title: Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines.
abstract: Previous studies have reported abnormalities of white-matter diffusivity in pediatric bipolar disorder. However, it has not been established whether these abnormalities are able to distinguish individual subjects with pediatric bipolar disorder from healthy controls with a high specificity and sensitivity. Diffusion-weighted imaging scans were acquired from 16 youths diagnosed with DSM-IV bipolar disorder and 16 demographically matched healthy controls. Regional white matter tissue microstructural measurements such as fractional anisotropy, axial diffusivity and radial diffusivity were computed using an atlas-based approach. These measurements were used to 'train' a support vector machine (SVM) algorithm to predict new or 'unseen' subjects' diagnostic labels. The SVM algorithm predicted individual subjects with specificity=87.5%, sensitivity=68.75%, accuracy=78.12%, positive predictive value=84.62%, negative predictive value=73.68%, area under receiver operating characteristic curve (AUROC)=0.7812 and chi-square p-value=0.0012. A pattern of reduced regional white matter fractional anisotropy was observed in pediatric bipolar disorder patients. These results suggest that atlas-based diffusion weighted imaging measurements can distinguish individual pediatric bipolar disorder patients from healthy controls. Notably, from a clinical perspective these findings will contribute to the pathophysiological understanding of pediatric bipolar disorder.
authors.affiliation: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors.forename: Benson
authors.initials: B
authors.lastname: Mwangi
authors.affiliation: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors.forename: Mon-Ju
authors.initials: MJ
authors.lastname: Wu
authors.affiliation: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors.forename: Isabelle E
authors.initials: IE
authors.lastname: Bauer
authors.affiliation: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors.forename: Haina
authors.initials: H
authors.lastname: Modi
authors.affiliation: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors.forename: Cristian P
authors.initials: CP
authors.lastname: Zeni
authors.affiliation: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors.forename: Giovana B
authors.initials: GB
authors.lastname: Zunta-Soares
authors.affiliation: Department of Diagnostic & Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Khader M
authors.initials: KM
authors.lastname: Hasan
authors.affiliation: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors.forename: Jair C
authors.initials: JC
authors.lastname: Soares
mesh_terms: D000293:Adolescent; D016880:Anisotropy; D001714:Bipolar Disorder / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism*; D001921:Brain / Q000378:metabolism* / Q000473:pathology; D002648:Child; D039721:Diagnostic and Statistical Manual of Mental Disorders; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D060388:Support Vector Machine; D066127:White Matter / Q000378:metabolism / Q000473:pathology
keywords: Diffusion weighted imaging; Machine learning; Pattern classification; Pediatric bipolar disorder; Support vector machines