Multivariate Classification of Structural MRI Data Detects Chronic Low Back Pain. (17th December 2012)

• Record Type: Journal Article
• Authors: Ung, Hoameng; Brown, Justin E.; Johnson, Kevin A.; Younger, Jarred; Hush, Julia; Mackey, Sean
• Is Part Of: Cerebral cortex. Volume 24:Number 4(2014:Apr.)
• Page Start: 1037
• Page End: 1044
• Publication Date: 2012-12-17
• Subjects: classification -- low back pain -- structural imaging -- support vector machine; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhs378
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract : Chronic low back pain (cLBP) has a tremendous personal and socioeconomic impact, yet the underlying pathology remains a mystery in the majority of cases. An objective measure of this condition, that augments self-report of pain, could have profound implications for diagnostic characterization and therapeutic development. Contemporary research indicates that cLBP is associated with abnormal brain structure and function. Multivariate analyses have shown potential to detect a number of neurological diseases based on structural neuroimaging. Therefore, we aimed to empirically evaluate such an approach in the detection of cLBP, with a goal to also explore the relevant neuroanatomy. We extracted brain gray matter (GM) density from magnetic resonance imaging scans of 47 patients with cLBP and 47 healthy controls. cLBP was classified with an accuracy of 76% by support vector machine analysis. Primary drivers of the classification included areas of the somatosensory, motor, and prefrontal cortices—all areas implicated in the pain experience. Differences in areas of the temporal lobe, including bordering the amygdala, medial orbital gyrus, cerebellum, and visual cortex, were also useful for the classification. Our findings suggest that cLBP is characterized by a pattern of GM changes that can have discriminative power and reflect relevant pathological brain morphology.