Abnormal Large-Scale Dynamic Brain Networks in Parkinson’s Disease With Cognitive Impairment: Insights From EEG Co-Activation Patterns
Bibliographic record
Abstract
This study investigated the large-scale dynamic brain network mechanisms underlying cognitive decline in Parkinson's disease (PD) by integrating high-density Electroencephalography (EEG) signal source localization and co-activation pattern (CAP) analysis to track transient network states during cognitive tasks. Twenty patients with PD and fourteen healthy controls (HC) underwent simultaneous EEG acquisition while performing Chinese character reading, color recognition, and Stroop tasks; cognitive functions were assessed using the Montreal Cognitive Assessment (MoCA). High-density EEG signals were reconstructed using standardized low-resolution brain electromagnetic tomography (sLORETA), yielding six CAPs representing whole-brain transient dynamic activation. Results indicated that the decoupling between the default mode network (DMN) and the task-related network (TRN) is impaired in PD patients. Specifically, during the Stroop task, PD patients showed reduced dwell time in CAP4 (TRN activation/DMN inhibition), prolonged DMN activation, and increased transitions from TRN to DMN states. CAP fraction of time correlated positively with MoCA scores, suggesting DMN-TRN decoupling efficiency predicts cognitive performance. PD patients also exhibited compensatory overactivation of the anterior cingulate cortex (ACC) and salience network (SN). In conclusion, PD is characterized by disrupted dynamic DMN-TRN interactions, frequent state switching, and compensatory hyperactivation, directly contributing to cognitive decline. This study maps large-scale dynamic brain networks in PD with millisecond resolution, revealing new insights into transient network states and compensatory mechanisms, identifying potential biomarkers, and informing interventions targeting network uncoupling efficiency.
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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 | 0.000 |
| 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) | 0.000 | 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 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".