A longitudinal study of functional brain complexity in progressive Alzheimer's disease
Bibliographic record
Abstract
Abstract INTRODUCTION Cross‐sectional resting‐state functional magnetic resonance imaging (rsfMRI) studies have revealed altered complexity with advanced Alzheimer's disease (AD) stages. The current study conducted longitudinal rsfMRI complexity analyses in AD. METHODS Linear mixed‐effects (LME) models were implemented to evaluate altered rates of disease progression in complexity across disease groups. RESULTS The LME models revealed complexity of the higher frequency in the CNtoMCI group (those converted from cognitively normal [CN] to mild cognitive impairment [MCI]) decayed faster over time versus CN in the prefrontal and lateral occipital cortex; complexity of the lower frequency decayed faster in AD versus CN in various frontal and temporal regions ( p < 0.05 & Benjamini–Hochberg corrected with q < 0.05). DISCUSSION Local functional brain activities decayed in the early stage of the disease, and long‐range communications were impacted in the later stage. Our study demonstrated longitudinal changes in AD‐related rsfMRI complexity, indicating its potential as an imaging biomarker of AD. Highlights We conducted longitudinal resting state functional magnetic resonance imaging (rsfMRI) complexity analyses using the Alzheimer's Disease Neuroimaging Initiative dataset. Higher‐frequency complexity in the CNtoMCI group (those transitioning from cognitively normal [CN] to mild cognitive impairment [MCI]) was found to decay faster over time compared to CN, specifically in the prefrontal and lateral occipital cortex. Lower‐frequency complexity was found to decay faster in AD versus CN in various frontal and temporal regions. This study demonstrated that longitudinal changes in rsfMRI complexity could serve as a potential imaging biomarker for Alzheimer's disease.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".