Building Functional Connectivity Neuromarkers of Behavioral Self-Regulation in Children with and without Autism Spectrum Disorder
Bibliographic record
Abstract
ABSTRACT Children with Autism Spectrum Disorder (ASD) are known to struggle with behavioral self-regulation, which associates with greater daily-life challenges and an increased risk for psychiatric comorbidities. Despite these negative outcomes, little is known about the neural expression of behavioral regulation in children with and without ASD. Here, we examined whole-brain linear associations between brain functional correlations (FC) and behavioral regulation through connectome predictive modelling (CPM), a data-driven protocol for developing predictive models of brain–behavior relationships from data, assessing ‘neuromarkers’ using cross-validation. Using data from two sites of the ABIDE II dataset comprising 276 children with and without ASD (8-13 years), we identified functional brain networks whose FC predicted individual differences in two, of three, behavioral regulation subdomains. These distributed network models predicted novel individuals’ inhibition and shifting from resting-state FC data both in a leave-one-out, as well as split halves, cross-validation. We observed commonalities and differences in the functional networks associating with these subdomains, with inhibition relying on more posterior networks, shifting relying on more anterior networks, and both involving regions of the DMN. Our findings present a substantial addition to our knowledge on the neural expressions of inhibition and shifting across the spectrum of children with and without ASD, demonstrating the utility of this trans-diagnostic modelling approach. Given the numerous cognitive and behavioral issues that can be quantified dimensionally in neurodevelopmental disorders, further refinement of whole-brain neuromarker techniques may thus pave a way for functional neuroimaging to meaningfully contribute to individualized medicine.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".