Bibliographic record
Abstract
Compression and dimensionality reduction are tools through which we can recreate complex endpoints from simple underlying principles. But how helpful are these tools for understanding the reorganisation of structural and functional connectivity across childhood and adolescence? In Chapter 1, I introduce key concepts in developmental systems neuroscience, such as equifinality and multi-finality, alongside the need for a trans-diagnostic cross-modal integrated neuroscience with increased focus on individual-level heterogeneity, as opposed to group-level case-control comparisons. To address these theoretical considerations, I explore compression and dimensionality-reduction techniques as a lens through which we may systematically test, under different conditions, the extent to which group-level developmental principles apply to the individual, across development. In Chapter 2, using a population-level modelling approach, I examine the link between generative wiring principles underlying structural connectivity emergence, namely an economic trade-off, in preadolescent children with their genetic propensity for high cognitive ability. The cost penalty within this generative model varies as a function of polygenic scores for general intelligence, and converges on overlapping genetic ontologies, where children with a particularly strong genetic propensity for high cognitive ability exhibit simulated networks with significantly softer wiring constraints, resulting in a more randomised topology, and thus increased stochasticity and simulated global efficiency. In Chapter 3, across typically-developing and neurodivergent developmental cohorts, I establish organisational principles underpinning structural and functional connectivity variability across childhood and adolescence. I used diffusion-map embedding to derive low-dimensional manifolds or axes, constituting gradients of variability. In contrast to the literature, I demonstrate that such gradients are temporally stable and are refined with age but not reordered. To explore interactions between structural and functional manifolds, I propose a novel manifold-based measure of structure-function coupling, extensively benchmarked with prior measures, and demonstrate that such coupling is significantly predicted by measures of cognition, but not psychopathology, in a developmentally-sensitive manner. In Chapter 4 I take a third approach to compression, this time to focus on compressing brain-behaviour associations to establish trans-diagnostic links between the developing brain and developmentally relevant behaviours. To do this I model the predictive link between resting-state functional connectivity and item-level psychopathology, as a latent variable, through a partial least squares analysis in a cohort at-risk of neuro-developmental conditions, spanning childhood and adolescence. Through examining differential participant-level expression of this latent variable, I find that the latent construct transcended traditional diagnostic borders, revealing a neurotypical-neurodivergent continuum. Using meta-analytic functional activation decoding, functional connectivity associated with heightened risk for psychopathology overlaps with regions related to executive functioning, whilst a protective effect against psychopathology is linked to functional activations related to language. I conclude that the predictive link between functional connectivity and psychopathology is constrained by underlying macroscale and microscale organisational hierarchies, and it aligned with a somatosensory-association axis. In Chapter 5, I contextualise my findings and consider future research directions to enhance translation. Together, this thesis employs a multi-modal approach to chart the organisational principles underlying brain development in childhood and adolescence, their phenotypic consequences, and their relationship to underlying group-level macroscale and microscale hierarchical constraints and genetic influences.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".