Reorganisation of diffusion microstructure in the precuneus is associated with preserved cognitive function in Parkinson’s disease
Bibliographic record
Abstract
Abstract Functional neuroimaging studies of patients with Parkinson’s disease (PD) have repeatedly identified over-activations in midline structures (medial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, and precuneus), especially in those without comorbid dementia. Here, we investigated whether the different cognitive profiles in PD were linked to measures of diffusion microstructure in medial regions of the brain. Using magnetic resonance based diffusion weighted imaging (DWI) in healthy volunteers (HV) and PD patients with and without mild cognitive impairment (PD-nonMCI and PD-MCI), applying diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) techniques, we observed: 1) increased fractional anisotropy (FA) in the precuneus and the anterior cingulate in the PD-nonMCI participants compared with the HV; 2) an association between precuneus FA and executive and memory function, respectively, in PD and HV; 3) a negative correlation between age and midline structure FA in PD but not HV; and 4) a differential association between cognitive scores and apparent fiber density (AFD) of the posterior cingulate-precuneus bundle in HV vs. PD. Together, these findings suggest that white matter reorganization of the posterior medial microstructures might serve a compensatory role for damaged basal ganglia function in PD-nonMCI.
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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".