P2–158: Combining morphometric measurements of the basal forebrain cholinerigc system and the cortical thickness for better diagnostic accuracy
Bibliographic record
Abstract
Cortical thickness and volumetric measurements of the basal forebrain cholinergic system (BFCS) show highly significant changes in patients with Alzheimer's disease (AD) compared to healthy controls (HC). The combination of volumetry of BFCS subnuclei and regional measurement of cortical thickness will allow (i) determining the corticotopy of BFCS nuclei using in vivo imaging, and (ii) combining diagnostic accuracy from both key markers of structural changes in AD. We used 280 high-resolution 3-D structural MRI datasets from the European DTI Study on Dementia (EDSD) including 137 patients with AD following the NINDS-ADRDA criteria and 143 HC. Nine scanners in eight European countries have been involved. BFCS volumes were derived by atlas-based image segmentation using high-dimensional image normalisation and a newly created subregion specific atlas of the BFCS based on post-mortem MRI in-cranio. Cortical thickness was analysed using Freesurfer software. We focused our analysis on cholinergic projections known from post-mortem data such as Ch4p to transverse temporal gyrus (TTG) and Ch2 to hippocampus. Ch4p volume showed significant correlation with thickness of the TTG only in HC; volume of the Ch2 region correlated with thickness of the parahippocampal gyrus (PG) in HC and AD groups. Cortical thickness of precuneus and PG showed an accuracy of 81.7 % for separating AD and HC. The combination with volumetry of the BFCS contributed significantly to diagnostic accuracy in the logistic regression model (85.3 %). In contrast, hippocampus volume, the best established structural imaging marker, did not add to the diagnostic accuracy of cortical thickness measures (80.7 %). Our data support the notion of a corticotopic organization of BFCS subnuclei as proposed by post mortem findings in non human primates. BFCS volume and cortical thickness measurements carry complementary diagnostic accuracy that can help to increase diagnostic accuracy of each single marker alone. Diagnostic utility remained largely unaffected by variability of scan parameters across multiple sites. Presently, an analysis is ongoing on the use of these markers to predict AD in mild cognitive impairment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".