Impact of hyperactive neuropsychiatric symptoms on brain morphology in mild cognitive impairment and Alzheimer’s disease
Bibliographic record
Abstract
ABSTRACT Background The neuroanatomy of hyperactive neuropsychiatric symptoms (NPS) is poorly understood, and it is unclear whether these symptoms result from the same pathogenic processes responsible for cognitive decline or if they have an independent etiology to the neurodegeneration due to Alzheimer’s disease (AD). Objective We aim to investigate how the severity of hyperactive NPS (i.e., agitation, disinhibition, and irritability) can impact brain structures at different stages of cognitive decline. Methods Neuropsychiatric and 3T MRI data from 223 cognitively normal (CN) participants, 367 participants with mild cognitive impairment (MCI) and 175 participants with AD were extracted from the Alzheimer Disease Neuroimaging Initiative (ADNI) database. Statistical analyses based on the general linear model (GLM) were performed to define the effects of neuropsychiatric variables on brain structures in a two-by-two comparison (AD-MCI, CN-MCI and CN-AD). Linear regression analysis was also performed to investigate cortical changes as a function of NPS severity. Results In the AD group, the frontal dorsolateral is the most influenced region receiving an impact from more severe agitation, disinhibition and irritability. In AD, agitation and irritability influence some temporal inferior and parietal superior regions. Furthermore, severe disinhibition seems to have a stronger influence on CN participants compared to the other two groups, particularly in the occipital lingual, frontal middle rostral and frontal pars triangularis regions. Conclusion Our study shows that hyperactive NPS influence differently the brain morphology at different stages of cognitive performance. This might imply that their severity should be evaluated in relation to results of cognitive evaluations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".