Migraine Subclassification via a Data‐Driven Automated Approach Using Multimodality Factor Mixture Modeling of Brain Structure Measurements

• Published in: Headache Vol. 57; no. 7; pp. 1051 - 1064
• Main Authors: Schwedt, Todd J., Si, Bing, Li, Jing, Wu, Teresa, Chong, Catherine D.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.07.2017
• Subjects: Adult; allodynia; Automation; Bayes Theorem; Brain; brain structure; brain volume; Cerebral Cortex - diagnostic imaging; Classification; Cortex; cortical surface area; cortical thickness; Databases, Factual - statistics & numerical data; Diagnostic systems; Disability Evaluation; Documents; factor mixture model; Female; Headache; Heterogeneity; Humans; Hyperalgesia - complications; Image Processing, Computer-Assisted; Indexing; Magnetic Resonance Imaging; Male; Middle Aged; Migraine; Migraine Disorders - classification; Migraine Disorders - pathology; Migraine Disorders - physiopathology; Models, Statistical; multimodality factor mixture model; Neuroimaging; NMR; Nuclear magnetic resonance; Pain perception; Subgroups; Surveys and Questionnaires; allodynia; magnetic resonance imaging; migraine; brain volume; cortical thickness; factor mixture model; cortical surface area; headache; classification; brain structure; multimodality factor mixture model

Background The current subclassification of migraine is according to headache frequency and aura status. The variability in migraine symptoms, disease course, and response to treatment suggest the presence of additional heterogeneity or subclasses within migraine. Objective The study objective was t...