Repetitive Transcranial Magnetic Stimulation for the Treatment of Executive Function Deficits in Autism Spectrum Disorder: Clinical Trial Approach
Bibliographic record
Abstract
OBJECTIVE: Executive function (EF) deficits in patients with autism spectrum disorder (ASD) are ubiquitous and understudied. Further, there are no effective, neuroscience-based treatments to address this impairing feature of ASD. Repetitive transcranial magnetic stimulation (rTMS) has demonstrated promise in addressing EF deficits in adult neuropsychiatric disorders. This article will outline the design of a novel randomized-controlled trial of bilateral, 20 Hz, rTMS applied to the dorsolateral prefrontal cortex (DLPFC) for treatment of EF deficits in ASD that is currently ongoing. We describe prior therapeutic rTMS research for ASD and prior rTMS trials targeting EFs in adult neuropsychiatric disorders. A neurophysiological rationale for rTMS treatment of EF deficits in ASD is presented. METHODS: An ongoing protocol will enroll participants aged 16-35 with ASD and no intellectual disability. Psychotropic medications will be continued during the 4-week trial of active 20 Hz versus sham rTMS applied to the DLPFC. Twenty, active treatment sessions consisting of 25 stimulation trains at a 90% motor threshold will be administered. The primary outcome measure is the Cambridge Neuropsychological Test Automated Battery (CANTAB) spatial working memory task. At present, recruitment, enrollment, and treatment within the described clinical trial are ongoing. CONCLUSIONS: EF deficits are common and impairing symptoms of ASD. There are no evidence-based treatments for EF deficits in ASD. The protocol described here will provide important preliminary data on the feasibility and efficacy of 20 Hz rTMS to DLPFC for EF deficits in ASD.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".