Pediatric Chronic Headache and COVID-19: Use of Machine Learning and Biobehavioral Analysis to Classify Headache Mechanism and Optimize Treatment Course.

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Chronic pain experienced by children has the potential to persist into adulthood and drive drug addiction, mental health problems and suicidal behavior. The level of pain reported by children who experience headache is immense and likely poorly estimated based on lowered ability of children to articulate symptoms, potential malingering or underreporting of pain symptoms, and extensive variability in physical growth and brain development. Headache is a frequently reported and poorly understood primary and secondary disorder in pediatric subjects. The diverse presentation of headache underscores the potential for distinct mechanisms of headache presentation, thus placing emphasis on tailored treatment options. There is clear need to better define childhood headache in terms of the clinical presentation and underlying pain physiology. Our hypothesis is that each clinical headache disorder can be defined by unique biobehavioral characteristics. In the proposed research program, we (Aim 1) evaluate the clinical and behavioral elements of each headache disorder and pain modulation, (Aim 2) the biobehavioral signature of treatment refractory headache, and (Aim 3) develop machine learning classifiers to understand the features that differentiate headache subtype and treatment resistance. This study is likely to yield highly relevant information that will contribute towards identifying and treating headache disorders in children, identifying unique characteristics of headache and pain processing, and (3) outline biobehavioral targets for different headache disorders. Data from this investigation is likely to contribute greatly towards the treatment of pediatric pain disorders.