Neural Correlates of Cognitive Performance in Alzheimer’s Disease- and Lewy Bodies-Related Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Clinicopathological studies have demonstrated that the neuropsychological profiles and outcomes are different between two dementia subtypes, namely Alzheimer's disease (AD) and Lewy bodies-related disease. OBJECTIVE: We investigated the neural correlates of cognitive dysfunction in patients with AD-related cognitive impairment (ADCI) and those with Lewy bodies-related cognitive impairment (LBCI). METHODS: We enrolled 216 ADCI patients, 183 LBCI patients, and 30 controls. Cortical thickness and diffusion tensor imaging analyses were performed to correlate gray matter and white matter (WM) abnormalities to cognitive composite scores for memory, visuospatial, and attention/executive domains in the ADCI spectrum (ADCI patients and controls) and the LBCI spectrum (LBCI patients and controls) separately. RESULTS: Memory dysfunction correlated with cortical thinning and increased mean diffusivity in the AD-prone regions, particularly the medial temporal region, in ADCI. Meanwhile, it only correlated with increased mean diffusivity in the WM adjacent to the anteromedial temporal, insula, and basal frontal cortices in LBCI. Visuospatial dysfunction correlated with cortical thinning in posterior brain regions in ADCI, while it correlated with decreased fractional anisotropy in the corpus callosum and widespread WM regions in LBCI. Attention/executive dysfunction correlated with cortical thinning and WM abnormalities in widespread brain regions in both disease spectra; however, ADCI had more prominent correlation with cortical thickness and LBCI did with fractional anisotropy values. CONCLUSIONS: Our study demonstrated that ADCI and LBCI have different neural correlates with respect to cognitive dysfunction. Cortical thinning had greater effects on cognitive dysfunction in the ADCI, while WM disruption did in the LBCI.
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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) | 0.001 | 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".