[IC‐P‐074]: LONGITUDINAL DIFFUSION TENSOR IMAGING AS A PREDICTOR OF COGNITIVE DOMAINS DECLINE IN EARLY STAGE PARKINSON's DISEASE: ICICLE‐PD STUDY
Bibliographic record
Abstract
The risk of patients with Parkinson's disease developing dementia is five times greater than the general population. As the pathophysiological processes underlying Parkinson's disease (PD) dementia are heterogeneous, monitoring the progression of white matter microstructural changes might help identify its role in mediating cognitive deficits in PD. This study investigated whether white matter microstructural abnormalities are predictors of cognitive domains changes in early Parkinson's disease (PD). A total of 123 patients with early PD were enrolled along with 49 controls. Participants were part of the ICICLE-PD (Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation) study and underwent clinical, cognitive and DTI investigations at baseline and 18 months later with cognitive and diffusion tensor imaging. General cognition was tested using the Mini-Mental State Examination. Attention, memory, language, executive and visuospatial functions were assessed based on a selection of subtests from different batteries. Imaging parameters were analysed using Tract Based Spatial Statistics. The relationships between fractional anisotropy (FA) and mean diffusivity (MD) with cognition were investigated using multiple linear regression. All analyses were controlled for age, sex, education, levodopa dose and visit intervals. At baseline, patients with PD had significantly higher widespread MD then controls. At follow-up, both groups showed a further significant FA decrease and MD increase. Baseline MD was a significant predictor of executive function (β (95%CI) -7.85 (-11.81; -3.88), p<0.001) and general cognitive change (β (95%CI) -9.58 (-16.56; -2.60), p 0.008) among patients with PD. MD represents an important correlate and predictor of cognitive change and in PD: DTI is potentially a useful tool in stratification of patients into clinical trials and to monitor the impact of treatment on cognition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".