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OUTLINE

 1.  Abstract
 2.  
 3.  Key words
 4.  Abbreviations
 5.  Introduction
 6.  Methods
 7.  RESULTS
 8.  Discussion
 9.  Conclusions
 10. Ethics statement
 11. Sources of funding
 12. Declaration of competing interest
 13. Data availability
 14. References

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FIGURES (4)

 1. 
 2. 
 3. 
 4. 




TABLES (2)

 1. Table 1
 2. Table 2




ARCHIVES OF REHABILITATION RESEARCH AND CLINICAL TRANSLATION

Available online 27 August 2024, 100368
In Press, Journal Pre-proofWhat’s this?

ORIGINAL RESEARCH
THE EFFECT OF SENSORY REWEIGHTING ON POSTURAL CONTROL AND CORTICAL ACTIVITY IN
PARKINSON'S DISEASE: A PILOT STUDY

Author links open overlay panelMaryam Sadeghi MS 1, Thomas Bristow BS 2, Sodiq
Fakorede BS 1, Ke Liao PhD 3, Jacqueline A. Palmer PhD 4, Kelly E. Lyons PhD 5,
Rajesh Pahwa MD 5, Chun-Kai Huang PhD 1 6, Abiodun Akinwuntan PhD, FACRM 1 6 7,
Hannes Devos PhD, FACRM 1 6
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ABSTRACT


OBJECTIVE

To investigate the effects of sensory reweighting on postural control and
cortical activity in individuals with Parkinson's disease (PD) compared to
age-matched controls using a virtual reality sensory organization test (VR-SOT).


DESIGN

Cross-sectional pilot study.


SETTING

University research laboratory.


PARTICIPANTS

Ten participants with idiopathic Parkinson's disease and eleven age- and
sex-matched control participants without neurological disorders.


INTERVENTIONS

Not Applicable.


MAIN OUTCOME MEASURES

Changes in center of pressure (COP) and electroencephalography (EEG) activity
(i.e., power) in the alpha band and the theta/beta ratio recorded during the
VR-SOT were the main outcome variables.


RESULTS

PD participants exhibited greater COP displacement, particularly in the
mediolateral direction across sensory conditions. They also showed increased
alpha power when relying on visual inputs and increased theta/beta ratio power
when depending on somatosensory inputs.


CONCLUSION

PD affects sensory reweighting mechanisms involved in postural control, as
evidenced by greater COP displacement and altered cortical activity. These
findings emphasize the potential of EEG and VR-SOT in understanding and
monitoring postural control impairments in PD.



KEY WORDS

Parkinson's disease
sensory reweighting
postural control
electroencephalography
virtual reality
center of pressure


ABBREVIATIONS

PD
(Parkinson's Disease)
COP
(Center of Pressure)
EEG
(Electroencephalography)
VR-SOT
(Virtual Reality Sensory Organization Test)


INTRODUCTION

Postural imbalance is a hallmark symptom of Parkinson's disease (PD)1. Several
clinical tests, such as the Berg Balance Scale, the Tinetti Balance Test, the
BESTest, or the Mini-BESTest, have been developed and validated to identify
balance impairments and determine the risk of falls in individuals with PD2.
Although these tests include assessments of both static and dynamic postural
control3, they provide limited insights into the specific sensory systems that
contribute to postural control.
The Sensory Organization Test (SOT) was designed to quantitatively evaluate the
ability to maintain postural control by incorporating visual, vestibular, and
somatosensory inputs4. The SOT uses sensory reweighting in which the brain
adjusts the relative importance of different sensory systems to sustain
balance5,6. This sensory reweighting is compromised in PD, indicating
difficulties in distinguishing and selecting reliable information from various
sensory systems to ensure postural control4,7. Particularly, individuals with PD
show increased postural sway when relying predominantly on the inputs of the
somatosensory and vestibular systems8, 9, 10, 11. These studies suggest that
individuals with PD exhibit changes in central processing of somatosensory and
vestibular information, resulting in increased reliance on vision to maintain
balance. Yet, the cortical processes involved with sensory reweighting of
postural control in PD have not been investigated.
Mobile neuroimaging technology is increasingly used to elucidate the cortical
processes related to postural control in healthy adults and adults with
neurological conditions12,13,14. Electroencephalography (EEG) utilizes
electrodes placed on the scalp to measure voltage potential differences between
two locations on the scalp15. These EEG signals can then be decomposed into
functionally distinct frequency bands, such as delta (0 – 4 Hz), theta (4 – 7
Hz), alpha (8 – 12 Hz), beta (13 – 30 Hz), and gamma (>30 Hz). The activity in
these frequency bands is referred to as power. Alpha power and the ratio of slow
(theta) to fast wave (beta) activity are of particular interest to study the
cortical processes of postural control16,17. Changes in alpha power appear to
reflect the sensory and movement-related information processing of postural
control18. In young adults, occipital alpha power increased when standing with
the eyes closed19,20. Theta/beta ratio power reflects cognitive activity related
to attentional control and executive function21. Theta/beta ratio power
increased while standing with the eyes closed and while standing while
performing a dual task in neurotypical young adults20. In older adults,
theta/beta ratio power increased during dual-task standing compared to
single-task standing21. In PD, participants with balance impairments showed
decreased mid-frontal and mid-cerebellar theta power while standing with the
eyes open compared to those with no balance problems and neurotypical older
adults22. The results of this previous study22 warrant further assessment to
elucidate the cortical processes involved in the sensory organization of
postural control in PD.
The primary objective of this pilot study was to investigate the effects of
sensory reweighting on postural control and cortical activity in individuals
with PD compared to age-matched older adults. We hypothesized that individuals
with PD would exhibit greater CoP displacement and increased cortical activity
during the SOT than older adults, with a specific focus on alpha power and the
theta/beta ratio as key indicators of the cortical contribution to sensory
reweighting in postural control.


METHODS


PARTICIPANTS

Ten participants with a diagnosis of idiopathic PD were recruited from the
Parkinson's disease and Movement Disorder Clinic at the University of Kansas
Medical Center. Eleven control participants were age- and sex-matched with PD
participants and were recruited from the community. Inclusion criteria were the
ability to comprehend and follow instructions in English, ability to stand
without assistive devices, scoring more than 20 on the Montreal Cognitive
Assessment (MoCA)23, and the participant with PD being in the medication on
state. Exclusion criteria were a diagnosis of severe cognitive impairment or
dementia, decreased visual acuity or loss of visual fields that cannot be
resolved with corrective lenses, severe head and trunk dystonia or dyskinesia in
the medication on state, blepharospasm, unpredictable motor fluctuations, deep
brain stimulation, and presence of any musculoskeletal conditions which could
affect standing and balance activities. This cross-sectional pilot study was
approved by the University of Kansas Medical Center Institutional Review Board
(#00148555).


PROTOCOL

All participants provided informed consent before starting the study procedures.
Relevant demographic information such as age, sex, education level, disease
duration, disease severity (measured using the Hoehn and Yahr (H&Y) scale)24 and
medication details were extracted from medical records. Medication dose was
converted into a Levodopa Equivalent Daily Dose (LEDD)25. The Montreal Cognitive
Assessment (MoCA) was administered as a general screen of cognitive function23.
In addition, we recorded the participant-reported number of falls in the six
months prior to their visit. All experiments were conducted when participants
were in their optimal medication state (ON), about 45 minutes after medication
intake.
We used the virtual reality-based Comprehensive Balance Assessment and Training
(VR-COMBAT) system to administer the virtual reality SOT (VR-SOT)26. The
VR-COMBAT system includes a processing computer (Alienware, Dell)a, a
VR-integrated head-mounted device (VR-HMD) from HTC VIVE Pro Eyeb, and VR
tracking sensors (Steam VR Base Stations, HTC)b. The HTC VIVE Pro Eye features
dual OLED displays (2,880 × 1,600 pixels) with a 90 Hz refresh rate and a
110-degree field of view. The Steam VR software (version 1.13, Valve) links the
computer and VR headset. The head-mounted device integrates with an AMTI Optima
force plate (Watertown, MA)c, synchronized for precise measurements during
trials (Figure 1).
 1. Download: Download high-res image (732KB)
 2. Download: Download full-size image

Figure 1. Demonstration of the virtual reality comprehensive balance assessment
and training system with mobile electroencephalography left: Participant
standing on a firm platform & right: Participant standing on foam. Participant
signed a photo and video release form.


EXPERIMENTAL PROCEDURE

Participants were instructed to remove their shoes and socks and stand barefoot
on an AMTI Optima force plate (Watertown, MA, United States). They were asked to
position their heel centers 17.5 cm apart with their feet oriented at 14°. Each
participant was fitted with a safety harness attached to an overhead anchor for
security during testing. A trained spotter was positioned behind each
participant to ensure safety throughout the procedure.
The VR-SOT comprises six different conditions that mimic the six conditions of
the Equitest® SOT. Condition 1 serves as a benchmark for assessing static
balance on a stable surface with fixed VR surroundings. The panels in the VR
environment do not move. Participants can use the feedback of the visual,
somatosensory, and vestibular systems to keep their balance. Condition 2
provides the same balance testing condition as to condition 1. However,
participants must rely on somatosensory inputs to remain upright since the VR
surroundings are blackened out27. In condition 3, the surrounding panels are
moving in the anteroposterior direction with a maximum of 20 degrees and a
maximum velocity of 15 degrees/sec. This condition creates a conflict between
normal input from the somatosensory and vestibular systems and the visual
information from the moving VR panels. Conditions 4, 5, and 6 mirror the
parameters of conditions 1, 2, and 3, respectively. However, in conditions 4, 5,
and 6, a foam (19 × 15 × 1.5 inch) is placed between the feet and the force
plate, thus challenging the somatosensory system. In condition 4, participants
must rely on visual inputs to maintain balance, whereas in condition 5,
participants must rely on vestibular inputs27,28. Condition 6 generates a
conflict between visual, somatosensory and vestibular systems.
Each of the six conditions comprised three trials, with each trial lasting 20
seconds. Between each trial, a 5-second break was given. Data of the three
trials were averaged.


DATA CAPTURE AND PROCESSING

The center of pressure (COP) data were obtained from the force plate at 200 Hz
and processed using the MATLAB software (MathWorks, Natick, MA)29 to compute the
following parameters30:
 * •
   Mean displacement in the anterior-posterior (AP) or medial-lateral (ML)
   direction (MeanAP or MeanML): The average displacement of the COP from its
   mean position in the AP/ML direction.
 * •
   Mean velocity in the AP or ML direction (VelAP or VelML): The average speed
   at which the COP moves in the AP/ML direction.
 * •
   Average frequency in the AP or ML direction (MfAP or MfML): The rotational
   frequency (in Hz) of the COP as it completes a full circle with a radius
   equal to the mean displacement.


EEG DATA ACQUISITION AND PROCESSING

To capture cortical activity, we used the mobile EXG system from Mentalab
(Munich, Germany)d. Eight electrodes (seven dry brush electrodes on the scalp
and one flat wet reference electrode) were wired to the Mentalab Explore hub
attached to the back of the EEG net. The Explore device captures the scalp
electrical activity at 500 Hz and transmits the signals to the laptop via
Bluetooth. Electrode placement included the midline channels Fz, Pz, Cz, Oz, the
frontal channels Fpz and Fp1, the central channel Fcz, and the reference
electrode on the right mastoid TP10, according to the International 10-20
system. Impedance was kept below 50 kΩ for the recording. Feasibility testing
ensured that placing the VR apparatus over the EEG net did not compromise EEG
recording.
The EEG data underwent filtering from 0.1 to 30 Hz using the EEGlab31 plugin
within the MATLAB software32. To reduce noise, EEG recordings were trimmed 10
seconds before the trials began and 10 seconds after the last trial ended. The
continuous EEG data were segmented into six datasets for each condition.
EEGLAB31 automatic channel rejection was used to initially detect the noisy
channels. In detail, the pop_rejchan() function from EEGLAB was used, in which
the probability of each EEG channel was calculated as the rejection measurement
with z-score threshold as 5. Then, the data were visually inspected to mark
those channels with extreme noise. Finally, the Artifact Subspace Reconstruction
(ASR) method33 from the EEGLAB clean_rawdata plugin
(https://github.com/sccn/clean_rawdata) was used to reject bad data periods. The
standard deviation cutoff for removal of bursts was chosen from 1 to 20
(mean/standard deviation: 8.61/6.15). The cleaned data were manually inspected
again to be included into power calculation. A dataset was excluded from further
data analysis if any of its channels was rejected as bad channel. To calculate
the theta (4–7 Hz), alpha (8–13 Hz), and beta (13–30 Hz) frequency band power
for all six conditions, EEGlab's Spectopo function was used to extract power
spectral density from all electrodes, which uses Welch's method on the 1 s
epochs with 50% overlap between its calculation windows.


STATISTICAL ANALYSIS

All statistical analyses were conducted using SAS software (version 9.4, Cary,
NC). Normality of data distributions was assessed through the Shapiro–Wilk test
and visual inspection of the histogram and Q-Q plots. We used Fisher's Exact
tests and independent t-tests to compare demographic and clinical variables
between groups.
We calculated differences in sensory reweighting on COP and cortical activity
between participants with PD and healthy controls (HC) using linear mixed
models. We used a random intercept term and a subject-specific coefficient to
adjust for correlation between measures within subjects. The linear mixed model
included the main effects of group (PD – HC), condition (1 – 6), and the
interaction effect of group*condition. To minimize the risk of type 1 errors, we
only compared (1) condition 2 to condition 1 to evaluate the reweighting of
postural control to the somatosensory system; (2) condition 4 to condition 1 to
evaluate the reweighting of postural control to the visual system; (3) and
condition 5 to condition 1 to evaluate the reweighting of postural control to
the vestibular system28,34. We only report the results of the interaction
effect. Finally, we correlated COP with EEG measures for each group separately
using Pearson r correlations. The significance threshold for all analyses was
set at alpha = 0.05.


RESULTS


DEMOGRAPHIC AND CLINICAL VARIABLES

No differences were found in demographic and clinical characteristics between
groups, except for the MoCA score (Table 1). Participants with PD scored
slightly lower on the MoCA compared to controls. The disease duration and the H
& Y stages indicated that participants with PD were in the very mild to moderate
stage of the disease.

Table 1. Comparison of demographic and clinical characteristics between groups.

VariablesPD (n=10)HC (n=11)P valueAge, years72.30 ± 8.7769.90 ± 3.270.43 aSex,
f:m3:74:70.45 bMoCA score, /3025.20 ± 2.0428.27 ± 1.490.0008 aEducation,
years15.71 ± 2.214.7 ± 2.050.44 aDisease duration, years2.11 ± 3.07N/AN/AHoehn
and Yahr (ON), stage 0:1:2:31:2:5:2N/AN/ALEDD, mg/day298.8 ± 258.7N/AN/AFalls in
previous 6 months, yes: no5:5N/AN/ANumber of falls in previous 6 months,
0:1:2:30:3:1:1N/AN/A

Abbreviations: LEDD: Levodopa Equivalent Daily Dose; MoCA, Montreal Cognitive
Assessment; PD: Parkinson's disease; HC: healthy controls. Continuous variables
are expressed as means ± standard deviations; dichotomous variables are
expressed as frequencies. aIndependent t-test
b
Fisher's Exact test

Table 2. Comparison of COP variables across the six VR-SOT conditions between PD
(n = 10) and HC (n = 11)

VariableGroupsMeanAP (mm)MeanML (mm)VelAP (mm/s)VelML (mm/s)MfAP (Hz)MfML
(Hz)Condition 1PD11.87 ± 1.121.58 ± 1.0827.47 ± 3.8817.36 ± 2.772.38 ± 1.751.89
± 1.02HC4.56 ± 2.031.23 ± 1.6220.45 ± 5.3911.29 ± 2.690.94 ± 0.331.79 ±
1.00Condition 2PD25.84 ± 8.7911.06 ± 1.7329.36 ± 4.6017.74 ± 2.802.29 ± 1.002.17
± 0.94HC8.43 ± 5.742.42 ± 1.3024.25 ± 5.6411.76 ± 2.751.05 ± 0.321.65 ±
0.79Condition 3PD24.52 ± 7.8912.93 ± 1.0834.65 ± 16.6617.47 ± 2.102.29 ±
0.962.06 ± 0.70HC10.83 ± 5.464.50 ± 2.2624.23 ± 4.4712.97 ± 3.541.11 ± 0.251.64
± 0.61Condition 4PD21.49 ± &.5913.98 ± 3.7742.64 ± 12.5331.77 ± 11.002.52 ±
1.512.21 ± 1.23HC6.21 ± 1.834.07 ± 1.8222.27 ± 5.0012.55 ± 2.570.98 ± 0.231.68 ±
0.80Condition 5PD44.96 ± 23.7419.53 ± 5.4141.78 ± 9.8323.34 ± 4.522.69 ±
1.112.55 ± 0.82HC9.49 ± 5.155.25 ± 1.8026.33 ± 5.1314.46 ± 3.071.67 ± 0.321.68 ±
0.77Condition 6PD35.14 ± 15.5817.09 ± 4.4039.41 ± 15.2720.36 ± 3.532.02 ±
1.402.10 ± 0.71HC24.91 ± 15.588.67 ± 3.7424.76 ± 8.4714.18 ± 2.781.15 ± 0.331.62
± 0.52

MeanAP/MeanML: Mean displacement of the center of pressure (COP) in the
anteroposterior/mediolateral direction; VelAP/VelML: Mean velocity of the center
of pressure (COP) in the anteroposterior/mediolateral direction; MfAP/MfML: Mean
rotational frequency of the center of pressure (COP) in the
anteroposterior/mediolateral direction; PD: Parkinson's Disease; HC: healthy
controls. Variables are expressed as mean ± standard deviation. Condition 1:
stable surface with fixed VR surrounding; Condition 2: stable surface with
blacked out VR surroundings; Condition 3: stable surface with VR visual
conflict; Condition 4: unstable surface with fixed VR surroundings; Condition 5:
unstable surface with blacked out VR surroundings; and Condition 6: unstable
surface with VR visual conflict.
Initially, our study enrolled 12 participants with PD and 12 control
participants. During the postural balance conditions, two participants with PD
lost their balance and requested support from the spotter. Of those, one person
with PD discontinued in condition 5; the other person with PD lost balance
during condition 4 but completed all conditions. One control lost balance in
condition 4 and discontinued. Data from these participants were excluded from
the final analysis. Consequently, the final sample size included in our analyses
was reduced to 10 PD and 11 control participants.


COP VARIABLES (TABLE 2)

COP DISPLACEMENT

The analysis of mean AP displacement demonstrated a significant group by
condition interaction effect (F = 8.31, p < 0.001). Post-hoc analysis revealed
that this interaction was significant for condition 5 (unstable surface, blacked
out VR) relative to condition 1 (stable surface, fixed VR), producing a p <
0.001. Participants with PD exhibited greater displacement in the AP direction
compared to controls when relying on vestibular inputs to maintain balance
(Figure 2A).
 1. Download: Download high-res image (649KB)
 2. Download: Download full-size image

 1. Download: Download high-res image (622KB)
 2. Download: Download full-size image

Figure 2. Comparison of center of pressure data using box-and-whisker and
raincloud plots between participants with PD (n = 10) and HC (n=11). AP,
anteroposterior; ML, mediolateral.

Abbreviations: PD: Parkinson's disease; HC: healthy controls.
Similarly, the analysis of mean ML displacement demonstrated a significant group
by condition interaction effect (F = 7.25, p < 0.0001). Post-hoc analysis
indicated that this interaction was significant for condition 2 (stable surface,
blacked out VR; p = 0.03), condition 4 (unstable surface, fixed VR; p = 0.002),
and condition 5 (unstable surface, blacked out VR; p < 0.0001) relative to
condition 1 (stable surface, fixed VR). Individuals with PD displayed greater ML
displacement compared to controls when relying on either somatosensory, visual,
or vestibular inputs (Figure 2B).

COP VELOCITY

The analysis of AP velocity approached significance for group by condition
interaction effect (F = 2.01, p = 0.08). No post-hoc effects were therefore
calculated.
The analysis of ML velocity demonstrated a significant group by condition
interaction effect (F = 7.64, p < 0.001). Post-hoc analysis indicated that this
interaction was significant for condition 4 (unstable surface, fixed VR; p <
0.001) relative to condition 1 (stable surface, fixed VR). Individuals with PD
displayed greater velocity of the COP in the ML direction compared to controls
when the relative contribution of the visual system to postural control was
being tested (Figure 2D).

COP FREQUENCY

The analysis of frequency outcomes did not demonstrate any significant group by
condition interaction effects (Figure 2E and Figure 2F).


EEG VARIABLES

ALPHA POWER

The analysis of alpha power demonstrated a significant group by condition
interaction effect (F = 3.50, p = 0.005). Post-hoc analysis revealed that this
interaction was specifically significant for condition 4 (unstable surface,
fixed VR; p = 0.003) relative to condition 1 (stable surface, fixed VR).
Participants with PD (5.97 ± 7.23 µV/Hz2) exhibited increased alpha power
compared to controls (3.49 ± 4.41 µV/Hz2) when relying on visual inputs of
postural control (Figure 3A).
 1. Download: Download high-res image (571KB)
 2. Download: Download full-size image

Figure 3. Comparison of EEG power using box-and-whisker and raincloud plots
between participants with PD (n = 10) and HC (n=11).

Abbreviations: PD: Parkinson's disease; HC: healthy controls.
Since participants with PD exhibited cognitive impairment, we repeated the
linear mixed model while adjusting for MOCA scores. Although MOCA scores were
associated with alpha power (F = 4.59, p < 0.001), the interaction effect of
group by condition remained significant (F = 3.74; p = 0.0007), with post-hoc
effects for condition 4 (unstable surface, fixed VR; p = 0.0004) relative to
condition 1 (stable surface, fixed VR).

THETA/BETA RATIO POWER

Similarly, the analysis of theta/beta ratio power demonstrated a significant
group by condition interaction (F = 3.77, p = 0.003). Post-hoc analysis
indicated that this interaction was significant for condition 2 (stable surface,
blacked out VR; p = 0.01) relative to condition 1 (stable surface, fixed VR).
Individuals with PD (5.14 ± 3.27) displayed increased theta/beta ratio power
compared to controls (2.95 ± 2.51) when the relative contribution of the
somatosensory system was being tested.
We repeated the linear mixed models while adjusting for MOCA scores. MOCA scores
were not associated with theta/beta ratio power (F = 0.59; p = 0.79), and the
interaction effect of group by condition remained significant (F = 3.20;
p = 0.005), with post-hoc effects for condition 2 (p = 0.03) relative to
condition 1.
Figure 4 shows the topographical maps of alpha power of condition 4 (unstable
surface, fixed VR) – condition 1 (stable surface, fixed VR) and theta/beta ratio
power of condition 2 (stable surface, blacked out VR) – condition 1 (stable
surface, fixed VR) in participants with PD and controls.
 1. Download: Download high-res image (862KB)
 2. Download: Download full-size image

Figure 4. Topographical maps of alpha power (µV/Hz2) during visual system
testing and theta/beta ratio power during somatosensory system testing in the PD
group (n = 10) and the HC group (n = 11).

Abbreviations: PD: Parkinson's disease; HC: healthy controls.


DISCUSSION

The purpose of this study was to examine the impact of sensory reweighting of
postural control on center of pressure (COP) and cortical activity in
individuals with Parkinson's disease (PD) compared to age-matched healthy older
adults. We observed two key findings: (1) participants with PD displayed greater
mediolateral (ML) displacement when relying on somatosensory, visual, or
vestibular inputs; and (2) participants with PD exhibit greater cortical
activity when relying on somatosensory and visual inputs of postural control
compared to healthy controls.
Participants with PD exhibited greater COP displacement and velocity
particularly in the ML direction, compared to the control group. While increased
postural sway in the anteroposterior (AP) direction is more noticeable with
higher postural control task difficulty, both PD and control groups responded
similarly to sensory adjustments in the AP direction. Previous studies have
indicated that individuals in the early stage of PD demonstrated the ability to
use sensory inputs for postural control in the AP direction effectively35, 36,
37. However, they showed reduced ability to quickly adapt postural control to
changing sensory conditions38. In contrast, individuals with PD showed increased
COP displacement in the ML direction when relying on either somatosensory,
visual, or vestibular inputs. Previous studies suggesting that ML sway is more
sensitive than AP sway in detecting postural instability, detecting disease
progression, or risk of falls in PD39,40.
We hypothesized that the increased COP displacement during sensory reweighting
is linked to increased cortical activity. Indeed, participants with PD exhibited
increased alpha power when relying primarily using the visual system, and
increased theta/beta ratio power when primarily relying on the somatosensory
system for postural control.
Increased alpha power in PD participants when relying on visual inputs suggests
heightened cortical activity associated with visual processing. The increased
alpha power may reflect either impaired central processing of visual cues, or
potentially compensating for deficits in other sensory modalities. This finding
aligns with the notion that patients with PD may depend more heavily on visual
information to maintain balance, due to impaired proprioceptive and vestibular
functions.41 Theta/beta ratio power increased in participants with PD when
relying on somatosensory cues42. Occlusion of the dominant visual system likely
prompted participants with PD to focus more attention on maintaining postural
control, resulting in increased theta/beta ratio power42. In line with the
Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) model43,
individuals with PD might engage additional cortical regions or increase
activation in specific areas to compensate for postural control impairments. An
alternative explanation for the enhanced cognitive activity may be that
participants with PD exhibited mild cognitive impairment. The average MOCA score
in the PD group was 25.20, which is below the threshold for normal cognition (26
and above).44 We adjusted for differences in MOCA scores in the statistical
analyses. After such adjustment, the increased cortical activity persisted in
the PD group. This result implies that difficulties with postural control,
independent of changes in cognitive function, are associated with heightened
cortical activity in people with PD.
Although preliminary, our findings suggest that COP displacement, particularly
in the mediolateral direction, and changes in EEG alpha power and theta/beta
ratio, could help in the early identification of postural balance impairment and
falls risk in individuals with PD. Our findings may also inform the development
of targeted interventions, including balance training programs that emphasize
sensory integration and real-time EEG biofeedback to monitor and adjust
treatment strategies.


STUDY LIMITATIONS

While the study presents the first link between cortical activity and sensory
reweighting in PD, caution is necessary due to the relatively small sample size
of this study26. The study may have been underpowered to identify real
differences between groups. In addition, the findings may not be generalized to
the population of people with PD until the results have been confirmed in a
larger sample size. Many VR systems currently on the market (e.g., Bertec®,
Virtualis®, UprightVR) or used for research have found the VR-SOT to be valid
compared to traditional SOT45,46. However, caution is warranted to directly
compare the outcomes of VR-SOT with traditional SOT47. Future studies should
encompass larger sample sizes, a wider range of PD severities, and subtypes for
a more comprehensive understanding how PD affects cortical processes of sensory
reweighting. Particularly, the robustness of our findings should be tested in a
group of PD patients with no cognitive impairments. The study results are also
sufficiently encouraging to explore the use of EEG as an early marker of balance
impairment in PD. Future studies should evaluate the temporal association
between changes in cortical activity and balance impairment in PD.


CONCLUSIONS

In conclusion, our study provides preliminary insights into the challenges faced
by individuals with PD in adjusting postural control in response to changes in
sensory inputs, particularly in the ML direction. These exploratory findings
underscore the potential role of EEG in detecting subtle changes in cortical
activity associated with balance impairments in PD. The increased cortical
activity observed in PD participants when relying on somatosensory and visual
inputs highlights the need for further investigation into the neural mechanisms
underlying postural control in this population. Future studies are encouraged to
build on this foundation, exploring the clinical implications of sensory
reweighting and cortical activity changes in PD for improving balance and
reducing fall risk.


ETHICS STATEMENT

The research involving human participants underwent a review and received
approval from the Institutional Review Board (IRB) at the University of Kansas
Medical Center. All participants provided their explicit written consent to take
part in this study.


SOURCES OF FUNDING

This study was funded in part by the Mabel A. Woodyard Fellowship in
Neurodegenerative Disorders (M.S.). M.S. and T.B. received support from the NIH
T32 HD057850 Kansas University Training Program in Neurological and
Rehabilitation Sciences.


DECLARATION OF COMPETING INTEREST

There are no conflicts of interest to report by any of the authors.
Recommended articles


DATA AVAILABILITY

 * Authors will make available all raw data supporting their conclusions without
   undue delay.




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