A Deep Learning Approach to Determine Age-related EEG Features in Parkinson's Disease
Bibliographic record
Abstract
Oscillatory biomarkers are useful for development of Brain-computer interface (BCI) and EEG-based neuro-feedback systems, which may have therapeutic implications for seniors and those with the disease. Although many biomarkers for age and disease exist, the EEG has the benefit that it is widely available, inexpensive, and potentially may act as a biomarker over rapid time scales, which might be beneficial during, e.g., the performance of a specific task such as neurofeedback games. Parkinson's disease (PD) is ideally suited for the exploration of oscillatory biomarkers since abnormal oscillations have been widely implicated in the pathophysiology of PD. Specifically, beta-band oscillations may be broadly considered “anti-kinetic” and seen as inhibiting movement, while gamma-band oscillations are considered “pro-kinetic” and appear to facilitate movement. However, many domain-based EEG features in people with PD overlap considerably with those just seen in normal aging. Here, we contrast the age-related EEG features in PD subjects and age-matched healthy controls (HC). We employed an end-to-end training strategy and built deep recurrent neural network models with Long Short-Term Memory (LSTM) cells to predict age from 60-s of rest EEG recorded from PD and HC. When reliable models with reasonable errors were found for both groups ($MAE=1.897$for HC and$MAE=2.172$for PD), we investigated their deterioration of predictive power when fed frequency band-limited data. In PD subjects, beta and gamma bands in channels T7, FP2, and F7 were significantly more important for predicting age in PD than in HC. After medication, differences in the frequency bands predicting age between PD and controls become more prominent when PD subjects were on medication. Our results suggest that after the development of PD, beta and gamma become more strongly associated with age, implying that future studies examining beta and gamma changes in PD will need to take particular care in controlling for the age of subjects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".