Auditory sustained potential as a biomarker of language functioning in children with autism and its implication in clinical trials
Bibliographic record
Abstract
Abstract Language impairment is the most frequently reported co-occurring condition in autism; however, its neural mechanisms are not well understood. A potential neural biomarker associated with language in autism can be a 40Hz Auditory Steady-State Response (ASSR) triggered by periodic click trains which in electroencephalogram (EEG) evokes two types of responses – 40Hz steady-state gamma response (or ASSR) and sustained potential (SP). Although these responses represent low-level auditory processing, they correspond to different stages of sound perception/analysis and are essential in processing of spectrally/temporally complex sounds, including speech. However, until now there were no studies focusing on the potential of these responses to serve as objective measures in clinical trials. This open-label clinical trial evaluated the effects of the probiotic beverage supplement Bio-K+ in children with autism. Participants were assessed at three timepoints: T0 (baseline), T14 (14 weeks after treatment initiation), and T22 (8 weeks post-treatment, during the ‘wash-out’ phase), including EEG 40Hz ASSR and behavioral phenotyping. First, at T0 we showed a reduction of SP amplitude in children with autism compared to typically-developing (TD) controls, and this reduction was associated with lower language skills. Second, the amplitude of SP significantly changed during the treatment period: by T14 it became similar to that of TD children. Finally, these changes in the amplitude of SP were associated with improvement in language skills. Importantly, we showed that this biomarker as well as its change was related specifically to language, but not to other behavioral measures.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".