Conceptio › Archive › ClinicalTrials.gov
ClinicalTrials.gov public full text Public Domain

Individualized Alpha Neurofeedback for ADHD

The University of Hong Kong · 2024
ClinicalTrials.gov · Datasets · License: Public Domain · 2024
Open Source ↗
adhdneurofeedback
Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders, characterised by inattention, hyperactivity, and impulsivity. Although pharmacotherapy is considered the first-line treatment for ADHD at all ages (at least for severe cases), non-pharmacological therapies might be equally effective without the risk of drug side effects. Some studies have shown that electroencephalographic (EEG) neurofeedback improves parent-rated ADHD symptoms in children and adolescents. However, whether neurofeedback is an effective treatment for ADHD is still under debate. Several issues may hinder the evaluation of the effectiveness of neurofeedback treatment in previous studies. Firstly, previous neurofeedback studies did not utilize effective neurophysiological markers for ADHD. Theta/Beta ratio, the most common neurofeedback marker for treating ADHD in the past two decades, has recently been suggested to be only weakly correlated to individuals' attention. Secondly, previous studies mostly used the norm of the EEG markers in age/gender matched healthy children as the training target for ADHD children, which largely ignored the individual variations in EEG acquisition. Third, most of the previous studies lack a rigorous study design, for comparing neurofeedback with a 'placebo' condition and evaluating its specific and non-specific effects. In the current studies, we propose to conduct a sham-controlled, triple-blind trial to evaluate the effectiveness of an individual-based neurofeedback treatment for ADHD children and adolescents. The EEG marker for neurofeedback in treating ADHD would be the individualized lower/higher alpha band power, based on the recent methodological advances in EEG spectrum processing (1/f model fit and individualized peak alpha frequency modelling) . The training target will be individualized and defined according to the neurophysiological pattern shown in pre-training resting-state conditions, and thus each p
Conceptio Open Archive

Analyze & explore related research in Conceptio

Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.

This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗

Related documents

Record · ID 1093065
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive