A Machine Learning Approach for Predicting tDCS Treatment Outcomes of Adolescents With Autism Spectrum Disorders

Summary

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by disturbances in communication, poor social skills, and aberrant behaviors. Particularly detrimental are the presence of restricted and repetitive stereotyped behaviors and uncontrollable temper outbursts over trivial changes in the environment, which often cause emotional stress for the children, their families, schools and neighborhood communities.

Fundamental to these cognitive and behavioral problems is the disordered cortical connectivity and resultant executive dysfunction that underpin the use of effective strategies to integrate information across contexts. Brain connectivity problems affect the rate at which information travels across the brain. Slow processing speed relates to a reduced capacity of executive function to recall and formulate thoughts and actions automatically, with the result that autistic children with poor processing speed have great difficulty learning or perceiving relationships across multiple experiences. In consequence, these children compensate for the impaired ability to integrate information from the environment by memorizing visual details or individual rules from each situation. This explains why children with autism tend to follow routines in precise detail and show great distress over seemingly trivial changes in the environment.

To date, there is no known cure for ASD, and the disorder remains a highly disabling condition. Recently, a non-invasive brain stimulation technique, transcranial direct current Stimulation (tDCS) has shown great promise as a potentially effective and costeffective tool for reducing core symptoms such as anxiety, aggression, impulsivity, and inattention in patients with autism. This technique has been shown to modify behavior by inducing changes in cortical excitability and enhancing connectivity between the targeted brain areas. However, not all ASD patients respond to this intervention the same way and predicting the behavioral impact of tDCS in patients with ASD remains a clinical challenge. This proposed study thus aims to address these challenges by determining whether resting-state EEG and clinical data at baseline can be used to differentiate responders from non-responders to tDCS treatment. Findings from the study will provide new guidance for designing intervention programs for individuals with ASD.

Conditions

• Transcranial Direct Current Stimulation
• Autistic Disorders Spectrum
• Electroencephalography
• Machine Learning

Interventions

DEVICE

tDCS

Active-tDCS over 10 sessions in 2 weeks (once per day, for 10 consecutive working days), while performing the executive function training tasks. Also, participants will complete an online cognitive training program while they receive active tDCS stimulation for 10 training sessions. The training session will last for 20 minutes and the online cognitive training program is comprised of 5 exercises targeting at information processing speed and executive function capacities. Each exercise lasts for approximately 4 minutes, totaling approximately 20 minutes.

Sponsors & Collaborators

• Chinese University of Hong Kong

  collaborator OTHER

• The Hong Kong Polytechnic University

  lead OTHER

Principal Investigators

• Yvonne Han, PhD · The Hong Kong Polytechnic University

Study Design

Allocation

NA

Purpose

TREATMENT

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

12 Years

Max Age

22 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2022-01-05

Primary Completion

2026-06-30

Completion

2026-06-30

Countries

• Hong Kong

Study Locations