Early functional network alterations predict motor and cognitive decline in parkinson’s disease
Bibliographic record
Abstract
This study aimed to identify potential structural and resting state FC (rs-FC) alterations in de novo PD and examine their possible relationship with motor and cognitive symptoms; and to explore whether early structural and rs-FC alterations were associated with subsequent clinical progression. Seventy-eight de novo PD patients and thirty-one healthy subjects (HS) were enrolled. The severity of motor symptoms was assessed by the MDS-Unified Parkinson’s Disease rating scale and cognitive performance was assessed with the Montreal Cognitive Assessment. Forty out of 78 PD patients underwent a clinical follow-up after a period of 5.29 ± 1.40 years. All participants underwent 3T MRI scanning. Structural MRI analyses included gray matter volume estimation, thalamus, basal ganglia and cerebellar volumetry. Resting-state functional connectivity was assessed using independent component analysis. Compared to HS, de novo PD patients did not show any structural alteration. Conversely, they showed decreased rs-FC in several brain networks, including the default mode network, sensorimotor, cerebellar, medial visual, occipital, orbitofrontal, dorsal attention, executive control, and the left frontoparietal networks. Although baseline functional alterations were not associated with clinical measures at the time of assessment, reduced baseline rs-FC in most resting-state networks was predictive of clinical progression over time. De novo PD patients exhibit widespread rs-FC alterations across multiple brain networks, despite a preserved structural integrity. Early rs-FC alterations in sensorimotor, cerebellar, and cognitive networks were linked to subsequent clinical deterioration. These findings highlight the potential of rs-fMRI as a valuable early imaging correlate for tracking disease progression in 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".