Tackling hypo and hyper sensory processing heterogeneity in autism: From clinical stratification to genetic pathways
Bibliographic record
Abstract
As an integral part of autism spectrum symptoms, sensory processing issues including both hypo and hyper sensory sensitivities. These sensory specificities may result from an excitation/inhibition imbalance with a poorly understood of their level of convergence with genetic alterations in GABA-ergic and glutamatergic pathways. In our study, we aimed to characterize the hypo/hyper-sensory profile among autistic individuals. We then explored its link with the burden of deleterious mutations in a subset of individuals with available whole-genome sequencing data. To characterize the hypo/hyper-sensory profile, the differential Short Sensory Profile (dSSP) was defined as a normalized and centralized hypo/hypersensitivity ratio from the Short Sensory Profile (SSP). Including 1136 participants (533 autistic individuals, 210 first-degree relatives, and 267 controls) from two independent study samples (PARIS and LEAP), we observed a statistically significant dSSP mean difference between autistic individuals and controls, driven mostly by a high dSSP variability, with an intermediated profile represented by relatives. Our genetic analysis tended to associate the dSSP and the hyposensitivity with mutations of the GABAergic pathway. The major limitation was the dSSP difficulty to discriminate subjects with a similar quantum of hypo- and hyper-sensory symptoms to those with no such symptoms, resulting both in a similar ratio score of 0. However, the dSSP could be a relevant clinical score, and combined with additional sensory descriptions, genetics and endophenotypic substrates, will improve the exploration of the underlying neurobiological mechanisms of sensory processing differences in autism spectrum.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".