Distinct Neural Correlates of Executive Function by Amyloid Positivity and Associations with Clinical Progression in Mild Cognitive Impairment
Bibliographic record
Abstract
PURPOSE: This study aimed to identify the neural basis of executive function (EF) in amnestic mild cognitive impairment (aMCI) according to beta-amyloid (Aβ) positivity. Furthermore, we explored if the identified brain areas could serve as predictors for clinical progression. MATERIALS AND METHODS: F]-florbetapir-positron emission tomography (PET), fluorodeoxyglucose-PET, and EF scores, as well as follow-up clinical severity scores at 1 and 5 years from baseline from the Alzheimer's Disease Neuroimaging Initiative database. The correlations between EF score and regional cerebral glucose metabolism (rCMglc) were analyzed separately for aMCI with low Aβ burden (aMCI Aβ-, n=230) and aMCI with high Aβ burden (aMCI Aβ+, n=268). Multiple linear regression analysis was conducted to investigate the associations between rCMglc and clinical progression. RESULTS: Longitudinal courses differed between aMCI Aβ- and aMCI Aβ+ groups. On average, aMCI Aβ- subjects maintained their level of clinical severity, whereas aMCI Aβ+ subjects showed progression. EF impairment in aMCI Aβ- was related to the anterior cingulate cortex (ACC), whereas that in aMCI Aβ+ was related to Alzheimer's Disease-vulnerable brain regions. ACC and the posterior cingulate cortex were associated with clinical progression in aMCI Aβ- and aMCI Aβ+, respectively. CONCLUSION: Our findings suggest that although MCI subjects showed similar behavioral phenotypes at the time of diagnosis, EF and further progression were associated with different brain regions according to Aβ burden. Clarification of the etiologies and nature of EF impairment in aMCI are critical for disease prognosis and management.
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 | 0.000 | 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".