Decreased Dynamic Flexibility of Brain Functional Connectivity in Prodromal Alzheimer’s Disease
Bibliographic record
Abstract
Background and Hypothesis: In neuroimaging, functional connectivity (FC), defined as the correlation between the functional MRI signals of two brain grey matter regions of interest (ROIs), is thought to reflect communication between ROIs. Changes in whole brain FC networks have been detected in Alzheimer’s disease (AD); however, traditional FC networks generated using the entire length of an fMRI scan could miss cognitively relevant fluctuations in FC. Analyzing dynamic patterns of FC within subsets of fMRI scans is hypothesized to enable greater sensitivity to deficits of information transfer and processing in AD compared to static FC. Project Methods: Functional MRI data of 58 participants with either subjective cognitive decline (SCD), mild cognitive impairment (MCI), AD, or controls were divided into time windows; the FC within each window provides sequential dynamic FC networks (dFC). Each dFC network was partitioned into subnetworks, e.g. visual or motor, whose member ROIs are strongly interconnected, and the functional flexibility of an ROI was estimated by the number of times it switches subnetworks in a scan. Results: The flexibility of the left inferior parietal lobule, right rostral lateral orbitofrontal cortex, and right amyglada/parahippocampal gyrus showed the highest correlations with Montreal Cognitive Assessment scores: r = 0.2516, 0.2480, and 2421, respectively. Although no correlations reached conventional significance (p = 0.0568, 0.0605, and 0.0671, uncorrected), this may reflect low power that should be increased with a planned larger sample. Potential Impact: Dynamic FC analyses may help clarify the neurophysiological mechanisms underlying cognitive decline, but methodological refinements and higher resolution data are likely needed to realize this potential.
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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 | 0.000 | 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".