Altered cingulum bundle microstructure in autism spectrum disorder
Bibliographic record
Abstract
OBJECTIVE: Here, we examined the cingulum bundle, a long-range white matter tract mediating dorsal limbic connectivity, using diffusion tensor imaging (DTI) tractography, in children and adolescents with autism spectrum disorder (ASD) versus controls. We hypothesised that cingulum bundle microstructure would be altered in ASD, based on evidence implicating abnormal white matter connectivity in this disorder. METHODS: DTI data were acquired for 19 ASD participants (IQ ⩾ 70; 7-18 years; mean = 12.4 ± 3.1) and 16 age-matched controls (7-18 years; mean = 12.3 ± 3.6) on a 3 T magnetic resonance imaging system. Deterministic tractography was used to isolate the cingulum bundle. Left and right cingulum bundles were examined for differences in several DTI metrics in ASD children/adolescents versus controls, including: fractional anisotropy (FA), mean, axial, and radial diffusivity. RESULTS: Significant age × group interaction effects were found for all DTI metrics (mean diffusivity: F 1,28 = 9.5, p = 0.005, radial diffusivity: F 1,28 = 7.8, p = 0.009, axial diffusivity: F 1,28 = 5.2, p = 0.03, FA: F 1,28 = 4.4, p = 0.04). Interaction effects were driven by increases in cingulum bundle diffusivity (mean, radial, and axial diffusivity), and decreased FA, in younger ASD participants within our sample versus controls. CONCLUSION: Our results point to immature microstructural organisation of the cingulum bundle in ASD, particularly during the early years of life, with implications for limbic network synchronisation and complex socio-emotional performance.
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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".