RS-fMRI Evidence for Differential Within- and Between-Module Neural Interactions Across Age
Bibliographic record
Abstract
Abstract Background Previous resting-state functional MRI (rsfMRI) studies have identified a robust inverse relationship between nodal strength and amplitude of low-frequency fluctuations (ALFF) across cortical modules. This study examined whether this negative relationship exists within modules and further explored age-related effects on these associations. Methods Using MOSI (modularity analysis and similarity measurements), rsfMRI data from three public datasets spanning different age cohorts were analyzed. Correlations between ALFF and nodal strength between modules or voxel concordance within modules were calculated. Geometric mean p-values assessed robustness at the individual level. Results Confirming prior findings, a significant inverse correlation between nodal strength and ALFF was observed at the between-module level (geometric p-values 10 −4 to 10 −5 ). Within-module negative associations were nonsignificant in younger cohorts at individual level (mean ages 10 and 21) but became significant in the older cohort (mean age 33). The magnitude of this negative association increased with age, consistent with maturation of local inhibitory network mechanisms. Conclusions The findings support MOSI as a valid FP method for cortical network construction, with within-module inhibitory effects strengthening in adulthood. The age-dependent modulation reveals network maturation at the modular level, with implications for neurodevelopmental and neuropsychiatric conditions.
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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 | 0.004 |
| 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) | 0.003 | 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".