Corpus Callosum and Cingulum Tractography in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: In Parkinson's disease (PD) cell loss in the substantia nigra is known to result in motor symptoms; however widespread pathological changes occur and may be associated with non-motor symptoms such as cognitive impairment. Diffusion tensor imaging is a quantitative imaging method sensitive to the micro-structure of white matter tracts. OBJECTIVE: To measure fractional anisotropy (FA) and mean diffusivity (MD) values in the corpus callosum and cingulum pathways, defined by diffusion tensor tractography, in patients with PD, PD with dementia (PDD) and controls and to determine if these measures correlate with Mini-Mental Status Examination (MMSE) scores in parkinsonian patients. METHODS: Patients with PD (17 Males [M], 12 Females [F]), mild PDD (5 M, 1 F) and controls (8 M, 7 F) underwent cognitive testing and MRI scans. The corpus callosum was divided into four regions and the cingulum into two regions bilaterally to define tracts using the program DTIstudio (Johns Hopkins University) using the fiber assignment by continuous tracking algorithm. Volumetric MRI scans were used to measure white and gray matter volumes. RESULTS: Groups did not differ in age or education. There were no overall FA or MD differences between groups in either the corpus callosum or cingulum pathways. In PD subjects the MMSE score correlated with MD within the corpus callosum. These findings were independent of age, sex and total white matter volume. CONCLUSIONS: The data suggest that the corpus callosum or its cortical connections are associated with cognitive impairment in PD patients.
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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) | 0.002 | 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".