A Multimodal Neuroimaging and Neuropsychological Study of Visual Hallucinations in Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Hallucinations in Alzheimer's disease (AD) have been linked to more severe cognitive and functional decline. However, research on visual hallucinations (VH), the most common type of hallucinations in AD, is limited. OBJECTIVE: To investigate the cognitive and cerebral macrostructural and metabolic features associated with VH in AD. METHODS: Twenty-four AD patients with VH, 24 with no VH (NVH), and 24 cognitively normal (CN) matched controls were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Differences in regional gray matter (GM) volumes and cognitive performance were investigated with whole brain voxel-based morphometry analyses of MRI structural brain scans, and analyses of neuropsychological tests. Glucose metabolic changes were explored in a sub-sample of patients who had FDG-PET scans available. RESULTS: More severe visuoconstructive and attentional deficits were found in AD VH compared with NVH. GM atrophy and hypometabolism were detected in occipital and temporal areas in VH patients in comparison with CN. On the other hand, NVH patients had atrophy and hypometabolism mainly in temporal areas. No differences in GM volume and glucose metabolism were found in the direct comparison between AD VH and NVH. CONCLUSION: In addition to the pattern of brain abnormalities typical of AD, occipital alterations were observed in patients with VH compared with CN. More severe visuoconstructive and attentional deficits were found in AD VH when directly compared with NVH, and might contribute to the emergence of VH in AD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".