levodopa OFF medication
conditions. Hence, the standard approach was compared to each
particular kinematic approach in a pairwise fashion. Using the
same technique, differences between particular (mean, eigenvari-
ate) amplitude-invariant and amplitude-sensitive kinematic ap-
proaches were evaluated.
The difference between medication conditions was assessed
using a flexible-factorial model with within-subjects factors ‘Hand’
(RIGHT/LEFT) and ‘Levodopa medication’ (OFF/ON) by
choosing the difference between the ON and OFF conditions as
an effect of interest. The analyses were carried out for both
standard and kinematic approaches. An uncorrected threshold of
p,0.001 with 30 voxels extent was adopted. On the cluster level,
an FWE rate of p,0.05 was used to control for false positive
activations. To assess the amplitude of activity, ROIs in the left
and right pallidum were defined a priori using the AAL atlas based
on previous work of Kraft et al. [11] and Feigin et al. [59]
revealing the activity in BG in patients on and off medication.
Then, PSC was calculated for each level of experimental factors
using the same procedure as described above. Similarly, pairwise
statistical comparisons were evaluated between the particular
modeling approaches for the levodopa ON condition. Since BG
were not activated in the levodopa OFF medication condition at
all (time courses corresponded to noise), we decided not to perform
any statistics for that condition.
Results
Comparing OFF and ON conditions, the UPDRS-III scores
dropped significantly (F(1,11)=122.52, p,0.001) from 33.5 (9.0)
to 9.6 (4.0) (mean value with standard deviation), demonstrating
the improvement of patients’ motor symptoms in the ON
condition. Analysis of lateralized hemibody UPDRS-III scores in
the OFF condition showed non-significant left/right asymmetry
suggesting that patients with main involvement of the right or left
hemispheres were represented equally in our study.
Behavioral analyses of the hand revealed a significant increase
in the movement variability in the ON condition compared to
OFF in both global (F(1,11)=14.83, p,0.001) and residual
(F(1,11)=33.61, p,0.001) movement patterns (Figure 2). More-
over, a significant interaction between RIGHT/LEFT hand
tapping and the OFF/ON medication condition was found in
the global movement pattern (Figure 2; F(2,11)=5.57, p=0.04).
The resting tremor and dyskinesias were absent in the course of
the experiment in all our patients which was confirmed by analysis
of the glove motion during resting periods of the task.
FMRI analyses performed for each hand and both medication
conditions independently, with the purpose of showing neural
Figure 2. Variability of movement on behavioral level. Each bar
represents average variance of movement performance calculated from
a collection of measurement sessions separated for each level of
experimental factors ‘Hand’ and ‘Levodopa medication’; displayed as
mean+standard error. A: Average variances of global movement pattern
representing the most coherent parts of glove recordings – finger
tapping itself. Main effect of ‘Levodopa medication’ is significant
(p,0.001) and an interaction between ‘Levodopa medication’ and
‘Hand’ is significant (p,0.05). B: Average variances of residual
movement pattern representing stochastic, variable quantity of
participants’ motor behavior. Main effect of ‘Levodopa medication’ is
significant (p,0.001). a.u. - arbitrary unit.
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correlates of finger movements, revealed brain activity in the
primary motor cortex in the hemisphere contralateral to finger
tapping. However, for each hand, a decreased extent of activity in
the primary motor cortex was observed with levodopa intake
(Figure 3). In addition, as opposed to OFF, significant activity was
detected in subcortical areas (BG) after levodopa administration.
Figure 3. Random-effects parametric maps showing brain correlates of right finger movements with and without dopaminergic
medication. Maps were obtained by separate analysis (one sample t-test) of both medication-free (OFF) and medication (ON) conditions on the
group level (top, blue: group maps obtained by standard first-level modeling without further assumptions on motor performance; bottom, orange:
group maps obtained by mean kinematic modeling technique taking motor performance into account). The brain correlates of left finger movements
are not shown here, nevertheless resulted in a similar activity pattern as right finger movements, with a cortical cluster located in the contralateral
hemisphere. Maps were adjusted with a threshold of p,0.001; uncorrected and extended to 150 voxels to show only significant clusters (p,0.05;
FWE corrected) on the cluster level. FWE - Family Wise Error. kC- number of activated voxels in cortical cluster. kS- number of activated voxels in
subcortical cluster.
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Comparing OFF and ON separately, a sensitivity increase in the
extent of activity using kinematic modeling compared to the
standard one was found (Figure 3; see kC, kS). All kinematic
approaches showed significantly higher amplitude of activity
compared to the standard approach, in particular in the precentral
gyrus with patients OFF (Figure 4A; Table 2) and ON (Figure 4B;
Table 2) medication. Also, a significant difference between
amplitude-sensitive and amplitude-invariant versions of both
kinematic approaches was observed for both conditions with an
exception of the eigenvariate kinematic approach in OFF
condition (Table 2).
Investigating the difference between both medication condi-
tions, an increased bilateral response to levodopa in the putamen
and globus pallidus was revealed. Interestingly, the increased
BOLD response in the ON condition was present solely within
areas of BG and not observed in the primary motor cortex. This
result was obtained with all modeling methods (Figure 5; p,0.001
uncorrected). Strikingly, all variations of kinematic modeling
outperformed standard modeling and resulted in an extensive
sensitivity increase, and provided a larger spatial extent of activity
and higher FWE-corrected cluster p-values (Figure 5; Table 3). In
contrast, the right subcortical cluster obtained with standard
modeling did not remain significant after FWE multiple test
correction.
Effect sizes represented as percent signal change in ROIs
located in the left and right pallidum revealed differences between
standard and kinematic modeling approaches even without taking
inter-individual differences into account (Figure 6B). With regard
to inter-individual variability, rmANOVA showed significance in
the main effect of ‘Modeling approach’ (p,0.05) with all kinematic
approaches. Additionally, a significant difference (p,0.05) be-
tween amplitude-sensitive and amplitude-invariant versions of
both (mean, eigenvariate) kinematic approaches was discovered
(Figure 6B). Table 4 summarizes the results in more detail.
Discussion
We investigated the benefit of controlling PD patients’
movement within a finger-tapping fMRI experiment by taking
the movement parameters into account in the analysis. Our results
provide clear evidence of increasing sensitivity in detecting brain
activity in PD patients using fMRI analyses considering on-line
quantification of their motor outcome, compared to generic fMRI
statistics.
Solely behavioral analyses revealed substantial differences in
motor outcome of PD patients between two experimental
manipulations requiring repeated sessions, underlining the impor-
tance of controlling for movement to obtain ‘true’ brain motor
responses with fMRI.
Previous studies investigating levodopa intervention in PD
patients with fMRI have produced conflicting results [17,20,60].
Contradictory activity patterns solely in cortical areas such as the
supplementary motor area (SMA), premotor cortex (PMC) and
primary motor cortex (M1) were observed. Surprisingly, no
activity was detected in subcortical areas such as BG – areas
closely associated with PD – possibly due to a lack of statistical
power with a combination of relatively subtle BOLD responses in
those areas [31]. A more recent study by Ng et al. [18] discussing
conflicting results and interpretations in previous studies conclud-
ed that they only investigated the amplitude of BOLD response
and neglected the spatial pattern of levodopa-induced activity.
They showed that the main effect of levodopa seems to be a spatial
‘focusing effect’ in both subcortical and cortical structures. Work
by Kraft et al. [11] exploited a bimanual task to increase BOLD
responses in BG and showed a bilateral striatal activity in PD
patients as a response to levodopa treatment. However, only
Haslinger et al. [60] explicitly applied the behavioral motor
information in fMRI modeling. In our opinion, not taking the
movement into account in HR modeling may have been a
sufficient source of bias in interpretations in the aforementioned
studies, considering the close relationship between patients’ motor
performance and dopaminergic medication. Using flexible-facto-
rial random-effects design, we increased the statistical power by
joining data from left and right finger movements in one model.
We observed a significant increase in the BOLD response in BG as
a result of levodopa intake in PD patients compared to the
medication-free condition, which is in agreement with the fMRI
results of Kraft et al. [11] and PET results of Feigin et al. [59].
This supports the idea of a ‘normalizing effect’ of levodopa in
Figure 4. Comparison of modeling approaches as average
effect size in anatomical ROI (contralateral precentral gyrus).
Each bar represents the average value from ROI for the ‘group-level
mean’ PSC image, and each error bar the average of the ‘group-level
standard error’ PSC image. A: Percent signal change for the Levodopa
OFF condition and all modeling approaches. B: Percent signal change
for the Levodopa ON condition and all modeling approaches. AI{-
amplitude invariant.
doi:10.1371/journal.pone.0036271.g004
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putamen in cortico-subcortico-cortical circuits of the current
pathophysiological model [1,61]. The response in BG nevertheless
appears to a different degree of amplitude and extent using either
standard or kinematic modeling techniques.
In this study, kinematic modeling was performed using the
behavioral information incorporated in a single regressor, in order
to preserve the design efficiency and avoid covariate correlation
problems because the movement-related regressor is likely to
correlate with the stimulus-based regressor to a high degree. In the
interpretations of fMRI statistical tests, correlation is a potential
source of ambiguity arising even in the simplest models [62].
Orthogonalizing the regressor in respect to other as a method to
tackle correlation problems in studies controlling for motor
performance of participants [33–35] is an option; however, it may
not solve the problem because of decreased sensitivity. The type of
modeling framework used in this work is especially useful for
identifying ‘true’ motor brain responses. Extending the design with
another stimulus-specific regressor would be particularly interesting
for investigating phenomena such as neural correlates of motor
planning and preparation, sensorimotor integration, or identifying
motor circuitry responsible for pathological movement. In such
cases, the model must incorporate experiment information in order
to discriminate between actual movement and stimuli presentation.
We used two types of linear Gaussian models to input glove
recordings with hemodynamic modeling and compared this
approach with generic analysis commonly used in PD motor
studies, where no quantitative analysis of movement performance
is usually reported. In investigating motor abnormalities with task-
related fMRI, experimental set-up and timing do not provide
sufficient information for modeling the hemodynamic response
adequately, resulting in sensitivity decrease. All formerly described
kinematic models yielded better fits than the standard analysis. We
favor the eigenvariate approach as being theoretically more
sensitive in reducing the dimensionality of high-dimensional data
as compared to simple averaging. It preserves the dominant task-
related movement pattern by assigning higher weights to inputs
contributing to it while eliminating components likely correspond-
ing to noise. On the other hand, the mean model accounts for
every single input (sensor) to the same degree, which may result in
detecting movement deviations specific to the disease, with the
penalty of a higher probability for introducing noise. In fact, both
approaches are often correlated and result in a similar outcome,
especially if the input data are pre-smoothed (i.e., low-pass
Table 2. Main effect of ‘Modeling approach’: percent signal change in anatomical ROI (left, right precentral gyrus) as F-statistics
obtained by comparing particular modeling approaches in levodopa OFF and levodopa ON conditions.
Modeling approaches compared
F-statisticSignificance level
OFFON OFFON
Standard – Kinematic, meanF(1, 11)=55.86F(1, 11)=108.20p,0.001p,0.001
Standard – Kinematic, mean AI{
F(1, 11)=59.77F(1, 11)=85.43p,0.001p,0.001
Standard – Kinematic, eigenvariateF(1, 11)=63.75F(1, 11)=68.21p,0.001p,0.001
Standard – Kinematic, eigenvariate AI{
F(1, 11)=52.97F(1, 11)=77.46p,0.001p,0.001
Kinematic, mean – Kinematic, mean AI{
F(1, 11)=23.26F(1, 11)=37.86p=0.001p,0.001
Kinematic, eigenvariate – Kinematic, eigenvariate AI{
F(1, 11)=3.64F(1, 11)=18.15p=0.083p=0.001
AI{- amplitude invariant.
doi:10.1371/journal.pone.0036271.t002
Figure 5. Group-level response (ON-OFF) of PD patients to levodopa treatment. Uncorrected threshold of p,0.001 was adopted and maps
were overlaid on coronal and axial slices (left, blue: group maps obtained by standard first-level modeling without further assumptions on motor
performance; right, orange: improvement of group-level maps obtained using various kinematic modeling techniques taking movement
performance into account). AI{- amplitude invariant.
doi:10.1371/journal.pone.0036271.g005
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filtered). Then the input variables have a higher likelihood of co-
varying and reflect to similar extents on the first principal
component, which roughly speaking, is a process of averaging.
Both of the approaches presented provided a clear increase in
sensitivity and we recommend them both for merging multi-
dimensional movement recordings such as those presented here.
A clear sensitivity increase was gained by using all variations of
kinematic approaches, as they accounted for undesired movement
variations of participants in hemodynamic modeling. This
increased the sensitivity in detecting correlations of kinematic
models’ predictors with measured brain responses and contributed
to the reduction of ‘type II’ errors on the individual level. With the
repetitive nature of the experiment and the experimental
intervention altering the motor outcome significantly, an additive
character of the sensitivity increase was also demonstrated on the
group level by treating both conditions of dopaminergic treatment
separately and by contrasting their difference. Besides conspicuous
and dominant increase of sensitivity caused by accurate timing of
the HRF, a beneficial effect of considering the amplitude of
movement using amplitude-sensitive kinematic approaches in
forming the HRF is also evident. Waldvogel et al. [26] stated
that an increased neuronal firing rate resulting from healthy
subjects tapping with a larger amplitude leads to higher synaptic
activity, higher metabolic demand, and therefore an increased
BOLD signal. Their alternative explanation is based on the fact
that the observed BOLD increase is caused by subjects using
additional muscles needed to stabilize the hand during large-
amplitude movements. However, they did not provide an account
of the quantitative reciprocal relationship between the two. With
one exception (eigenvariate kinematic approach, levodopa OFF),
we detected significant increases of percent signal change using
amplitude-sensitive versions compared to amplitude-invariant
versions of both linear Gaussian kinematic approaches. Consid-
ering the conclusions by Waldvogel et al. [26] and our results, we
conclude that there is a mutual relationship between an increase in
movement amplitude and HR in PD patients, too. In addition to
ensuring that timing is correct, amplitude of movement in PD
fMRI motor experiments must also be controlled.
Conclusions
The approach presented here used an fMRI design of
alternating movement/rest blocks, but might be also suitable for
event-related designs. In comparison to block designs, event-
related designs require more variable tasks in terms of motor
performance (to preserve the design efficient) and where a precise
knowledge of behavioral information such as subjects’ motor
performance is of greater importance according to numerical
Table 3. List of performance measures revealed by all modeling approaches.
Modeling approach
Cluster p-valueNumber of activated voxelsPeak t-value
Left clusterRight clusterLeft clusterRight cluster Left clusterRight cluster
Standard0.0210.073 64344.274.21
Kinematic, mean0.0170.007 68914.374.59
Kinematic, mean AI{
0.0120.025 79594.22 4.28
Kinematic, eigenvariate0.010 0.01480 71 4.20 4.75
Kinematic, eigenvariate AI{
0.0290.011 8055 4.214.30
Showed items include p-values of clusters, numbers of activated voxels and peak t-values. Cluster p-value is FWE (Family Wise Error) corrected for multiple comparisons
at p,0.05. Number of activated voxels are at the threshold of p,0.001 (uncorrected). AI{- amplitude invariant.
doi:10.1371/journal.pone.0036271.t003
Figure 6. Comparison of modeling approaches as average
effect size in anatomical ROI (left and right pallidum). Each bar
represents the average value from ROI for the ‘group-level mean’ PSC
image, and each error bar the average of the ‘group-level standard
error’ PSC image. A: Percent signal change for the Levodopa OFF
condition and all modeling approaches. In contrast to the ON condition,
in the OFF condition, the basal ganglia were not activated so the data
corresponds to noise. B: Percent signal change for the Levodopa ON
condition and all modeling approaches. AI{- amplitude invariant.
doi:10.1371/journal.pone.0036271.g006
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simulations by MacIntosh et al. [33]. In PD patients, with
increasing demands and task difficulty, and with a wide spectrum
of possible experimental manipulations such as medication,
without controlling for movement, one can barely detect
undesirable movement deviations. Our results demonstrate the
importance of controlling movements when investigating PD
patients using fMRI. We strongly advocate quantitatively control-
ling for motor performance in order to increase the sensitivity by
taking the patient’s behavior into account.
Supporting Information
Table S1
Disease Rating Scale. G: gender. FS: First signs of the disease at
age (years). LC: Late complications of the disease at age (years).
UPDRS-III* - Motor part of Unified Parkinson’s
LT: duration of levodopa treatment (years). OFF: without
levodopa medication. ON: with levodopa medication.
(DOC)
Acknowledgments
The authors thank Rosie Wallis for proofreading this manuscript.
Author Contributions
Conceived and designed the experiments: RJ KM. Performed the
experiments: RJ TS. Analyzed the data: SH KM. Wrote the paper: SH.
Interpretation of results: SH MLS RJ KM. Revisions and final version
contributions: SH HEM TS MLS RJ KM.
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