Correlation of regional cerebral blood flow with brain structural changes in mild cognitive impairment and Alzheimer's disease
Bibliographic record
Abstract
Abstract Objectives Brain atrophy and structural changes due to aging contribute strongly in the pathophysiology of mild cognitive impairment (MCI) and Alzheimer’s Disease (AD). Regional cerebral blood flow (CBF) impairment is believed to be one of the initial changes in the AD continuum. In this study, we investigated the association between CBF and brain structural changes associated with aging and neurodegeneration. Methods Data from three groups of participants including 39 control normal (CN), 82 MCI, and 28 AD subjects were downloaded from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Magnetic resonance images (MRI) of participants were automatically segmented by FreeSurfer V 7.0 software and arterial spin labeling (ASL) MRI was applied to measure CBF and investigate effect of aging and structural changes on CBF in experimental groups. One way ANOVA and Pearson correlation coefficient were used to compare data and find correlation between CBF and structural changes in the brain. Results AD patients had significantly lower Mini-Mental State Examination (MMSE) score (p=0.001) and more APOE-ε4 carriers (p=0.001) as compared to the MCI and CN groups. Our findings revealed a wide spread significant correlation between the CBF and structural changes, including cortical volume, subcortical volume, surface area, and thickness in all participants, particularly AD patients after adjusting for age, sex, and APOE genotyping status. Conclusion Several structural abnormalities correlated with declined CBF and could potentially be used as imaging biological correlates biomarkers to predict early onset of AD, regardless of Aβ or tau accumulation.
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 | 0.001 | 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".