Cognitive Deficit and Aberrant Intrinsic Brain Functional Network in Early-Stage Drug-Naive Parkinson’s Disease
Bibliographic record
Abstract
Background Although cognitive deficit is a common non-motor symptom of Parkinson’s disease (PD), the mechanism and valid biomarkers of it have not been identified. To our best knowledge, this was the first study to investigate the intrinsic dysconnectivity pattern of whole-brain functional networks in early-stage drug-naive (ESDN) PD patients and its association with cognitive deficit of PD using voxel-wise Degree Centrality (DC) approach. Methods A total of 53 ESDN PD patients and 53 healthy controls (HC) were recruited. Resting-state fMRI (rs-fMRI) data were acquired, and voxel-wise DC approach was applied. Electrophysiological testing at P300 amplitude was recorded. The Montreal Cognitive Assessment (MoCA) was conducted to evaluate cognitive performance. Results ESDN PD patients had lower MoCA scores and P300 amplitudes, but higher P300 latency, than HC (all p < 0.0001). PD patients displayed higher DC in the right inferior frontal gyrus (IFG), left medial frontal gyrus (MFG) and left precentral gyrus (PreCG); but lower DC in the left inferior parietal lobule (IPL), left inferior temporal gyrus (ITG), right occipital lobe, and right postcentral gyrus (PoCG) (p Bonferroni correction < 0.0001). Interestingly, the DC values of left MFG, right PoCG and right occipital lobe were negatively associated with P300 latency but positively associated with P300 amplitudes and MoCA scores (all p Bonferroni correction < 0.0001). Conclusions Our results indicate the cognitive deficit and abnormal intrinsic brain functional network in ESDN PD patients. The damage of Default Mode Network (DMN) may be contributes to the pathogenesis of cognitive dysfunction in ESDN PD.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".