Decoding the neural basis of sensory phenotypes in Autism
Bibliographic record
Abstract
Background: Differences in sensory processing are a defining characteristic of autism, affecting up to 87% of autistic individuals. These differences cause widespread perceptual changes that can negatively impact cognition, development, and daily functioning. Recent research identified five sensory processing phenotypes with varied behavioural presentations; however, their neural basis remains unclear. This study aims to ground these sensory phenotypes in unique patterns of functional connectivity. Methods: We analyzed data from 146 autistic participants in the Province of Ontario Neurodevelopmental Network. We classified participants into sensory phenotypes using k-means clustering of scores from the Short Sensory Profile. We then computed a connectivity matrix from 200 cortical and 32 subcortical regions and calculated graph-theoretic measures (betweenness centrality, strength, local efficiency, clustering coefficient) to assess information exchange between these regions. We then trained machine learning models to use these measures to classify between all pairs of sensory phenotypes. Results: We replicated that our sample of autistic participants was best categorized into five sensory phenotypes. The machine learning models distinguished 7/10 phenotype pairs using graph-theoretic measures (p < 0.005). Information exchange within and between the somatomotor network, orbitofrontal cortex, posterior parietal cortex, prefrontal cortex and subcortical areas were highly predictive of sensory phenotype. Conclusions: This study shows that distinct sensory phenotypes in autism correspond with unique patterns of functional connectivity. Cortical, subcortical, and network-level connectivity all play a role in shaping distinct sensory processing styles in autism. These findings lay the groundwork for understanding these phenotypes and highlight opportunities to develop interventions in cases of maladaptive sensory processing
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".