Bibliographic record
Abstract
BACKGROUND: Recent studies have reported abnormal functional connectivity patterns in the brains of people with autism that may be accompanied by decreases in white matter integrity. Since autism is a developmental disorder, we aim to investigate the nature and location of decreases in white and grey matter integrity in an adolescent sample while accounting for age. METHODS: We used structural (T1) imaging to study brain volumetrics and diffusion tensor imaging (DTI) to investigate white and grey matter integrity in people with autism. We obtained magnetic resonance images for adolescents aged 12-18 years with high-functioning autism and from matched controls. Fractional anisotropy and mean diffusivity, as well as grey and white matter volumetrics were analyzed. RESULTS: There were 17 participants with autism and 25 matched controls included in this study. Participants with autism had lower fractional anisotropy in the left and right superior and inferior longitudinal fasciculus, but this effect was not significant after adjusting for age and intelligence quotient (IQ). The kurtosis of the white matter fractional anisotropy probability distribution was higher in this participant group, with and without adjustment for age and IQ. Most notably, however, the mean diffusivity levels were markedly increased in the autism group throughout the brain, and the mean diffusivity probability distributions of both grey and white matter were shifted toward a higher value, particularly with age and IQ adjustment. No volumetric differences in grey and white matter were found. LIMITATIONS: We corrected for age and IQ using a linear model. The study was also limited by its sample size, investigated age range and cross-sectional design. CONCLUSION: The findings suggest that autism is characterized by a generalized reduction of white matter integrity that is associated with an increase of interstitial space. The generalized manifestation of the white matter abnormalities provides an important new perspective on autism as a connectivity disorder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".