Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer's disease
Bibliographic record
Abstract
Cortical thickness is a more reliable measure of atrophy than volume due to the low variability in the cytoarchitectural structure of the grey matter. However, this more desirable measure of disease-related alterations is not fully evaluated in early dementia. The study presented here is the first to report the spatial patterns of cortical thickness in the pre-clinical stages of Alzheimer's disease, namely mild cognitive impairment (MCI). Cortical thickness measurements for 34 healthy elderly, 62 MCI and 42 Alzheimer's disease subjects were made using fully automated magnetic resonance imaging-based analysis techniques in order to determine the pattern of cortical thinning as a function of disease progression. The thickness of the cortex decreased significantly when the healthy elderly brains were compared to those with MCI, mainly in the medial temporal lobe region and in some regions of the frontal and the parietal cortices. With the progression of disease from MCI to Alzheimer's disease, a general thinning of the entire cortex with significant extension into the lateral temporal lobe was found. In all cases, the results were more pronounced in the left hemisphere. In conclusion, we have shown that there is a specific pattern in the thinning of the cortical ribbon which is in agreement with the previous histological reports. These novel findings support the notion of increased isocortical involvement with the progression of disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".