Associations between affective/vegetative neuropsychiatric symptoms and brain morphology in aging people with mild cognitive impairment and Alzheimer’s disease
Bibliographic record
Abstract
Objectives Neuropsychiatric symptoms (NPS) are common in mild cognitive impairment (MCI) and even more so in Alzheimer’s disease (AD). The affective/vegetative NPS cluster model (sleep disorders, depression, appetite changes, anxiety, and apathy) has been associated with an increased risk of dementia in patients with MCI and these five NPS have common neuroanatomical associations. Hence, in this study, we examined how brain morphology is influenced by the severity of affective/vegetative NPS across different stages of cognitive performance. Participants 175 AD, 367 MCI and 223 cognitively normal (CN) participants. Setting Participants were recruited at multiple centers in North America included in the ADNI project. Design A GLM was established to test for intergroup differences (CN -MCI, CN-AD, AD-MCI) of the effects of the five NPS on brain structures. A regression model was also performed to show slope directionality of the regions of interest as NPS severity increases. Measurements 3T MRI data (cortical volumes, areas and thickness) and severity scores of the five NPS. Results Associations within AD were predominantly stronger compared to MCI. Increased severity of sleep disorders and appetite changes were associated with a decrease in frontal surface areas in AD. Furthermore, increased severity of all NPS (except apathy) were associated with changes in the temporal regions, predominantly with decreased volumes and surface areas. Conclusion These findings show the implication of fronto-temporal regions with sleep disorders, depression and appetite changes, and contribute to a better understanding of brain morphological differences between CN, MCI and AD with respect to all five NPS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".