According to the new study published in the journal Frontier in Physiology, led by a University at Buffalo researcher, there is possibility of detecting the attention deficit hyperactivity disorder (ADHD), through machine learning.
The research is based on the machine learning classifiers to identify ADHD of adults, who were diagnosis with ADHD when they were kids. Acceding to Chris McNorgan, an assistant professor of psychology in the UB college of Arts and Science, “brain connectivity is a stable biomarker for ADHD, at least into childhood even when an individual’s behavior had become more typical, perhaps by adapting different strategies that obscure the underlying disorder”. The study is not only detecting ADHD but also help clinical targets treatments by understanding where patients sit on a broad spanning continuum.
ADHD is a most commonly diagnosed psychological disorder among school aged children, which is hard to identify.
- Chris McNorgan, Cary Judson, Dakota Handzlik, John G. Holden. Linking ADHD and Behavioral Assessment Through Identification of Shared Diagnostic Task-Based Functional Connections. Frontiers in Physiology, 2020; 11 DOI: 10.3389/fphys.2020.583005