Developmental changes of cortical white–gray contrast as predictors of autism diagnosis and severity
Bibliographic record
Abstract
Recent studies suggest that both cortical gray and white-matter microstructural characteristics are distinct for subjects with autism. There is a lack of evidence regarding how these characteristics change in a developmental context. We analysed a longitudinal/cross-sectional dataset of 402 magnetic resonance imaging (MRI) scans (171 subjects with autism and 231 with typical development) from the Autism Brain Imaging Data Exchange, cohorts I-II (ABIDE-I-II). In the longitudinal sample, we computed the rate of change in the white-gray contrast, a measure which has been related to age and cognitive performance, at the boundary of the cerebral cortex. Then, we devised an analogous metric for the cross-sectional sample of the ABIDE dataset to measure age-related differences in cortical contrast. Further, we developed a probabilistic model to predict the diagnostic group in the longitudinal sample of the cortical contrast change data, using results obtained from the cross-sectional sample. In both subsets, we observed a similar overall pattern of greater decrease within the autistic population in intensity contrast for most cortical regions (81%), with occasional increases, mostly in primary sensory regions. This pattern correlated well with raw and calibrated behavioural scores. The prediction results show 76% accuracy for the whole-cortex diagnostic prediction and 86% accuracy in prediction using the motor system alone. Our results support a contrast change analysis strategy that appears sensitive in predicting diagnostic outcome and symptom severity in autism spectrum disorder, and is readily extensible to other MRI-based studies of neurodevelopmental cohorts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".