Neuroplasticity-based novel brain stimulation support intervention options for autistic population
Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS), introduced in 1985, has become a vital tool for investigating brain-behaviour relationships and therapeutic interventions. Repetitive TMS (rTMS) as a therapeutic tool has shown promise for various neuropsychiatric conditions, including autism, which affects approximately 1% of the global population. Evidence suggests that atypical neuroplasticity characterizes the neurobiology of autism. Recent studies using TMS paradigms like theta-burst stimulation (TBS) indicate an excessive neuroplasticity or hyper-plasticity in the form of an excessive long-term potentiation (LTP) in the motor cortex of autistic adults compared to neurotypical controls. Hyper-plasticity may negatively impact cognitive and behavioural outcomes. Our proposed neuroplasticity-based rTMS intervention protocols aim to address motor function, sensory sensitivities, and executive function difficulties in autistic adults. We present a testable framework to evaluate neuroplasticity in the motor, sensory, and dorsolateral prefrontal cortices, hypothesizing the presence of hyper-plasticity in autistic adults. We anticipate that this hyper-plasticity underpins motor, sensory, and executive function difficulties in autistic adults. Additionally, we propose investigating the efficacy of bilateral rTMS to reduce hyper-plasticity and improve these functions in autistic adults. This approach not only seeks to enhance therapeutic options but also provides biological insights into the brain mechanisms underlying some of the common autism-associated difficulties.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".