Corpus Callosum in Neurodegenerative Diseases: Findings in Parkinson’s Disease
Bibliographic record
Abstract
Corpus callosum area has been examined in neurodegenerative diseases as a marker for cortical pathology and for differential diagnosis; however, it has not been examined in Parkinson's disease (PD). We compared callosal area in patients with PD and PD with dementia (PDD) to healthy controls and patients with Alzheimer's disease (AD). We subsequently compared our results to a meta-analysis of studies examining callosal area in AD, frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). For the imaging study, midsagittal T1-weighted MRIs were analyzed and the callosal area was determined in patients with PD (n = 24), PDD (n = 25), AD (n = 16) and controls (n = 27). The meta-analysis combined results from all publications (Medline or PubMed) representing unique samples and measuring callosal area in AD, FTD, PSP, and CBD. We found that PD and PDD patients did not show statistically significant callosal atrophy compared to controls (effect size d, 95% CI, d = 0.13, -0.26 to 0.52, and d = 0.05, -0.44 to 0.33, respectively) or AD. The AD patients had a significant loss of callosal area compared to controls (d = -0.58, -1.01 to -0.15). Dementia severity was correlated with total callosal atrophy in AD (R = 0.66, p < 0.01) but not in PDD patients (R = 0.18, p > 0.1). The meta-analysis revealed significant combined effect sizes for callosal atrophy of: AD (d = -1.03, -1.13 to -0.93), FTD (d = -1.21, -1.56 to -0.86), PSP (d = -1.09, -1.38 to -0.81), and CBD (d = -1.80, -2.18 to -1.43). We conclude that PD and PDD patients do not have callosal atrophy in contrast to other neurodegenerative diseases, including AD. Callosal atrophy was correlated with dementia severity in patients with AD but not PDD.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".