Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls - Holmes, Scott; Mar'i, Joud; Simons, Laura E.; Zurakowski, David; LeBel, Alyssa Ann; O'Brien, Michael; Borsook, David (Frontiers in Pain Research, 2022)
Post-traumatic headache (PTH) is a challenging clinical condition to identify and treat as it integrates multiple subjectively defined symptoms with underlying physiological processes. The precise mechanisms underlying PTH are unclear, and it remains to be understood how to integrate the patient experience with underlying biology when attempting to classify persons with PTH, particularly in the pediatric setting where patient self-report may be highly variable. The objective of this investigation was to evaluate the use of different machine learning classifiers to differentiate pediatric subjects with PTH from healthy controls using behavioral data from self-report questionnaires reflecting concussion symptoms, mental health, and the participants pain experience, as well as structural brain imaging from cortical and sub-cortical locations. Findings showed that behavioral data, alongside brain imaging, survived data reduction methods and both contributed towards final models. The behavioral data was focused on the pain-experience, integrating both the child and the parent perspective. Brain imaging features produced two unique clusters reflecting regions previously found in mTBI and PTH. Affinity based propagation analysis demonstrated that behavioral data remained independent relative to neuroimaging data, suggesting there is a role for both behavioral and brain imaging data when attempting to classify children with PTH.