Neurofeedback Training for Autistic Children

Official title

Developing an EEG-fNIRS Neurofeedback Application for Brain Training for Autistic Children

Overview

The goal of this study is to learn if a new brain training method, called combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) neurofeedback, can improve thinking, emotions, and social functioning in children with autism spectrum disorder (ASD). It will also learn if this training is practical and safe to use with children in Hong Kong. The main questions this study aims to answer are: * Does combined EEG-fNIRS neurofeedback improve attention, emotion regulation, and social skills in children with ASD? * Is this type of neurofeedback training feasible and well-tolerated by children? Researchers will compare the new combined EEG-fNIRS training with single EEG or fNIRS training to see if it provides additional benefits. Participants will: .Receive sessions of EEG-fNIRS neurofeedback training. .Complete assessments of thinking skills, emotional regulation, and social functioning before and after training.

Detailed description

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition characterized by difficulties in social communication and interaction, often accompanied by cognitive and emotional regulation challenges. In Hong Kong and many other countries, ASD is increasingly prevalent. Despite this, the brain health of autistic individuals has been relatively neglected in both healthcare systems and public policies. There is also a lack of approaches and technologies that directly intervene with brain function. Since many autistic children experience poor vocational and health outcomes in adulthood, there is a strong need to develop effective and accessible neuroscience-based treatments.

This project aims to apply cutting-edge neuroscientific methods to develop an innovative closed-loop brain training intervention for children with ASD. The intervention will combine electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) in a unified neurofeedback training system. Neurofeedback training teaches individuals to self-regulate brain activity by providing real-time feedback. In the traditional neurofeedback study, EEG has been used to guide neurofeedback by monitoring electrical activity in the brain, while more recently fNIRS has been used to track hemodynamic activity. However, no existing neurofeedback system has integrated these two modalities. Combining EEG and fNIRS provides an opportunity to enhance neurovascular coupling, the relationship between neural activity and blood flow, which is often altered in neuropsychiatric conditions such as autism.

The proposed neurofeedback application will include multiple training modules designed to address cognitive, emotional, and social difficulties common in autism. The cognitive training module will target brain activity patterns associated with attention and executive function. The affective training module will focus on modulating frontal brain activity linked to emotional regulation. The social training module will aim to enhance neural and hemodynamic activity associated with social cognition and communication. By integrating both EEG and fNIRS indices, the system will encourage children to regulate electrical and hemodynamic activity simultaneously, which cannot be achieved using either modality alone.

To maximize engagement, the application will incorporate ecologically valid feedback stimuli and reward-based learning principles. Instead of relying solely on abstract indicators such as bars or tones, the feedback will involve intrinsically rewarding stimuli, such as videos or positive visual cues, to increase motivation and adherence. The training difficulty will be adjusted progressively based on individual performance to ensure sustained engagement and improvement.

In addition, the system will be developed as a cross-device application using open-source lab streaming layer (LSL) software, ensuring compatibility with a wide range of EEG and fNIRS devices. The hardware and software will be optimized to ensure high-quality signals, including the use of shielded wet electrodes for EEG to reduce noise and short-separation channels in fNIRS to minimize extracerebral signal contamination. These features will allow neurofeedback training to be conducted with minimal environmental interference, enhancing both reliability and clinical applicability.

Through this proof-of-concept project, this project aims to establish the feasibility of combined EEG-fNIRS neurofeedback as a novel form of brain training for autistic children. If successful, this approach has the potential to offer a comprehensive, technology-based neurorehabilitation solution that can improve functional outcomes, reduce healthcare burdens, and foster innovation in neurotechnology in Hong Kong.

Interventions

• Device EEG and fNIRS
  For EEG and fNIRS, EEG signals will be recorded using the ANT Neuro eego rt 8 amplifier device (ANT Neuro, Hengelo, The Netherlands), with electrodes placed at C3, C4, F3, F4, Fpz, M1, M2, and GND (ground). fNIRS signals will be recorded using the Artinis Brite Lite fNIRS device(Artinis Medical Systems, The Netherlands). The overall channel configuration consists of eight sources and four detectors. Among these, four sources (T2a-d) and four detectors (R1-4) form four short-separation channels,
• Device EEG
  EEG signals will be recorded using the ANT Neuro eego rt 8 amplifier device (ANT Neuro, Hengelo, The Netherlands), with electrodes placed at C3, C4, F3, F4, Fpz, M1, M2, and GND (ground).
• Device fNIRS
  fNIRS signals will be recorded using the Artinis Brite Lite fNIRS device(Artinis Medical Systems, The Netherlands). The overall channel configuration consists of eight sources and four detectors. Among these, four sources (T2a-d) and four detectors (R1-4) form four short-separation channels, while the remaining four sources and four detectors constitute six long-separation channels (T1-R1, T3-R1, T3-R2, T4-R3, T5-R3, T5-R4). The overall configuration is approximately arranged in two L-shaped lay

Primary outcome measures

• Effectiveness of treatments for autistic individuals [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Social behavior and Social impairments [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Executive function in children and adolescents [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Anxiety and depression symptoms in children and adolescents [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]

Secondary outcome measures (10)

• Go/No-go(post; RT) [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Go/No-go (post; Accuracy) [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Sternberg Working Memory Task (post; RT) [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Sternberg Working Memory Task (post; Accuracy) [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Task Switching (post; RT) [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Task Switching (post; Accuracy) [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Child Eyes Test (post; RT) [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Child Eyes Test (post; Accuracy) [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Facial Emotion Recognition Task (post; RT) [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]
• Facial Emotion Recognition Task (post; Accuracy) [Time frame: Within 1 week before the first training session, and within 1 week after the last training session]

Eligibility criteria

Inclusion criteria

• Children aged 8 to 12 years
• Previous diagnosis of autism spectrum disorder (ASD) or Asperger's syndrome
• No intellectual impairment or studying in mainstream schools
• Right-handedness
• Normal or corrected-to-normal vision

Exclusion criteria

\- Not meeting any of the above inclusion criteria

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Triple blind

Primary purpose

Treatment

Study locations

Hong Kong · 1 center

• Faculty of Education And Human Development OF The Educational University Of Hong Kong — Hong Kong

Publications

• Wang SY, Lin IM, Fan SY, Tsai YC, Yen CF, Yeh YC, Huang MF, Lee Y, Chiu NM, Hung CF, Wang PW, Liu TL, Lin HC. The effects of alpha asymmetry and high-beta down-training neurofeedback for patients with the major depressive disorder and anxiety symptoms. J Affect Disord. 2019 Oct 1;257:287-296. doi: 10.1016/j.jad.2019.07.026. Epub 2019 Jul 5. PMID 31302517
• Van Doren J, Arns M, Heinrich H, Vollebregt MA, Strehl U, K Loo S. Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis. Eur Child Adolesc Psychiatry. 2019 Mar;28(3):293-305. doi: 10.1007/s00787-018-1121-4. Epub 2018 Feb 14. PMID 29445867
• Trambaiolli LR, Kohl SH, Linden DEJ, Mehler DMA. Neurofeedback training in major depressive disorder: A systematic review of clinical efficacy, study quality and reporting practices. Neurosci Biobehav Rev. 2021 Jun;125:33-56. doi: 10.1016/j.neubiorev.2021.02.015. Epub 2021 Feb 12. PMID 33587957
• The Government of the Hong Kong Special Administrative Region. (2019). EDB to enhance support for students with autism spectrum disorders. Retrieved August 24, 2022, from https://www.info.gov.hk/gia/general/201910/03/P2019100300291.htm?fontSize=1.
• Steingrimsson S, Bilonic G, Ekelund AC, Larson T, Stadig I, Svensson M, Vukovic IS, Wartenberg C, Wrede O, Bernhardsson S. Electroencephalography-based neurofeedback as treatment for post-traumatic stress disorder: A systematic review and meta-analysis. Eur Psychiatry. 2020 Jan 31;63(1):e7. doi: 10.1192/j.eurpsy.2019.7. PMID 32093790
• Shaffer RC, Pedapati EV, Shic F, Gaietto K, Bowers K, Wink LK, Erickson CA. Brief Report: Diminished Gaze Preference for Dynamic Social Interaction Scenes in Youth with Autism Spectrum Disorders. J Autism Dev Disord. 2017 Feb;47(2):506-513. doi: 10.1007/s10803-016-2975-2. PMID 27878742
• Russo, G. M., Balkin, R. S., & Lenz, A. S. (2022). A meta-analysis of neurofeedback for treating anxiety-spectrum disorders. Journal of Counseling & Development, 100(3), 236-251.
• Rosson S, de Filippis R, Croatto G, Collantoni E, Pallottino S, Guinart D, Brunoni AR, Dell'Osso B, Pigato G, Hyde J, Brandt V, Cortese S, Fiedorowicz JG, Petrides G, Correll CU, Solmi M. Brain stimulation and other biological non-pharmacological interventions in mental disorders: An umbrella review. Neurosci Biobehav Rev. 2022 Aug;139:104743. doi: 10.1016/j.neubiorev.2022.104743. Epub 2022 Jun 14 PMID 35714757

Identifiers

NCT: NCT07149974 · 2022-2023-0505 · ITS/077/22

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗