BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder
Bibliographic record
Abstract
Variability of neural signaling is an important index of healthy brain functioning, as is signal complexity, which relates to information processing capacity. Alterations in variability and complexity may underlie certain brain dysfunctions. Here, resting-state fMRI was used to examine variability and complexity in children and adolescents with and without autism spectrum disorder (ASD). Variability was measured using the mean square successive difference (MSSD) of the time series, and complexity was assessed using sample entropy. A categorical approach was implemented to determine if the brain measures differed between diagnostic groups (ASD and controls). A dimensional approach was used to examine the continuum of relationships between each brain measure and behavioral severity, age, IQ, and the global efficiency (GE) of each participant's structural connectome, which reflects the structural capacity for information processing. Using the categorical approach, no significant group differences were found for neither MSSD nor entropy. The dimensional approach revealed significant positive correlations between each brain measure, GE, and age. Negative correlations were observed between each brain measure and the severity of ASD behaviors across all participants. These results reveal the nature of variability and complexity of BOLD signals in children and adolescents with and without ASD.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".