P4‐572: NEURAL CORRELATES OF COGNITIVE PERFORMANCE IN ALZHEIMER'S DISEASE AND LEWY BODY DISEASE SPECTRA
Bibliographic record
Abstract
We investigated the neural correlates of cognitive dysfunction in patients with Alzheimer's disease (AD)-related cognitive impairment (ADCI) and those with Lewy bodies-related cognitive impairment (LBCI). We enrolled 216 ADCI patients, 183 LBCI patients and 30 controls from January 2014 to April 2017. Cortical thickness and diffusion tensor imaging analyses were performed to correlate gray matter (GM) 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. Regarding the ADCI spectrum, memory dysfunction correlated with cortical thinning and increased mean diffusivity (MD) in the AD-prone GM and adjacent WM regions, respectively, and decreased fractional anisotropy (FA) in the WM connecting these brain regions. Regarding the LBCI spectrum, memory dysfunction only correlated with increased MD in the WM adjacent to the anteromedial temporal, insula, and basal frontal cortices. For visuospatial dysfunction, the ADCI spectrum was correlated with cortical thinning in the posterior brain regions, while the LBCI spectrum correlated with decreased FA in the corpus callosum and widespread WM regions. Attention/executive dysfunction correlated with cortical thinning and WM abnormalities in widespread brain regions in both disease spectra; however, ADCI and LBCI correlated more prominently with cortical thinning and decreased FA, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".