Investigating the Effect of Electrical Stimulation (tDCS) of the Prefrontal Cortex of the Brain on the Improvement of Behavioral and Neurological Symptoms of Children with Specific Learning Disabilities
Bibliographic record
Abstract
The aim of the current research was to investigate the effect of electrical stimulation of the dorsolateral prefrontal cortex on improving behavioral and neuropsychological symptoms in children with specific learning disorders. The research method was quasi-experimental. The study population comprised all children aged 7 to 12 years with specific learning disorders attending a private psychotherapy center in Tehran during 2022. From this group, 20 individuals were selected as the statistical sample using convenient random sampling, with 10 placed in the experimental group and 10 in the control group. Research tools included an electrical stimulation program for improving behavioral and neuropsychological symptoms in children with specific learning disorders, the Symptom Checklist-90-Revised (SCL-90R), and the Conners' Neuropsychological Questionnaire (2004). Statistical data analysis was performed using SPSS software and mixed analysis of variance with repeated measurements. The findings showed significant differences between the behavioral symptom scores and neuropsychological signs of the experimental and control groups in the pre-test and post-test phases. The results indicated that electrical stimulation of the dorsolateral prefrontal cortex had a significant effect on reducing both behavioral symptoms and neuropsychological signs in the experimental group. The effectiveness of this intervention was also sustained in the follow-up phase according to the Bonferroni post-hoc test. Based on the findings, it can be concluded that electrical stimulation of the dorsolateral prefrontal cortex can be used to reduce behavioral symptoms and neuropsychological signs in students with specific learning disorders. Therefore, it is suggested that this method be used in child and adolescent counseling centers and rehabilitation facilities.
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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 | 0.001 |
| 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".