Transdiagnostic Profiles of BOLD Signal Variability in Autism and Schizophrenia Spectrum Disorders: Associations with Cognition and Functioning
Bibliographic record
Abstract
Autism spectrum disorder (autism) and schizophrenia spectrum disorders (schizophrenia) exhibit overlapping social and neurocognitive impairment and considerable neurobiological heterogeneity. Blood-oxygen-level-dependent (BOLD) signal variability captures the brain's moment-to-moment fluctuations, offering a dynamic marker of neural flexibility that is sensitive to cognitive capacity. This study aimed to examine intra-regional BOLD signal variability during rest and task across schizophrenia, autism, and typically developing controls (TDC) to explore transdiagnostic patterns of brain signal variability and their relationship with cognitive and functional outcomes. Intra-regional BOLD variability, measured by mean squared successive difference (MSSD), was obtained from resting-state and empathic accuracy task fMRI in 176 SSD, 89 autism, and 149 TDC participants. ANCOVAs, controlling for age, sex, and motion, assessed group differences in intra-regional and network-level BOLD variability and dimensional associations with social cognition, neurocognition, social functioning, and symptom severity. Both autism and schizophrenia exhibited lower BOLD signal variability than TDC across rest and task, with reduced variability observed in somatomotor, visual, and auditory networks (pFDR < 0.01). Greater network variability was positively associated with better social cognitive, neurocognitive, and functional scores across the sample. Resting-state variability showed stronger group-based differences and cognitive associations than task-based variability. BOLD signal variability is positively associated with social cognition, neurocognition, and social functioning across groups, suggesting that variability impacts cognitive efficiency and behavior. Reduced variability in autism and schizophrenia may indicate similar patterns of neural rigidity among these related conditions, positioning BOLD variability as a potential biomarker for neural flexibility and a valuable target for future transdiagnostic clinical interventions.
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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 | 0.001 |
| 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".