Evaluating the Effect of Galvanic Vestibular Stimulation in Parkinson's disease via Microstate Resting State EEG Analysis
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurological disorder based on changes in dynamic brain activity, which can be partially ameliorated with invasive Deep Brain Stimulation. Galvanic vestibular stimulation (GVS), a non-invasive method, could potentially improve the motor symptoms of Parkinson's disease, but the mechanisms are unclear. Biomarkers based on the electroencephalogram (EEG) are being actively pursued. Here we examine the properties of EEG microstates as a potential GVS-sensitive EEG biomarker, whereby multichannel, broadband EEG signals are approximated by a sequence of discrete spatial patterns. We used the Microstate Analysis plugin for EEGLAB and compared the characteristics between healthy (n=20) and people with PD (n=22, stimulated/sham, and OFF Medication/ ON Medication). We extracted 25 Microstate related features from 4 different microstates (‘A’ - ‘D’) and examined their differences between groups (a healthy control group was considered as the reference to extract the feature values). Overall disease severity, as assessed by the clinical Unified Parkinson's Disease Rating Scale (UPDRS) Part 3, was predictable from microstate features. The duration of microstate A - selected by LASSO during UPDRS prediction- was significantly changed by both types of GVS stimuli (multi-sine 50–100 Hz (GVS1), and multi-sine 100–150 Hz (GVS2)), but not medication. The fraction of total recording time for microstate C, also a key feature in disease prediction, was found to be selectively affected GVS1 only. The above results suggest that GVS may provide benefits complementary to medication but in a stimulus-dependent manner. These results could potentially guide optimal GVS design in the pursuit of complementary therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".