Neurofeedback for autism spectrum disorder: Current evidence, challenges, and future directions

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Authors: Yan Zhang, Junjie Wang, Hao-Yu Xing, Juan Yan
Cited by: 0
References: 11

Abstract

Neurofeedback therapy (NFT) has emerged as a promising noninvasive intervention for autism spectrum disorder (ASD), targeting core symptoms such as social communication deficits and emotional dysregulation. This editorial synthesizes findings from recent studies, including Wang et al ’s retrospective analysis (2025), which reported improvements in Social Responsiveness Scale and Aberrant Behavior Checklist scores following NFT combined with conventional therapy. Mechanistically, NFT may modulate prefrontal gamma-band activity, enhances neuroplasticity in social brain networks (e.g., default mode network, a brain network involved in social cognition), and optimizes cognitive processing via event-related potential changes (e.g., shortened P300 latency). Emerging trends include hybrid approaches (e.g., NFT with repetitive transcranial magnetic stimulation and artificial intelligence-driven protocols). However, challenges persist in protocol standardization, long-term efficacy validation, and biomarker identification. Future research must prioritize large-scale randomized trials, neuromarker discovery, and individualized protocols to establish NFT as a viable component of precision psychiatry for ASD.

Fields of Study & Subjects

Autism Spectrum Disorder Research EEG and Brain-Computer Interfaces Genetics and Neurodevelopmental Disorders

Publication details

Journal / source
World Journal of Psychiatry
Volume
16
Issue
2
Pages
114358–114358
Publication date
29 January 2026
Language
EN