Regional gyrification alterations and metabolic correlates in biologically defined Alzheimer's disease using a multimodal approach
Bibliographic record
Abstract
Background Surface-based morphometry (SBM) metrics, such as the gyrification index (GI), have emerged as biomarkers for detecting early, subtle alterations in Alzheimer's disease (AD). Objective We investigated GI differences between biologically defined AD patients (based on cerebrospinal fluid (CSF)/positron emission tomography (PET) biomarkers), and participants with non-AD cognitive impairment. We further explored correlations between GI, regional metabolism (FDG-PET), cortical thickness (MRI), and cognitive performance using the Montreal Cognitive Assessment (MoCA). Methods T1-weighted MRI and FDG-PET scans from 36 AD and 15 non-AD participants were retrospectively analyzed. GI was computed using both SPM-based whole-brain and ROI-atlas based analyses. FDG-PET was available for 27 AD and 13 non-AD participants and SUVRs were extracted from standard ROIs. Cognitive performance was measured via the MoCA. Results GI and metabolic uptake were reduced in the insula, while GI was increased in the entorhinal/parahippocampal cortex for AD participants. Metabolic uptake was also lower in the insula in AD. Insular GI correlated positively with metabolism (SUVR; R = 0.370, p = 0.021), cortical thickness ( R = 0.510, p = 0.001), and MoCA scores ( R = 0.554, p = 0.004). Parahippocampal GI was inversely associated with cortical thickness ( R = −0.340, p = 0.034). No significant correlations were observed with CSF biomarkers. Conclusions Our findings demonstrate region-specific GI alterations in AD, particularly in the insula and entorhinal cortex. The novel correlation between GI and metabolism suggests disease-related mechanisms linking cortical folding to synaptic dysfunction. These results highlight GI as a valuable early biomarker for AD with potential diagnostic and therapeutic implications.
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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 | 0.001 |
| 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 | 0.001 | 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".