Functional imaging derived ADHD biotypes based on deep clustering: a study on personalized medication therapy guidance.

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing ADHD biotypes may lead to more individualized, biotype-guided treatment.

METHODS

Here we proposed a graph convolution network for biological subtype detection (GCN-BSD) using both functional network connectivity (FNC) and non-imaging phenotypic data for ADHD biotype. We applied GCN-BSD to ADHD patients from the ABCD study as the discovery dataset and a validation ADHD dataset with longitudinal medication treatment from Peking University Sixth Hospital.

FINDINGS

We identified two biotypes based on 1069 ADHD patients selected from Adolescent Brain and Cognitive Development (ABCD) study, which were validated on independent ADHD adolescents undergoing longitudinal medication treatment (n = 130). Interestingly, in addition to differences in cognitive performance and hyperactivity/impulsivity symptoms, biotype 1 demonstrated a significantly better recovery rate in psychosomatic problems score (p < 0.05, baseline symptom score adjusted) when treated with methylphenidate than with atomoxetine.

INTERPRETATION

Our results suggested that such an imaging-driven, biotype-guided approaches hold promise for facilitating personalized treatment of ADHD and exploring possible boundaries through innovative deep learning algorithms to improve medication treatment effectiveness.

FUNDING

Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, the Startup Funds for Talents at Beijing Normal University, China Postdoctoral Science Foundation, and the National Institutes of Health.