Atypical functional brain connectivity during rest in autism spectrum disorders
Bibliographic record
Abstract
OBJECTIVE: Connectivity atypicalities in autism spectrum disorders (ASD) have been extensively proposed. The default mode network (DMN) is critical in this study, given the insight it provides for long-distance connectivity, and the importance of regions in this network for introspection and social emotion processing, areas affected in ASD. However, study of this network has largely been limited to adults; research earlier in development is lacking. The objective of this study was to examine DMN connectivity in children/adolescents with ASD. METHODS: A total of 115 children/adolescents, aged 6 to 17 years (71 males with ASD and 44 group age-matched TD males) were included in these analyses. We examined group differences in (1) functional connectivity between the posterior cingulate cortex and regions across the brain, (2) connectivity within the DMN as a function of age and intelligence quotient (IQ), and (3) the association between DMN connectivity and empathic accuracy. RESULTS: Individuals with ASD, relative to controls, showed either stronger or weaker connectivity between the posterior cingulate cortex (PCC) and DMN regions, depending on the region, but also showed stronger connectivity with non-DMN regions. A significant group-by-age interaction was observed in functional connectivity between the PCC and medial prefrontal cortex; connectivity increased with age in controls, but decreased in individuals with ASD. No effects of IQ were found. There was a significant group difference in the relation between DMN connectivity and empathic accuracy. INTERPRETATION: Differences in functional connectivity may suggest the presence of neural atypicalities that impact the development of typical connectivity in ASD. In addition to affecting DMN dynamics, these atypicalities may also impact social-cognitive abilities.
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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".