Neuropsychiatric, cognitive and brain morphology characteristics of conversion from Mild Cognitive Impairment to Alzheimer’s Disease
Bibliographic record
Abstract
Abstract The impact of neuropsychiatric symptoms (NPS) on cognitive performance has been extensively reported, and this impact was better defined in the aging population. Yet a potential impact of NPS on brain morphology, cognitive performance and interactions between them in a longitudinal setting, as well as the potential of using these values as prediction of conversion – have remained questionable. We studied 156 participants with mild cognitive impairment (MCI) from the Alzheimer’s Disease Neuroimaging Initiative database who maintained the same level of cognitive performance after a 4-year follow-up and compared them to 119 MCI participants who converted to dementia. Additionally, we assessed the same analysis in 170 healthy controls who remained healthy at follow-up. Compared to 15 controls who converted to MCI. Their neuropsychological, neuropsychiatric, and brain morphology data underwent statistical analyses of 1) baseline comparison between the groups; (2) analysis of covariance model controlling for age, sex, education, and MMSE score, to specify the cognitive performance and brain structures that distinguish the two subgroups, and 3) used the significant ANCOVA variables to construct a binary logistic regression model that generates a probability equation for a given individual to convert to a lower cognitive performance state. Results showed that MCI who converted to AD in comparison to those who did not convert, exhibited a higher NPS prevalence, a lower cognitive performance and a higher number of involved brain structures. Furthermore, agitation, memory and the volumes of inferior temporal, hippocampal and amygdala sizes were significant predictors of MCI to AD conversion.
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 | 0.002 |
| 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 | 0.001 | 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".