Using micro-cognition biomarkers of neurosystem dysfunction to redefine ADHD subtypes: A scalable digital path to diagnosis based on brain function

Abstract

Symptom-based diagnosis does not align with underlying neruropathology, confounding new treatment development and treatment selection for individual patients. Using high precision micro-cognition biomarkers of neurosystem dysfunction acquired during digital neurotherapy (DNT), we characterized subgroups of ADHD children with different neuropathology. K-means clustering applied to 69 children 6–9 years old with ADHD using performance variables from a Go/NoGo test normalized against 58 typically developing (TD) children identified four subgroups that were validated and further characterized by micro-cognition biomarkers extracted from thousands of responses during the DNT. The clusters differed on emblematic features of ADHD. Cluster 4 showed poor response inhibition and inconsistent attention. Cluster 3 showed only poor response inhibition and the other two showed neither. Cluster 2 showed faster and more consistent responses, higher detection of simple targets and better working memory than TD children but marked performance decrements when required to track multiple targets or ignore distractors. Cluster 1 showed much greater ability recognizing members of abstract categories rather than natural categories that children learn through physical interaction with the environment while Cluster 4 was the opposite. Fine-grained, low-cost, noninvasive, and scalable digital micro-cognition biomarkers can identify patients with the same symptom-based diagnosis but differing neuropathology.