EEG-based biomarkers for galvanic vestibular stimulation (GVS) effects in Parkinson’s disease
Bibliographic record
Abstract
Abstract Background: The effects of GVS in PD appear to be stimulus- and subject-dependent. However, determining the behavioral effects (e.g. on motor vigor (MV)) from the infinite number of potential stimuli would take an intractably long time. By assessing the EEG, a high-dimensional intermediate step between (i.e., from {GVS -> behavior} to {GVS -> EEG -> behavior}, we propose to rapidly assess the effects of GVS on the EEG and/or predict the behavioral effects of EEG changes. Objective: To explore multiple complementary GVS related biomarkers, including EEG Microstates, Phase Amplitude Coupling (PAC), and Deep Koopman-based EEG analysis. Methods: Data were collected from 20 healthy control (HC) and 18 PD adults performing a delayed squeeze bulb task with sham or multi-sine (50-100 Hz "GVS1" or 100-150 Hz "GVS2") stimuli. Time from the onset of the “Go” cue to the peak of maximum pressure in the squeeze bulb was taken as a metric for MV. We took the EEG 1-s prior to the peak time to predict MV. We employed the Microstate Analysis plugin for EEGLAB. For the PAC biomarker, a deep convolutional neural network based on transfer learning from the VGG-16 architecture was used to estimate MV. To implement a Deep Koopman approach, we examined the EEG time locked to the “Go” signal. Results: For the microstate analysis biomarker, the GVS2 stimulus had similar effects in PD subjects as dopaminergic medication. The PAC-based biomarker predicted MV with mean absolute error of 54.8 msec. There was a positive correlation between movement time in PD subjects and their similarity of their EEG Koopman trajectories to HCs. Discussion: Despite MV normally assumed to be encoded in the basal ganglia, EEG biomarkers can be used to both predict MV, and infer GVS effects. These, and other EEG-based biomarkers, will streamline efforts for precision stimulus design. Research Category and Technology and Methods Translational Research: 15. Electroencephalography (EEG) Keywords: Parkinson's disease, Galvanic vestibular stimulation, Microstates, Phase Amplitude Coupling
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".