Effect of <scp>non‐invasive</scp> brain stimulation on behavior and serum brain‐derived neurotrophic factor and insulin‐like growth factor‐1 levels in autistic patients
Bibliographic record
Abstract
Aberrant neural connectivity and intra-cortical inhibitory dysfunction are key features of autism. Non-invasive brain stimulation (NIBS) protocols have been proposed that modulate this aberrant plasticity. However, additional investigations are needed to evaluate the impact of this intervention on biological biomarkers of the disease. We recently demonstrated alterations in serum insulin-like growth factor-1 (IGF-1) and brain-derived neurotrophic factor (BDNF) immunoreactivity in subjects with autism compared to controls. The aim of this pilot study was to explore the change in serum levels of the neurotrophic factors BDNF and IGF-1 in patients undergoing NIBS therapy. Sixteen subjects with autism spectrum disorder (ASD) were tested 1 week before and 1 week after NIBS to determine the short-term outcome on behavior using the total score on the autism behavior checklist, autism treatment evaluation checklist, clinical global impression severity and the autism diagnostic interview. ASD subjects younger than 11 years old (n = 11) were treated with transcranial direct current stimulation (tDCS), and those 11 years and older (n = 5) were treated with repetitive transcranial magnetic stimulation (rTMS). Serum levels of BDNF and IGF-1 were evaluated by Enzyme-Linked Immuno-Sorbent Assay before and after the intervention with NIBS. A significant reduction in scores on the clinical behavioral scales was observed in patients treated with NIBS (ABC-T p = .002, CGI-S p = .008, ADI-T and ATEC-T p < .0001). There was a trend towards reduced serum BDNF levels after NIBS (p = .061), while there was no change in IGF-1 levels. These data support further studies on the potential of BDNF as a biomarker to measure the effectiveness of NIBS in autism.
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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) | 0.001 | 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".