Attention deficit hyperactivity disorder (ADHD), in particular, is increasingly recognized as being heterogeneous, making it challenging to discover biomarkers and create guidelines for therapeutic therapy, according to a study. Researchers have historically tried classifying ADHD patients into meaningful subgroups by applying analytical clustering methods to various data sets. However, these studies typically employ algorithmic methods that do not make connections between behavior indicators, neurocognition, and genetic make-up and assume that group membership is error-free.