Atypical developmental features of cortical thickness trajectories in Autism Spectrum Disorder
Bibliographic record
Abstract
Abstract Neuroimaging studies have reported numerous region-specific atypicalities in the brains of individuals with Autism Spectrum Disorder (ASD), including alterations in cortical thickness (CT). However, there are many inconsistent findings, and this is probably due to atypical CT developmental trajectories in ASD. To this end, we investigated group differences in terms of shapes of developmental trajectories of CT between ASD and typically developing (TD) populations. Using the Autism Brain Imaging Data Exchange (ABIDE) repository (releases I and II combined), we investigated atypical shapes of developmental trajectories in ASD using a linear, quadratic and cubic models at various scales of spatial coarseness, and their association with symptomatology using the Autism Diagnostic Observation Schedule (ADOS) scores. These parameters were also used to predict ASD and TD CT development. While no overall group differences in CT was observed across the entire age range, ASD and TD populations were different in terms of age-related changes. Developmental trajectories of CT in ASD were mostly characterized by decreased cortical thinning during early adolescence and increased thinning at later stages, involving mostly frontal and parietal areas. Such changes were associated with ADOS scores. The curvature of the trajectories estimated from the quadratic model was the most accurate and sensitive measure for detecting ASD. Our findings suggest that under the context of longitudinal changes in brain morphology, robust detection of ASD would require three time points to estimate the curvature of age-related changes.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".