Resting state EEG as biomarker of cognitive training and physical activity’s joint effect in Parkinson’s patients with mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Cognitive decline is a major factor for the deterioration of the quality of life in patients suffering from Parkinson's disease (PD). Recently, it was reported that cognitive training (CT) in PD patients with mild cognitive impairment (PD-MCI) led to an increase of physical activity (PA) accompanied by improved executive function (EF). Moreover, PA has been shown to alter positively brain function and cognitive abilities in PD. Both observations suggest an interaction between CT and PA. OBJECTIVES: A previous multicenter (MC) study was slightly significant when considering independent effects of interventions (CT and PA) on EF. Here, we use MC constituent single center data that showed no effect of interventions on EF. Thus, this exploratory study considers pooling data from both interventions to gain insight into a recently reported interaction between CT and PA and provide a proof of principle for the usefulness of resting state EEG as a neurophysiological biomarker of joint intervention's effect on EF and attention in PD-MCI. METHODS: Pre- and post-intervention resting state EEG and neuropsychological scores (EF and attention) were obtained from 19 PD-MCI patients (10 (CT) and 9 (PA)). We focused our EEG analysis on frontal cortical areas due to their relevance on cognitive function. RESULTS: We found a significant joint effect of interventions on EF and a trend on attention, as well as trends for the negative correlation between attention and theta power (pre), the positive correlation between EF and alpha power (post) and a significant negative relationship between attention and theta power over time (post-pre). CONCLUSIONS: Our results support the role of theta and alpha power at frontal areas as a biomarker for the therapeutic joint effect of interventions.
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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".