A Machine Learning Approach for Predicting TDCS Treatment Outcomes of Adolescents with Autism Spectrum Disorders

Status: Recruiting
Location: See location...
Intervention Type: Device
Study Type: Interventional
Study Phase: Not Applicable
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.

Eligibility
Participation Requirements
Sex: All
Minimum Age: 12
Maximum Age: 22
Healthy Volunteers: f
View:

• Individuals who are confirmed by a clinical psychologist based on the Diagnostic and Statistical Manual of Mental Disorders-5th Ed (DSM-V) criteria of Autism spectrum disorder and structured interview with their parents or primary caregivers on their developmental history using the Autism Diagnostic Interview-Revised (ADI-R).

• Individuals with intelligence quotient above 60.

• Individuals who demonstrate the ability to comprehend testing and stimulation instructions.

Locations
Other Locations
Hong Kong Special Administrative Region
The Hong Kong Polytechnic University
RECRUITING
Hung Hom
Contact Information
Primary
Yvonne Han, PhD
yvonne.han@polyu.edu.hk
2766 7578
Backup
Melody Chan, PhD
mei-yan-melody.chan@connect.polyu.hk
Time Frame
Start Date: 2022-01-05
Estimated Completion Date: 2025-12
Participants
Target number of participants: 90
Treatments
Experimental: Responders vs Non-responders
After the tDCS outcome recorded immediately after tDCS treatment, participants will be categorized into responders and non-responders based on the percentage of change in the total SRS score (primary outcome). Participants that show reductions of at least 10% in the total SRS scores as compared to baseline scores will be considered responders.
Related Therapeutic Areas
Sponsors
Leads: The Hong Kong Polytechnic University
Collaborators: Chinese University of Hong Kong

This content was sourced from clinicaltrials.gov