Predicting Motor Vigor from EEG Phase Amplitude Coupling (PAC) in Parkinson’s Disease: Effects of Dopaminergic Medication and Non-invasive Modulation
Bibliographic record
Abstract
Abstract Impaired motor vigor is a defining characteristic of Parkinson’s disease (PD), yet the underlying brain mechanisms governing motor vigor (MV) remain unclear. Recent studies have suggested beta-gamma Phase-Amplitude Coupling (PAC) derived from the resting-state electroencephalogram (EEG) is a potential biomarker for PD that is modulated by Deep Brain Stimulation (DBS) and L-dopa treatment. Specifically, PAC has been suggested to be a marker of transitions between motor movements, as opposed to encoding the vigor of the current movement. Here, we comprehensively investigate the potential of various PAC interactions—across different frequency pairs—beyond the linear approaches typically employed to predict MV during motor tasks in PD and study the effects of dopaminergic medication and non-invasive Galvanic Vestibular Stimulation (GVS). We recorded EEG data from 20 PD patients and 22 healthy controls executing a simple, overlearned handgrip task. Subjects were tested on and off L-dopa medication and with and without GVS (multi-sine either 50-100Hz, 100-150Hz). In a preliminary linear (LASSO-based) analysis comparing various PACs and a broad range of commonly used EEG features, PAC features were found to be crucial for predicting MV approximately equally in PD and HC. Initial findings from the linear analysis showed PAC as a significant indicator for MV in both groups, although with variability in cross-validation that implied a complex, non-linear relationship between PAC and MV. To extensively investigate the PAC-MV relation, we used a deep convolutional neural network (PACNET)—developed based on pre-trained VGG-16 architecture—to estimate MV from PAC values. In both PD and HCs, delta-beta, theta-, alpha-, and beta-gamma PACs were important for MV prediction. In PD subjects, GVS affected delta-beta, theta-gamma-, and beta-gamma PACs role in MV prediction, which was sensitive to different GVS stimulation parameters. These PACs were also relevant for PD patients’ MV prediction after L-dopa medication. This study supports the hypothesis that EEG PAC across multiple frequency pairs, not just beta-gamma, predicts MV and not just motor transitions and can be a biomarker for assessing the impact of electrical stimulation and dopaminergic medication in PD. Our results suggest that PAC is involved in MV, in addition to a range of previously reported cognitive processes, including working and long-term memory, attention, language, and fluid intelligence. Non-linear approaches appear important for examining EEG PAC and behavior relations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".