title: Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning.
abstract: Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and 'skeletonized' white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The 'skeletonized' white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity=92%, specificity=97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an individual subject level. We suggest a strong clinical utility of the proposed approach in defining and validating biologically meaningful and less heterogeneous clinical sub-phenotypes of major psychiatric disorders.
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. Electronic address: benson.irungu@uth.tmc.edu.
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: 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: Ives C
authors.initials: IC
authors.lastname: Passos
authors.affiliation: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors.forename: Marsal
authors.initials: M
authors.lastname: Sanches
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: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors.forename: Thomas D
authors.initials: TD
authors.lastname: Meyer
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: D000328:Adult; D001714:Bipolar Disorder / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000503:physiopathology; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D059906:Neuroimaging / Q000379:methods*; D010641:Phenotype; D012680:Sensitivity and Specificity; D066127:White Matter / Q000000981:diagnostic imaging*
keywords: Big data; Bipolar disorder; Heterogeneity; Machine learning; Neuroimaging; Neuropsychology; Research Domain Criteria (RDoC)