MODELING BRAIN NETWORK DYNAMICS IN EARLY DEVELOPMENT WITH THE VIRTUAL BRAIN
Bibliographic record
Abstract
Childhood is a critical period marked by substantial changes in structural and functional brain network features. Subject-specific computational models provide a means to integrate various scales and modalities, offering insight into potential mechanisms and patterns underlying neurodevelopment. “TheVirtualBrain” (TVB) is a neuroinformatics platform developed to investigate whole-brain dynamics using multimodal neuroimaging data and neural mass models. The goal of this dissertation was to advance the field of network neuroscience by investigating the brain’s structure-function relationship and dynamics during early childhood, an understudied age range.In this thesis, longitudinal changes in early childhood brain network features were assessed to characterise the variability in developmental patterns and brain-behaviour associations. Using TVB, individualised brain network models were then generated to explore the relationship between structural connectivity (SC) changes and functional dynamics in early development. Biophysical model parameters, global coupling (G) and noise, were optimised to fit each subject's simulated and empirical functional data. The optimal parameter values and simulations were evaluated and compared with empirical structural topology metrics, as well as local and state functional dynamics. In study one, longitudinal changes in region-wise graph measures (i.e., weighted degree, local clustering) of children’s functional connectivity (FC) data and associations between structural brain network topology with age and behaviour were identified. In a combined analysis with SC, FC, and an SC-FC coupling metric, SC emerged as the dominant predictor of age. In study two, the utility of TVB in developing robust models across early childhood that align with empirical features was demonstrated. A longitudinal increase in the noise model parameter with age was also identified, while G showed no age-related changes. In study three, a region-wise metric characterising simulated bistable dynamics was related to regional empirical features and exhibited a distinct spatial distribution. Global coupling was associated with the bistability spatial distribution, a state of global functional activity coherence, and the complexity of the simulated functional time series. Together, these studies provide insight into the key role of brain network structure in early childhood and its regionally specific relationships with function and behaviour, establishing The Virtual Brain as a valuable integrative tool for neurodevelopmental research.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".