Quantitative neurodegenerative pathology does not explain the degree of hippocampal atrophy on MRI in degenerative dementia
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate the neuropathological substrates underlying in vivo hippocampal atrophy on magnetic resonance imaging (MRI) in autopsy confirmed neurodegenerative dementia cases. METHODS: Thirty-one neuropathologically verified cases (23 with Lewy body dementia (LBD) and eight with Alzheimer's disease (AD)) were included who had undergone an MRI scan close to death (mean 1.5 years). Manual volumetric measurements were undertaken for the hippocampus, entorhinal cortex and amygdala on MRI, along with quantitative neuropathological analysis of plaque, tangle and Lewy body pathology in the same regions. The relationship between neuropathology and MRI volumes was assessed using correlations and linear regression. RESULTS: Hippocampal and amygdala volumes were significantly smaller in cases with AD than with LBD, but there was no difference in entorhinal cortex volume. Analysing all cases together, a significant positive correlation was observed between normalised hippocampal volume and percent area of Lewy bodies in the hippocampus (r=0.449, p=0.017) but not with tangles (r=0.059, p=0.766) or plaques (r=-0.361, p=0.119). There were no other significant correlations between regional MRI volume and measures of neuropathology. Regression analysis showed that overall diagnosis of AD rather than burden of individual pathological changes was the most significant predictor of hippocampal volume loss in autopsy confirmed cases. CONCLUSION: Our results suggest that (i) hippocampal and amygdala but not entorhinal cortex, volumes differ between AD and LBD and (ii) factors other than current markers of neurodegenerative pathological change are responsible for atrophy of medial temporal lobe structures in AD and LBD.
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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 | 0.004 |
| 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 | 0.001 |
| 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".