TY - JOUR
T1 - Biotyping in psychosis
T2 - using multiple computational approaches with one data set
AU - Tamminga, Carol A.
AU - Clementz, Brett A.
AU - Pearlson, Godfrey
AU - Keshavan, Macheri
AU - Gershon, Elliot S.
AU - Ivleva, Elena I.
AU - McDowell, Jennifer
AU - Meda, Shashwath A.
AU - Keedy, Sarah
AU - Calhoun, Vince D.
AU - Lizano, Paulo
AU - Bishop, Jeffrey R.
AU - Hudgens-Haney, Matthew
AU - Alliey-Rodriguez, Ney
AU - Asif, Huma
AU - Gibbons, Robert
N1 - Publisher Copyright:
© 2020, The Author(s), under exclusive licence to American College of Neuropsychopharmacology.
PY - 2021/1
Y1 - 2021/1
N2 - Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.
AB - Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.
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U2 - 10.1038/s41386-020-00849-8
DO - 10.1038/s41386-020-00849-8
M3 - Review article
C2 - 32979849
AN - SCOPUS:85096511448
SN - 0893-133X
VL - 46
SP - 143
EP - 155
JO - Neuropsychopharmacology
JF - Neuropsychopharmacology
IS - 1
ER -