Intervention‐related changes in variability of dynamic functional connectivity and the relationship to cognition in older adults at risk for Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Aging is associated with a decline in specific cognitive abilities and increased variability of dynamic functional connectivity (v-dFC; Jauny et al., 2022), across the whole brain (Yang et al., 2023) and in the default network (Douw et al., 2016; Madhyastha & Grabowski, 2014). We investigated the extent to which age-related changes in v-dFC would be mitigated by an intervention to enhance physical activity in older adults at risk for Alzheimer's disease (AD) and whether the changes in v-dFC were related to cognitive improvements. METHOD: =70.3 years) for a four-week randomized controlled trial. Multi-echo gradient-echo EPI sequence was used to acquire resting-state functional MRI data at baseline and post-intervention. Using Schaefer's 200 parcellation across Yeo's 17 networks, the dFC matrices were constructed with the Multiplication of Temporal Derivatives method (Shine et al., 2015; 10 TR overlapping windows). Modularity, system segregation (Chan et al., 2014), within-network and between-network FC of the DefaultA/B/C and ControlA/B/C networks were calculated for each dFC matrix. V-dFC was defined as the standard deviation of these measures. Group by time interactions effects on v-dFC were estimated after controlling for age, education, sex, motion and APOE4 carriership status. Change scores in v-dFC measures were correlated with changes in cognitive performance on digit span and digit symbol matching tasks. RESULT: The intervention group showed decreased modularity and maintained system segregation within-DefaultC, between DefaultC-ControlC v-dFC, compared to the control group who showed increased v-dFC (Figure 1) after the intervention period relative to baseline. The baseline to post-intervention decrease in v-dFC of modularity was related to concurrent improvement in digit symbol matching task performance (Figure 2). CONCLUSION: An intervention to enhance physical activity mitigated age-related increases in v-dFC network segregation and connectivity of the DefaultC subnetwork. DefaultC corresponds to the medial temporal lobe subsystem (Andrews-Hanna et al., 2010), including the parahippocampal, retrosplenial and inferior parietal regions, which are highly vulnerable to AD pathology (Buckner et al., 2005).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".