Exploring Individual Brain Variability during Development based on Patterns of Maturational Coupling of Cortical Thickness: A Longitudinal MRI Study
Bibliographic record
Abstract
Structural covariance has recently emerged as a tool to study brain connectivity in health and disease. The main assumption behind the phenomenon of structural covariance is that changes in brain structure during development occur in a coordinated fashion. However, no study has yet explored the correlation of structural brain changes within individuals across development. Here, we used longitudinal magnetic resonance imaging scans from 141 normally developing children and adolescents (scanned 3 times) to introduce a novel subject-based maturational coupling approach. For each subject, maturational coupling was defined as similarity in the trajectory of cortical thickness (across the time points) between any two cortical regions. Our approach largely captured features seen in population-based structural covariance, and confirmed strong maturational coupling between homologous and near-neighbor cortical regions. Stronger maturational coupling among several homologous regions was observed for females compared to males, possibly indicating greater interhemispheric connectivity in females. Developmental changes in maturational coupling within the default-mode network (DMN) aligned with developmental changes in structural and functional DMN connectivity. Our findings indicate that patterns of maturational coupling within individuals may provide mechanistic explanation for the phenomenon of structural covariance, and allow investigation of individual brain variability with respect to cognition and disease vulnerability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".