Focal Decline of Cortical Thickness in Alzheimer's Disease Identified by Computational Neuroanatomy
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by a heterogeneous distribution of pathological changes throughout the brain. Magnetic resonance imaging can be used to investigate the regional distribution of cortical atrophy in AD in vivo. One marker for the disease-specific atrophy is the thickness of the cortical mantle across the brain, obtained with automated 3-D image processing. Here, we present data from 36 subjects (17 controls and, 19 patients diagnosed as probable AD) investigated for cortical thickness across the entire brain. We show significant cortical thickness decline in AD in temporal, orbitofrontal and parietal regions, with the most pronounced changes occurring in the allocortical region of the medial temporal lobes, outlining the parahippocampal gyrus, and representing a loss of >1.25 millimeters of cortical thickness. Moreover, focal cortical areas decline with progression of the disease as measured by time from baseline scan as well as the Mini-Mental State Exam. The results demonstrate the ability of this method to detect changes in cortical thickness in AD, across the entire brain, without need of prior anatomical definitions. The regional distribution of changes reported here is consistent with independent findings on the distribution of neuropathological alterations in AD. Using cortical thickness, moreover, we provide a direct quantitative index of atrophy in the 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".