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Accepted Manuscript
Beta synchrony in the cortico-basal ganglia network during
regulation of force control on and off dopamine
Petra Fischer, Alek Pogosyan, Alexander L. Green, Tipu Z. Aziz,
Jonathan Hyam, Thomas Foltynie, Patricia Limousin, Ludvic
Zrinzo, Michael Samuel, Keyoumars Ashkan, Mauro Da Lio,
Mariolino De Cecco, Alberto Fornaser, Peter Brown, Huiling Tan
PII: S0969-9961(19)30063-4
DOI: https://doi.org/10.1016/j.nbd.2019.03.004
Reference: YNBDI 4416
To appear in: Neurobiology of Disease
Received date: 11 January 2019
Revised date: 19 February 2019
Accepted date: 4 March 2019
Please cite this article as: P. Fischer, A. Pogosyan, A.L. Green, et al., Beta synchrony in
the cortico-basal ganglia network during regulation of force control on and off dopamine,
Neurobiology of Disease, https://doi.org/10.1016/j.nbd.2019.03.004
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Beta synchrony in the cortico-basal
ganglia network during regulation of force
control on and off dopamine
Petra Fischer1,2,* petra.fischer@ndcn.ox.ac.uk , Alek Pogosyan1,2, Alexander L. Green2, Tipu
Z. Aziz2, Jonathan Hyam3, Thomas Foltynie3, Patricia Limousin3, Ludvic Zrinzo3, Michael
Samuel4, Keyoumars Ashkan4, Mauro Da Lio5, Mariolino De Cecco5, Alberto Fornaser5,
Peter Brown1,2, Huiling Tan1,2
1Medical Research Council Brain Network Dynamics Unit at the University of Oxford, OX1 3TH, Oxford, UK
2Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, OX3 9DU, Oxford, UK
3Unit of Functional Neurosurgery, Department of Clinical and Movement Neurosciences, University College London
Institute of Neurology, WC1N 3BG, London, UK
4Departments of Neurology and Neurosurgery, King’s College Hospital, King’s College London, SE5 9RS, London, UK
5Department of Industrial Engineering, Università degli Studi di Trento, (via Sommarive, 9) 38123, Trento, Italy
*Corresponding author at: Nuffield Department of Clinical Neurosciences, Level 6, West
Wing, John Radcliffe Hospital, Oxford, OX3 9DU.
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Abstract
Beta power suppression in the basal ganglia is stronger during movements that require high
force levels and high movement effort but it has been difficult to dissociate the two. We
recorded scalp EEG and basal ganglia local field potentials in Parkinson’s disease patients
(11 STN, 7 GPi) ON and OFF dopaminergic medication while they performed a visually-
guided force matching task using a pen on a force-sensitive graphics tablet. Force
adjustments were accompanied by beta power suppression irrespective of whether the force
was increased or reduced. Before the adjustment was completed, beta activity returned. High
beta power was specifically associated with slowing of the force adjustment. ON medication,
the peak force rate was faster and cortico-basal ganglia beta phase coupling was more readily
modulated. In particular, phase decoupling was stronger during faster adjustments. The
results suggest that beta power in the basal ganglia does not covary with force per se, but
rather with a related factor, the absolute force rate, or a more general concept of movement
effort. The results also highlight that beta activity reappears during stabilization of isometric
contractions, and that dopamine-related suppression of cortico-basal ganglia beta coupling is
linked to faster force adjustments.
Keywords: Force control, isometric contraction, cortico-basal ganglia
coupling, beta oscillations, beta power, beta coupling
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Abbreviations
DBS Deep brain stimulation
EEG Electroencephalogram
GPi Internal globus pallidus
ISPC Intersite phase clustering
LFP Local field potential
STN Subthalamic nucleus
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Introduction
Mounting evidence suggests the basal ganglia are involved in regulating movement vigour
(Da Silva et al., 2018; Mazzoni et al., 2007; Turner and Desmurget, 2010; Yttri and Dudman,
2016). Direct recordings from basal ganglia targets in patients have shown that reciprocal
changes in beta and gamma band activities in the local field potential (LFP) covary with the
production of different force levels (Tan et al., 2013) as well as with movement size and
speed (Brücke et al., 2012; Joundi et al., 2012). Beta power in motor cortex and the basal
ganglia is suppressed during ballistic movements (Brücke et al., 2008; Kilavik et al., 2013;
Kühn et al., 2004), and especially so during vigorous movements (Tan et al., 2015, 2013), but
during sustained, stable contractions, motor cortical beta power and cortico-muscular
coherence is increased (reviewed in Kilavik et al., 2013). One prominent hypothesis is that
beta oscillations promote the status quo (Engel and Fries, 2010; Gilbertson et al., 2005) – or,
in other words, the stability of the current state. Interestingly, beta oscillations reappearing
after a movement are also modulated by sensory feedback (Tan et al., 2014a, 2014b;
Torrecillos et al., 2015). Most motor assessments involve either ballistic movements or
sustained force and thus to date it is unknown how basal ganglia beta oscillations relate to
small adjustments of otherwise sustained, isometric forces. Yet small movements are
impaired early in Parkinson’s disease, as evinced by the deterioration in handwriting that is
often the first symptom of the condition (Pinto and Velay, 2015; Rosenblum et al., 2013).
Here we recorded the LFP in the subthalamic nucleus (STN) or globus pallidus interna (GPi)
whilst Parkinsonian patients made visually-cued, low-force isometric force adjustments with
a hand-held pen on a force-sensitive tablet. This allowed us to contrast spectral changes
during isometric force increases and reductions. In particular, the paradigm enabled us to
explore two contrasting hypotheses. The first is that beta oscillations are inversely related to
force levels, in which case we should find decreased power during force increases, but
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increased power during force reductions. Alternatively, beta oscillations may be related to a
directionless measure, such as the rate of any changing muscle contraction, or the effort
made, rather than the generated force per se (Tan et al, 2015). In this case we should find that
beta power is suppressed irrespective of the direction of the force change. In addition, by
using the same paradigm both off and on dopaminergic medication, we were able to test the
hypothesis that the pattern of basal ganglia activity associated with finely controlled motor
adjustments differs according to dopaminergic status. Here, we were particularly interested in
the balance between beta reactivity locally in the STN and GPi and in the long-range
coupling between the STN/GPi and cortex (Cassidy et al., 2002; Litvak et al., 2011; van Wijk
et al., 2017).
Methods
Participants
Eighteen patients with idiopathic Parkinson’s disease (mean disease duration of 10 ± (STD) 5
years, mean age 62 ± 7 years, 16 males) provided written informed consent to take part in this
study, which was approved by the local ethics committees. Clinical details of the patients are
given in Supplementary Table 1. The patients showed 58 ± (STD) 14% improvement in the
motor section of the Unified Parkinson’s Disease Rating Scale (UPDRS) on treatment with
levodopa, indicating good responsiveness to medication.
All patients were implanted with bilateral DBS electrodes as a prelude to therapeutic high-
frequency stimulation for advanced Parkinson’s disease with motor fluctuations and/or
dyskinesia 3-6 days prior to the recording. The target for the electrodes were the STN in
eleven patients and the GPi in seven patients.
DBS electrode extension cables were externalized through the scalp to enable recordings
before they were connected to a subcutaneous DBS pacemaker implanted in a second
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operation up to 7 days later. The electrode implantation procedure has been described
previously (Foltynie and Hariz, 2010). The macroelectrodes implanted were Model 3389 TM
from Medtronic Neurologic Division and Model DB-2201 TM and DB-2202 TM from Boston
Scientific. Surgeries and recordings were performed at one of the following three sites:
King’s College hospital, London, University College hospital, London, or the John Radcliffe
hospital, Oxford, UK. Several cases have been previously reported in other studies (Fischer et
al., 2017a, 2017b).
Experimental paradigm
LFPs were recorded while patients performed a visually-guided force matching task. Each
experimental run lasted 120s, in which the size of a blue target box (contour width = 8
pixels), which denoted the target force, randomly changed on average every 3.7s 2.2s (Fig.
1A). The target force was on average 1.58 N 0.13 N, ranging from 0.31 to 2.79 N.
Patients were asked to control the size of a black box (contour width = 2 pixels) that was
centered on the same point of the screen as the target box by varying the force of a pen on a
graphic tablet (Wacom Intuos CTL-480, small). The box expanded with increasing force, and
patients were instructed to match the size of the target box as precisely as possible. A similar
setup has previously been introduced to assist rehabilitation protocols and assess motor
performance (Confalonieri et al., 2012; Kirchner et al., 2011). The task was performed with
the dominant hand, but if patients had severe tremor or rigidity in this hand it was performed
with the non-dominant one (in 6 of 18 patients). All patients were recorded ON medication,
and a subset of 11 patients were also recorded OFF medication (5 STN, 6 GPi). 6 of the 11
patients were recorded first in the OFF-medication condition to limit an order effect.
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In each condition, four consecutive runs were recorded with breaks in-between ranging
between several seconds to minutes depending on fatigue. Thus, patients performed the task
for eight minutes in total, or twice this if both medication states were tested. In half of all
runs, a red distractor box was present, which randomly differed in size from the target box.
Patients were instructed to ignore the red box and focus on matching the size of the black box
to the target box, which they successfully did. A demo of the task including the distractor box
is shown in a video available on YouTube (https://www.youtube.com/watch?v=v7EbPjZB-
dM). As we focus on motor dynamics in this study, we pooled the data across all runs.
Data recording
LFP and EEG signals were recorded with a TMSi Porti amplifier (2048 Hz sampling rate,
TMS International, Netherlands). The timings of target changes were registered with a light-
sensitive sensor attached to the corner of the presentation laptop. The software was
programmed to produce a brightness pulse under the sensor at the onset of changes in the
target size. The behavioural data (target force and applied force, sampled at 5 ms) were
written to a separate text-file stored on the presentation laptop and later merged with the
LFP/EEG recordings in MATLAB (RRID:SCR_001622, v. 2016a, The MathWorks Inc.,
Natick, Massachusetts) by interpolating and resampling the behavioural data. EEG electrodes
were placed over (or close to if sutures had to be avoided) Fz, Cz, Pz, Oz, C3 and C4
according to the international 10-20 system. For one patient, the electrode over ipsilateral
motor cortex was intermittently inactive and thus had to be excluded.
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Behavioural analysis
The force matching task required continuous control over hand and arm muscles. It consisted
of stable periods and isometric force modulations for which a precise adjustment of muscle
activity was required. Examples of the pen force recordings are shown in Fig. 1B. The start
and end points of each force adjustment were tagged manually to ensure they were correctly
identified. Sometimes these adjustments involved over- or undershoots into the opposite
direction of the instructed force change. These were also tagged so they could be analysed
separately. To get robust estimates of the central tendency of reaction times or the peak force
rate, the median across trials was computed.
Analysis of EEG and LFP recordings
Frequency-band and channel selection
The data were first down-sampled to 1000 Hz and artefacts in the LFP or EEG recordings
were excluded by visual inspection (16% of the data). EEG electrodes over the contra- and
ipsilateral motor cortex (C3 and C4) were re-referenced to the average of all recorded EEG
channels (Fz, Cz, Pz, Oz, C3 and C4). To obtain spatially focal bipolar signals of the
recorded LFPs, the difference between the raw signal of two neighbouring DBS electrode
contacts was computed. If single channels saturated or were inactive (in 6 of 36 electrodes),
the remaining surrounding contacts were subtracted instead. We included an additional
bipolar configuration by calculating the difference between the lowest and highest contacts,
as in some cases beta modulation across contiguous bipolar contacts was indistinct. For 8 of
36 electrodes the strongest modulation was in the configuration spanning the widest distance.
To assess if modulation was present during force adjustments, we first computed the power
for frequencies ranging from 5-45 Hz (in 1 Hz steps) and 55-100 Hz (in 5 Hz steps) using
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continuous Morlet wavelet transforms (fieldtrip-function ft_freqanalysis,
RRID:SCR_004849, Oostenveld et al., 2011). The number of wavelet-cycles was 6 for
frequencies below 50 Hz and 12 for frequencies above 50 Hz. To investigate in which
frequency range the power modulation was strongest, for each subject the median across all
force adjustments was computed, averaged across all bipolar signals for each electrode, and
smoothed with a 0.1s sliding window. Significance tests based on a cluster-based permutation
procedure for multiple comparison correction (see below) showed strongest modulation
between 15-30 Hz just before the mid-point of the force adjustment (Fig. 2). To select one
bipolar signal from each electrode for further analyses, only ON medication trials were
considered as not all patients were recorded OFF medication and as beta modulation is
stronger ON medication (Doyle et al., 2005). For each electrode, the channel with the
strongest force adjustment-related 15-30 Hz beta decrease in this range was pre-selected. This
was based on all trials in the ON medication condition irrespective of whether the force had
to be increased or reduced. The channel with the highest modulation was identified by
computing t-scores (across all adjustments) of 15-30 Hz beta power averaged across a -
300:100ms window around the mid-points of the adjustments, according to the significant
cluster shown in Fig. 2. The instantaneous phase and power of 15-30 Hz beta oscillations was
then extracted with a Hilbert-transform of the filtered data (Butterworth filter, filter order = 4,
passed forwards and backwards, fieldtrip-function ft_preproc_highpassfilter and
ft_preproc_lowpassfilter) to perform further analyses.
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Assessment of force adjustment-related power changes
To assess how power changed from the beginning to the end of a force adjustment despite
variable durations, we subdivided each adjustment period into three segments: 1) 1s before
the adjustment started until the start, 2) from the start until the end, 3) from the end of the
adjustment until 1s afterwards. Each of these three segments was divided into 30 equidistant
points, and then the median of beta power at these points was computed across all
adjustments to get a robust estimate. Thus, for adjustments that took relatively long, the
distances between the 30 points were larger than when they were short. This way, we
evaluated how beta changed throughout different phases of the force adjustments,
independent of how long they took. Before computing cluster-based permutation statistics
(see below) smoothing was applied (moving average window size: 5 samples, MATLAB
function smooth).
To test if beta increased significantly before completion of the adjustment, we computed
Pearson’s linear correlation coefficients for each patient based on the 30 points between the
start and end of the force adjustment (x = bin number, y = beta power). The resulting
coefficients were Fisher’s Z-transformed and then tested against zero to find out if a
relationship existed at the group level.
Correlation between beta power and force
In examples of individual trials, we noticed plateaus in the force adjustment when beta power
was high, indicating slowing (Supplementary Fig. 1). To assess at the group level if beta
power correlates with the absolute force rate (and as control analysis also with the force
level) irrespective of where the increase in power occurred within an adjustment, we again
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divided each adjustment into 30 equal bins. This resulted in [nr. of adjustments*30] values
for both variables, which were pooled to compute one within-subject correlation coefficient
for each patient. The resulting correlation coefficients were Fisher’s Z-transformed to test if
they differed significantly from zero at the group level.
As we observed a beta power increase towards the end of an adjustment, just before the
adjustment comes to a halt (thus coinciding more likely with a lower force rate), this
correlation may be mainly driven by beta power being higher and force rates being lower
towards the end. To show that this alone could not explain the correlation, we tested if the
correlation is also significant compared to a permutation distribution, which was created by
keeping the order of the 30 bins intact for each trial but permuting the association between
beta power and the force measurement across trials. This ensured that beta power from late
parts of an adjustment were always paired with absolute force rates from another late part of
an adjustment. After performing this permutation 500 times we tested if the original
correlation coefficient was more extreme than the permuted ones (computing two-tailed p-
values according to Ernst (2004)).
Assessment of cortico-basal ganglia coupling
Coupling strength was evaluated by computing inter-site phase-clustering values (ISPC,
Lachaux et al., 2000) based on the differences in beta phase between the concomitant EEG
and LFP recordings (). The phase was obtained by computing the Hilbert-
transform of the 15-30 Hz filtered signals. ISPC values correspond to the length of the
average vector of phase differences represented as vectors with length one on a unit circle (n
= number of time points):
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Note that the amplitude of the signal does not contribute to the ISPC estimate. However, if
beta power drops too low in one or both of the signals, phase is poorly defined and coupling
will necessarily drop.
Probability of beta bursts during movements and average amplitude
We also investigated whether movement-related power changes were associated with
modified beta burst probabilities or differences in beta burst peak amplitudes. Beta bursts
were classified as periods where the 15-30 Hz amplitude exceeded a threshold defined as the
75% percentile of the resting baseline recorded ON medication (Tinkhauser et al., 2017).
Only suprathreshold events exceeding a duration of 100ms were classified as bursts because
anything shorter would contain less than two beta cycles. We tested specifically how the
probability and peak amplitude varied in the first and second half of the movement.
Statistical analyses
Cluster-based permutation test
All statistical tests for which multiple time bins or multiple time and frequency bins (as in
Fig. 2) were tested were corrected for multiple comparisons by using a cluster-based
permutation correction approach (Maris and Oostenveld, 2007): A permutation distribution
was generated by swapping the sign of a random subset of pairwise differences 2000 times.
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Suprathreshold-clusters (pre-cluster threshold: P<0.05) were obtained for the original
unpermuted data and for each permutation sample by computing the z-scores relative to the
permutation distribution. If the sum of the absolute z- scores within any original
suprathreshold-cluster exceeded the 95th percentile of the sums of absolute z-scores from the
2000 largest suprathreshold-clusters obtained from the permutation distribution, it was
considered statistically significant. The procedure corrects for multiple comparisons by
comparing not each point individually but by comparing by the largest number of contingent
significant points, which should be lower in the permuted data if the cluster did not occur just
by chance. Any other tests that required multiple comparison correction but did not contain
contingent time- or frequency bins were corrected using FDR correction.
ANOVAs and pairwise comparisons
To ensure that pooling of the STN and GPi recordings was justified, we computed the
following ANOVAs to assess if beta power was modulated similarly in recordings from the
STN and the GPi: The first ANOVA only included data from the ON medication condition
(n=18) with nucleus as between-subjects factor and hemisphere (contra-/ipsilateral) and
adjustment type (force increase or reduction) as within-subjects factor.
The second ANOVA only included the subset of patients where both ON and OFF
medication conditions were recorded (n=11). It contained again the between-subjects factor
nucleus (STN/GPi) and two factors (medication: ON/OFF, and adjustment:
increase/reduction). If the sphericity assumption was violated, Greenhouse-Geisser correction
was applied. Pairwise comparisons were performed with paired t-tests or Wilcoxon signed
rank tests (denoted as WSR-test) if the normality assumption was violated (assessed with a
Lilliefors test).
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Correlations between peak force rates, power and coupling
To investigate the relationship between peak force rates versus beta power or beta phase
coupling ON and OFF medication, we computed robust Spearman’s correlation coefficients.
The resulting coefficients were Fisher’s Z-transformed before testing if they were
significantly different from zero on a group level. Additionally, we controlled for the relative
contribution of power changes by computing partial correlations between ISPC values and
peak force rates.
Results
Behavioural results
The peak force rate was higher when the force was reduced than when it was increased. This
was found to be statistically significant when patients were OFF medication (p=0.011, t10=-
3.1). It was not strictly statistically significant, but a trend was also present ON medication (p
= 0.065, t17=-2.0, Fig. 1C). Comparing peak force rates between the ON and OFF medication
condition, we found that the peak rate during force increases was significantly higher when
patients were medicated (p = 0.017, t10 = 2.9). The peak rate during force decreases did not
differ significantly between medication states (p = 0.278, t10 = -1.1).
ON medication, the average duration of the adjustment was 835ms and 645ms for force
increases and reductions respectively. OFF medication, it was 824ms and 676ms (no
significant differences after FDR correction for multiple comparisons, Fig. 1C). Reaction
times were significantly faster for force increases compared to reductions when patients were
ON medication (ON: p = 0.005, t17=-3.3; OFF: p = 0.895, t10=-0.1, Fig. 1C).
The average force preceding force increases was 1.30 0.12 and 1.83 0.17 N when the
adjustment was completed, while for force reductions it was 1.81 0.16 and 1.26 0.12 N.
The average amount of force change was similar for force increases and reductions
(increases: 0.51 0.05 N, reductions: 0.50 0.05 N).
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Electrophysiology
15-30 Hz beta power (averaged across all channels) decreased during force adjustments
As a first step, before focussing on the bipolar signals with the strongest beta modulation, we
investigated LFP power changes in the ON medication condition averaged across all bipolar
channels and all adjustments (force increases and reductions). We aligned the data to the
midpoint of each force adjustment (= the mid-point between two black crosses in Fig. 1B)
and found a significant power decrease relative to the median power of each session in all
contralateral structures, and even in the ipsilateral STN/GPi and M1 (Fig. 2). As the power
decrease in subcortical structures was most pronounced between 15-30 Hz just before the
midpoint of the force adjustment, we focussed all further analyses on channels showing the
strongest 15-30 Hz beta decrease within a -300:100ms window around the midpoint of the
force adjustments. These contacts are presumed to have picked up activity related to motor
control, and thereby to be closest to the dorsolateral sensorimotor region of the STN (Horn et
al., 2017). In the OFF medication condition recorded in a subgroup, a similar beta power
decrease occurred (Supplementary Fig. 2). Also when the data is aligned to the cue change,
the beta decrease looks similar (Supplementary Fig. 3). No significant power modulation
was observed in the gamma frequency range (Supplementary Fig. 4)
During force adjustments, beta power was lower than the resting baseline activity
We tested whether beta power during force adjustments was suppressed relative to a baseline
activity at rest (recorded in both ON and OFF medication states). Power obtained from the -
300:100ms window around the midpoint of the force adjustments (as used for channel pre-
selection and regardless of adjustment direction) was significantly suppressed in all channels
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in the ON medication condition (contra LFP: -14%, p = 0.013, t17 = -2.8; ipsi LFP: -13%, p =
0.007, t17 = -3.1; contra M1: -12%, p = 0.037, t17 = -2.3; ipsi M1: -17%, p = 0.007, t16 = -3.1)
and in all channels apart from the ipsilateral STN/GPi in the OFF medication condition
(contra LFP: -12%, p = 0.014, WSR-test (n=11); ipsi LFP: -3.0%, p = 0.102, WSR-test
(n=11); contra M1: -20%, p < 0.001, t10 = -5.8; ipsi M1: -21%, p = 0.003, t9 = -4.0)
We also compared if the holding period, within which a stable force had to be applied,
differed from the resting baseline. A 400ms long window starting 400ms before the cue
changed was examined. No significant differences between the stable period and the resting
baseline were found (ON: contra LFP: -0.5%, p = 0.939; ipsi LFP: -7.1%, p = 0.286; contra
M1: -3.3%, p = 0.521; ipsi M1: -9.2%, p = 0.112; OFF: contra LFP: 5%, p = 0.831; ipsi LFP:
3%, p = 0.520; contra M1: -5.5%, p = 0.285; ipsi M1: -9.3%, p = 0.215).
For all further analyses, for example contrasting force increases and decreases, we obtained
relative within-condition power changes by normalizing the data to the median of each
recording session to minimize heteroscedasticity between conditions.
Beta power decreased more when the force was reduced than when it was increased, and
behaved similarly in the two nuclei
Next, we assessed if power changes differed when the force was increased or reduced. We
computed a 2x2 repeated-measures ANOVA with the adjustment-related beta power change
as dependent variable (averaged within the -300:100ms window around the midpoint of force
adjustments, normalized within each session), and subcortical nucleus (STN/GPi) as
between-subjects factor (ON medication only). The two within-subjects factors were change
direction (CD: force increase/reduction) and hemisphere (HS: contra/ipsilateral). The
ANOVA resulted only in a significant main effect of hemisphere. An interaction between
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change direction and hemisphere only came close to being significant (HS: p = 0.025, CD:
p=0.204, CD *nucl: p = 0.325, CD*HS: p = 0.080, HS*nucl: p = 0.683, CD*HS*nucl: p =
0.853, nucl: p = 0.819, see Supplementary Table 2 for F-statistics). When directly
comparing the two beta power time courses using a cluster-based permutation procedure to
correct for multiple comparisons over time, we saw a difference between force increases and
reductions in the contralateral LFP (Fig. 3, red shaded areas): Beta power stayed more
strongly suppressed throughout the adjustment when the force was reduced compared to
when it was increased. Considering that the peak force rate during force reductions was faster
than during increases (Fig. 1C), the observed power difference may be related to differences
in adjustment speed, which will be investigated in more detail below.
A second ANOVA was computed to compare if the relative beta power suppression differed
between the two medication states in the reduced subset of patients where both ON and OFF
medication conditions were recorded (n=11). It contained again two factors (MED: ON/OFF,
and CD: increase/reduction) and the between-subjects factor nucleus (STN/GPi). The
dependent variable was the amount of beta suppression in the contralateral STN/GPi. Only
the main effect CD was significant (CD: p=0.007; MED: p = 0.908, MED*nucl: p=0.947,
CD*nucl: p = 0.402, CD*MED: p=0.436, CD*MED*nucl: p=0.105, nucl: p=0.646). The fact
that none of the effects involving the between-subjects factor nucleus were significant
suggests that beta modulation related to the force adjustment was similar in the STN and the
GPi. This similarity is also shown in Supplementary Fig. 5, which depicts the beta power
time courses separately for the two subsets of STN and GPi recordings.
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Trial-averaged beta power gradually began recovering from suppression already before the
force adjustment was completed
Fig. 3 shows that beta power dropped already about 300ms before the force adjustment
started. Beta power suppression was maximal at the beginning of the force adjustment and
began to recover gradually immediately afterwards – already before the change in force was
completed.
To assess if beta significantly began recovering before the force change was completed, we
computed Pearson’s correlation coefficients for each patient based on the power at the 30
points between the start and end of the adjustment (x: bin number, y: beta power). We first
tested if the Fisher’s Z-transformed correlation coefficients differed from zero at the group
level in the ON medication condition and included all adjustments of the isometric
contraction irrespective of whether the force was increased or reduced. The coefficients were
positive and significantly different from zero, showing a significant gradual increase (contra
LFP: Z(R) = 1.6, p = 0.006, t17 = 3.1, ipsi LFP: Z(R) = 2.3, p < 0.001, t17 = 4.6, contra M1:
Z(R) = 1.7, p = 0.002, t17 = 3.6, ipsi M1: Z(R) = 1.2, p = 0.029, t16 = 2.4). This demonstrates
that in all regions, beta power began recovering before the force adjustment was completed.
To test if the gradual beta recovery differed between change directions, hemispheres and
nuclei, we performed again a 2x2 ANOVA with GPi/STN as between-subjects factor. No
significant main effects or interactions were found (see Supplementary Table 2). The 2*2
ANOVA with the reduced subset of patients and factors medication (ON/OFF) and change
direction (increase/reduced) also showed no significant effects (see Supplementary Table
2). This suggests that the gradual beta power recovery before the adjustment was completed
was generally present during force adjustments, irrespective of direction. As the adjustment
slows down before reaching the correct force level, this also indicates that high beta power
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may be related to the absolute change in force – the absolute force rate.
Beta power recovery before the force adjustment was completed was associated with an
increased burst probability
Fig. 3 shows that the average beta power was higher towards the end of a pressure change
compared to the beginning but it is not clear if the average increase is due to an increased
probability of high-amplitude beta events (= beta bursts) or an increased peak amplitude of
these events. To help distinguish between these two options, we investigated how beta burst
probability and peak amplitude varied from the beginning to the end of each pressure change
in the contralateral LFP. We found that the probability of beta bursts was significantly higher
in the second half compared to the first (ONH2-H1: 3.0%, p = 0.034, t17 = 2.3; OFFH2-H1: 6.7%,
p = 0.008, WSR-test (n=11)) and that the peak amplitude of these events did not differ (ONH2-
H1: 47%, p = 0.227, t16 = 1.3; OFFH2-H1: 57%, p = 0.224, t8 = 1.3; dfs were reduced here as
some halves did not contain any bursts).
Low force rates coincided with high beta power
Examples of single trials illustrated that high beta power could coincide with lower force
rates not just towards the end but also midway through a force adjustment (Supplementary
Fig. 1). Correlations between beta power and the simultaneously measured absolute force rate
(see Methods section on “Correlation between beta power and force”) were highly significant
at the group level (ON: mean rho = -0.06, p < 0.001, t17 = -4.7; OFF: mean rho = -0.08, p =
0.009, t10 = -3.3). The negative correlation shows that when beta power was high, the
absolute force rate tended to be low.
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We also tested if this correlation is significant against a permutation distribution that controls
for the tendency of beta power being high towards the end of an adjustment, where slowing
was often observed. It was still significant (ON: perm p = 0.002; OFF: perm p = 0.002).
Finally, we repeated the same analysis using the force level instead of the absolute force rate.
No significant correlation was present for the force level (ON: mean rho = 0.01, p = 0.184,
WSR-test (n=18), perm p = 0.262; OFF: mean rho = 0, p = 0.996, t10 = 0, perm p = 0.956).
Beta power was higher before the correct force level was reached when it was not followed
by overshoot
The increased amplitude of beta oscillations before the end of a force adjustment may play a
role in slowing down and stabilizing muscle activity just before the correct force level is
reached. If this was the case, beta power should be relatively reduced when a force
adjustment ended with an overshoot. Indeed, when we compared beta power within the final
400ms before the force adjustment ended (when no overshoot was present, versus the 400ms
before the force change reversed when the target force was overshot), we found that beta
power in the contralateral STN/GPi was significantly lower when the target was overshot in
both medication conditions (ON: p = 0.001, t17 = -4.0, OFF: p = 0.016, t10 = -2.9, also see
Supplementary Fig. 6). ON medication, this was also the case in the contralateral M1 and
ipsilateral LFP (ON: contra M1: p = 0.021, t17 = -2.5, ipsi LFP: p = 0.006, t17 = -3.2). Note
that adjustments that ended with an overshoot were less common than those that ended more
accurately (% overshoot trials ON: 41%, OFF: 36%).
In a similar vein, we also assessed if trials, in which beta activity was high, resulted in less
overshoot at the end of a force adjustment. We median-split trials into high and low beta
power trials, and found that this was the case for the contralateral LFP and M1 (ON: contra
LFP: p = 0.035, WSR-test (n = 18), ipsi LFP: p = 0.058, WSR-test (n = 18); contra M1: p =
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0.008, t17 = 3.0, ipsi M1: p = 0.300, t16=1.1; OFF: contra LFP: p = 0.009, t10 = 3.2, ipsi LFP: p
= 0.496, t10 = 0.7, contra M1: p = 0.100, t10 = 1.8, ipsi M1: p = 0.048, t9 = 2.3).
Note, though, that the force adjustments that ended with an overshoot were also distinct in
other properties. Overshoots were stronger when the peak force rate was higher (both p <
0.001, ON t17 = 7.5, OFF t10 = 5.4, pooled across increases and reductions) and when the
amount of force change was smaller (both p < 0.001, ON t17 = -4.3, OFF t10 = -6.4).
Beta M1-STN/GPi phase coupling decreased at the onset of force adjustments when
dopamine levels were relatively restored
Several studies have shown significant coupling between motor cortical regions and the basal
ganglia at beta frequencies (Hirschmann et al., 2011; Litvak et al., 2011; Oswal et al., 2016;
Tinkhauser et al., 2018; van Wijk et al., 2017). Thus, the difference in peak force rate
between the ON and OFF medication condition may also be accompanied by differences in
coupling between M1 and the basal ganglia nuclei. Phase coupling strength was quantified by
computing inter-site phase clustering (ISPC) values across time separately for each trial (see
Methods).
First, we tested if coupling strength was significantly higher than that observed by chance by
comparing the original ISPC values against a null-distribution created by shuffling the trial-
to-trial-association between the LFP and EEG signals 500 times. For example, the LFP signal
from trial 1 was paired with the EEG signal from trial 5. For each of the 500 permutations the
trial order of the EEG signal was completely shuffled before re-pairing it with the LFP signal.
ISPC values were computed within 600ms long windows centred around the following five
points: 1s before the start of the force adjustment, at the start, at the mid- point of the
adjustment, at the end, and 1s after the adjustment ended. 600ms was chosen as length,
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because if beta oscillations in both sites are relatively regular for several cycles, permutation-
ISPC values would also be relatively high (despite permuting the trials) when windows are
small and contain only a small number of cycles.
Fig. 4A shows significant coupling 1s before the force adjustments started and immediately
afterwards in both the ON and OFF medication conditions. When dopamine levels were
relatively restored (ON medication), beta coupling decreased at the onset of the adjustment to
a level where coupling strength did not exceed the chance level (p-value above 0.05). When
dopamine levels were low in the OFF medication condition, the reduction in coupling was
less pronounced and that which did occur was delayed to around the mid-points of the force
adjustments.
Next, we tested directly if coupling was significantly lower at the beginning of force
adjustments compared to the end (pooled across force increases and reductions) and if this
reduction in coupling at the onset of adjustments, also further referred to as “decoupling”,
was stronger ON medication. ISPCs were computed for each trial in a 200ms window
directly after the adjustment began and in a 200ms window directly before it ended. We
found that ON medication, coupling was significantly lower at the beginning of the
adjustment compared to the end (Fig. B, ON: p = 0.001, t17 = -4.2; OFF: p = 0.623, t10 = -0.5).
Additionally, when directly comparing ON versus OFF medication, the amount of decoupling
at the beginning was significantly stronger ON medication (StartON-OFF: p = 0.024, t10 = -2.7).
This difference was again present to a similar extent in both basal ganglia nuclei (two-sample
t-test on the ON-OFF ISPC differences between the GPi and STN: p = 0.467, t9=0.76).
Beta decoupling at the onset of force adjustments was similar for increases and reductions
(Supplementary Fig. 7), but the difference in coupling strength between ON and OFF
medication seemed to be more pronounced for force increases, which was also where the
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behavioural difference in the peak force rate was found. The differences in coupling strength
were specific for the contralateral hemisphere as no differences were present in coupling
between beta from the contralateral LFP and ipsilateral M1 or the ipsilateral LFP and
ipsilateral M1 (Supplementary Fig. 8+9).
When the peak force rate was high, cortico-basal ganglia beta phase-coupling was reduced –
but only ON medication
To test if the peak force rate itself was related to coupling strength, we median-split trials into
slow and fast force adjustments. The median-split was performed separately for the ON and
OFF medication condition. We only included trials in which the force was changed into the
correct direction without any inadvertent initial changes into the wrong direction. Coupling
was much reduced during faster compared to slower force adjustments, which was again
specific to the ON medication condition and the contralateral hemisphere (Fig. 5, ON p =
0.003, t17=-3.4, OFF p = 0.477, t10=0.7). This difference was not just due to the reduced
sample size in the OFF medication condition, because for a reduced set of patients the
difference still was significant in the ON medication recordings (subset ON: p = 0.042, t10=-
2.3).
Note that the difference in peak force rate between fast and slow trials also was significantly
higher ON medication (mean difference ON = 2.0 N/s, OFF = 1.7 N/s, p = 0.012, t10=3.1),
indicating a broader range of force rates.
When comparing beta power instead of beta coupling between trials with low and high peak
force rates, a power difference in the contralateral STN/GPi was only observed OFF
medication: Local beta power was earlier and more strongly suppressed when the adjustment
was faster (Supplementary Fig. 10).
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To corroborate our finding of a relationship between the absolute rate of the force adjustment
and inter-regional coupling, we computed within-subjects correlations between the peak force
rates and ISPCs as well as local power (in a 0.2s long window starting at the onset of the
force adjustment). In line with the above findings, the second-level test to see if the
correlations significantly differed from zero showed that ON medication, the peak force rate
was significantly correlated with the coupling strength (ON: mean rho = -0.07, p = 0.028, t17
= -2.4), which was not the case OFF medication (OFF: mean rho = 0.05, p = 0.364, t10 = 1.0).
Instead, OFF medication, the peak force rate correlated consistently with power in the
contralateral STN/GPi (mean rho = -0.14, p = 0.003, t10 = -3.9) and slightly less with power
in contralateral M1 (mean rho = -0.12, p = 0.045, t10 = -2.3). These correlations with beta
power were not significant when patients were ON medication (contra LFP: mean rho = -
0.07, p = 0.054, t17 = -2.1, contra M1: mean rho = -0.04, p = 0.329, t17 = -1.0). A summary of
all key findings is shown in Fig. 6.
Finally, to test if the ON-medication correlation between the peak force rate and ISPCs would
be diminished when controlling for local power, we computed partial correlations. The effect
diminished only slightly when controlling for power from the contralateral M1 (mean rho = -
0.06, p = 0.037, t17 = -2.3) and the contralateral STN/GPi (mean rho = -0.06, p = 0.064, t17 = -
2.0).
These findings suggest distinct effects of dopamine depletion on cortico-basal ganglia beta
coupling and local beta power. Altogether, this indicates that when dopamine levels are low,
beta coupling between the contralateral M1 and the basal ganglia is less flexibly modulated,
associated with a reduced dynamic range of how fast the adjustments were performed. When
patients were off medication, long-range coupling was not significantly modulated, which
was not merely related to the reduced sample size. Instead, low and high peak force rates
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were accompanied by differences in local beta synchronization, particularly in the
contralateral STN/GPi.
Taken together, beta power and beta coupling were relatively suppressed at the onset of force
adjustments but already began recovering before they ended and postural stabilization set in.
Power suppression was strongest when the force was reduced, which was performed faster.
Cortico-basal ganglia beta phase decoupling at the onset of force adjustments was significant
only ON medication, thus after dopamine withdrawal the flexibility of cortico-basal ganglia
coupling was strongly reduced.
Control analyses
Finally, we performed several control analyses. Larger force adjustments tend to be
performed faster, which was also the case in our task (correlation between the amount of
force change and peak force rates: mean rho ON = 0.52, OFF = 0.56, both p <0.001). But
importantly, no beta power differences were found in the contralateral LFP when splitting
trials into small and large force changes (Supplementary Fig. 11). The strength of cortico-
basal ganglia beta coupling also was not significantly modulated by the amount of force
change (Supplementary Fig. 12).
Larger force changes also tended to take longer (mean rho ON = 0.44, OFF = 0.39, both p <
0.001). Additionally, the rate of the force adjustments and their duration was anti-correlated:
When force adjustments were performed slowly, they took longer (mean rho ON = -0.22,
OFF = -0.25, both p < 0.001). When dividing them into adjustments with short and long
durations, a difference similar to the one when median-splitting trials according to the peak
force rate was found in the OFF medication condition: Beta power was higher when the
adjustment took longer (and when they were slower, Supplementary Fig. 13). However,
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cortico-basal ganglia coupling was not significantly modulated by differences in duration
(Supplementary Fig. 14).
Finally, we evaluated if the peak force rate differed between trials in which a visual distractor
stimuli (a second box coloured red instead of blue) was present or not. No significant
differences were found (ON increase: p = 0.173, t17 = 1.4; ON reduction: p = 0.616, WSR-test
(n=18), OFF increase: p = 0.753, t10 = -0.3; OFF reduction: p = 0.655, t10 = -0.5).
Discussion
Our task required patients to perform relatively small, finely controlled force adjustments
while continuously maintaining an active muscle tone to hold the pen and apply the visually
cued force level. We found that beta LFP power in the STN/GPi and cortico-basal ganglia
phase coupling was suppressed at the onset of force adjustments and that the change was
greater ON dopaminergic medication than OFF medication, in line with previous reports
(Androulidakis et al., 2007; Devos et al., 2006; Doyle et al., 2005).
We also showed a gradual beta power increase before the force adjustment was completed,
which may be linked to the controlled nature of the cued adjustments, as will be discussed in
more detail below.
Beta power suppression does not relate to the absolute applied force but to the force rate
Beta power suppression occurs during movement and has been shown to be larger for actions
that generate higher force levels (Fischer et al., 2017a; Tan et al., 2015, 2013). However,
rather than being inversely related to the force levels, we found beta suppression to be related
to the absolute force rate for both force increases and reductions. Thus, beta oscillations may
be related to a directionless measure, such as the rate of any change in force or, more broadly,
the movement effort – or subjective gain of a motor command (Tan et al, 2015). In addition,
in our experiment the peak force rate was faster when the force was reduced compared to
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when it was increased. Beta power decreased more strongly when the force was reduced,
consistent with the hypothesis that beta power suppression reflects the absolute force rate
rather than the force level.
Beta activity returned before the force adjustments were completed and was associated with
a reduced force rate and more precise completion
For both directions of force adjustments, we saw a gradual recovery of beta power before the
adjustment was completed in the trial average. Our paradigm was remarkable for the
controlled nature of isometric force adjustments – performance accuracy was stressed by the
examiner, and visual feedback was continuously provided to enable patients to produce the
cued target force with high accuracy. Accordingly, force adjustments took on average longer
than 600ms, despite the relatively small changes in force. The fact that an increase in beta
activity occurred before the adjustment was completed raises two non-exclusive possibilities.
First, it may play an active role in slowing down the adjustment and, in line with this, the
elevated STN/GPi beta power coincided with a slowing of the force rate. Second, the increase
in beta power occurring before the force adjustment was completed may play a role in the
integration of visual and proprioceptive feedback to achieve accurate visuo-motor control
(Tan et al., 2014b). The latter idea stems from studies on sensorimotor adaptation (Tan et al.,
2016, 2014b; Torrecillos et al., 2015), which have led to the hypothesis that a post-movement
increase in beta activity is linked to integration of sensory information and updating of an
internal forward model (Cao and Hu, 2016). Both ideas are consistent with our observation
that beta power was lower before patients overshot the target force, i.e. when they failed to
stabilize the adjustment fast enough. In the periods of sustained, stable isometric contraction,
beta power was not significantly suppressed relative to baseline beta activity recorded at rest.
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This again implies that suppression of beta oscillations does not merely occur when muscles
are tonically contracted but only when the strength of the contraction is adjusted.
Cortico-basal ganglia beta phase coupling was more readily modulated ON medication
We also investigated changes in long-range motor-cortical-basal ganglia beta phase coupling
and found evidence to suggest different organisation of processing ON and OFF medication.
We demonstrated that beta phase coupling was significantly reduced at the start of the force
adjustments when patients were ON medication. This was specific to M1-STN/GPi coupling
in the contralateral hemisphere. Stronger decoupling was associated with a higher peak force
rate when patients were ON medication. OFF medication, we only saw differences in local
STN/GPi power. This suggests that cortico-basal ganglia beta coupling is more dynamic
when dopamine levels are relatively restored. The improved flexibility in coupling in turn
may underscore the larger dynamic range of force rates observed in the ON medication state.
Notably, ON medication, slow and fast force adjustments were not associated with
pronounced differences in local beta power. This points towards the relative independence
between long-range phase coupling and local beta activity, which may be unmasked in this
study because of the special nature of the fine motor control task.
An interesting observation is that early symptoms of Parkinson’s disease often include
handwriting impairments (Pinto and Velay, 2015; Rosenblum et al., 2013). Handwriting
requires fine control over the hand. Considering that we observed an impaired dynamic range
of task-related cortico-basal ganglia beta phase decoupling when dopamine levels were low,
an early feature of Parkinson’s disease may be pathological alterations in the dynamic range
of coupling that may cause early impairments of fine motor control in the absence of gross
motor symptoms.
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Limitations
Several limitations in our study are worth highlighting. First, we cannot establish causality in
any of the relationships evidenced in this observational study. Second, we recorded motor
cortical EEG activity and local field potentials from the STN or the GPi, depending on each
patient’s implantation target. As none of our comparisons indicated differences between the
STN and the GPi, in agreement with previous reports of similar beta modulation between the
two structures (Brücke et al., 2012; Joundi et al., 2012), we did not distinguish between the
two sites. However, it could be argued that we were underpowered to detect any but the
biggest differences in LFP reactivity between the two targets. Third, as we were primarily
interested in the force dynamics, we pooled the data across all trials irrespective of the
presence of a visual distractor. However, to be sure that this was not a confounding factor, we
computed a control analysis and showed that the presence of a distractor did not result in
significant differences in force rates. Fourth, we cannot categorically ascribe the beta band
changes observed here to the STN or GPi alone. This is particularly the case for the eight
wide-field bipolar signals that were included in a small group of subjects because beta
modulation was more pronounced than in more focal contiguous bipolar contacts.
Finally, we did not detect any significant gamma power increase in our task, probably
because the required force adjustments were too small (Tan et al., 2013). Past studies
involving large, ballistic movements have related STN gamma and not beta power to
movement speed (Joundi et al., 2012; Lofredi et al., 2018). Our continuous low-force tracking
task may have made it possible to detect more subtle changes in beta activity while larger
movements might quickly result in a floor effect of beta power suppression.
Conclusions
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We have demonstrated that small, controlled force adjustments are accompanied by initial
beta desynchronization followed by increased beta activity closer to completion of the
adjustment. Cortico-basal ganglia beta phase coupling was significantly reduced at the start
of an adjustment, but only ON and not OFF medication, suggesting that the dynamic range of
cortico-basal ganglia coupling is impaired during dopamine withdrawal. Beta power
suppression and phase decoupling was most closely linked to the rate of the force adjustments
and not the force level per se. The appearance of beta synchronization instead may be linked
to the timely and precise stabilization of force that is required to perform the present
visuomotor force matching task.
Acknowledgements
This work was supported by the Medical Research Council [MC_UU_12024/1] and the EU
[FP7-ICT 610391]. PB was further funded by the National Institute of Health Research
Oxford Biomedical Research Centre. HT was additionally funded by Medical Research
Council [MR/P012272/1]. The Unit of Functional Neurosurgery is supported by the
Parkinson Appeal UK, and the Monument Trust.
Competing interests
The authors declare no competing interests.
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Figure captions
Figure 1 Examples of the pen force recordings. A Patients controlled the size of a black
square on the screen to match a blue square by varying the force of a pen on a graphics tablet.
In the example the force needs to be further increased to expand the black square. B The grey
line with sharp jumps shows the target force and the red line shows the force applied by
regulating the force of a pen on a graphics tablet. The black crosses show the start and end
points of an adjustment. C Behavioural results: Peak (absolute) force rate, adjustment
durations and reaction times (RT). For all measures, four pairwise comparisons were
performed: ON Increase vs. ON Decrease, OFF Increase vs. OFF Decrease, ON Increase vs.
OFF Increase, ON Decrease vs. OFF Decrease. P-values are FDR-corrected to correct for
multiple comparisons.
Figure 2 Beta power decreased at the time of a force adjustment. The data are aligned to
the midpoint of each force adjustment, averaged across all bipolar channels in the ON
medication condition. Power in each frequency band was normalized by the median of each
session. The black outlines show significant clusters obtained with a cluster-based
permutation procedure for multiple comparison correction (p<0.05). Black arrows indicate
the average time of the visual cue change.
Figure 3 Baseline-normalized beta power decreased at the beginning of a force
adjustment, regardless of direction, but gradually increased already before the
adjustment was fully completed. Force increases and reductions were compared with a
cluster-based permutation procedure to correct for multiple comparisons over the full time
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period and significant differences in beta power are shown as shaded areas in red. Relative
beta power was suppressed by both force increases and reductions, but in the contralateral
STN/GPi it stayed significantly more suppressed throughout the adjustment when the force
was reduced. Note that the actual time that passed between the start and end of the
adjustments was less than one second and varied across trials (see Methods on the temporal
subdivision of the force adjustments). The force traces have been normalised between 0 and 1
before averaging.
Figure 4 Contralateral M1-STN/GPi beta phase coupling. A P-values below 0.05 show
that coupling was significant compared to a permutation distribution (also after FDR-
correction). Note that ON medication (left), decoupling (where p>0.1) took place earlier,
already at the start of an adjustment, than OFF medication (right), where decoupling was
weaker and most pronounced in the middle of the adjustments. B When patients were ON
medication, phase coupling between the contralateral BG nuclei and M1 was significantly
reduced in the first 200ms of a force adjustment compared with the final 200ms (left).
Positive differences are plotted in green, negative differences in black. The degree of
decoupling in these first 200ms was significantly stronger ON medication compared with
OFF medication (right).
Figure 5 Low and high peak force rates relate to differences in coupling strength ON
medication, and to differences in local beta power OFF medication. Only contralateral
M1-STN/GPi coupling was significantly lower when force adjustments were rapid and
patients were medicated (top left). Positive differences are plotted in green, negative
differences in black.
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Highlights
Basal ganglia LFPs were recorded during continuous visuo-motor force control
Beta power decreased when the force was increased but also when it was reduced
Beta power was inversely related to the absolute force rate
Cortico-basal ganglia beta coupling was more readily modulated ON dopamine
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