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Biotyping in psychosis: using multiple computational approaches with one data set


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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Fig. 1: Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) cartoon representing the path to psyhosis Biotypes.
Fig. 2: Average cortical grey matter reduction by Biotype (B-, B-2 and B-3) in the B-SNIP population.
Fig. 3: Big data routines directed to B-SNIP volumetric data.
Fig. 4: Gene enrichment in the default mode networks in psychosis.
Fig. 5: SVM classification results on regional homogeneity (ReHo) connectivity metrics.
Fig. 6: Effects of permutations on Minor Alle Frequency.
Fig. 7: These regions represent areas where high psychosis symptoms correlate significantly with low cortical thickness.
Fig. 8: PDS2 score comparisons across Biotypes.
Fig. 9: CRP and IL6 group comparisons.
Fig. 10: Choroid plexus group comparisons.
Fig. 11: Overall structural MRI (top plot) and cognition and neurophysiology (bottom plot) standard scores as a function of cognitive ability.
Fig. 12: MRI measures that deviate from the BANCC pattern.
Fig. 13: EEG measures that deviate from the BANCC pattern.


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CT takes overall responsibility for the paper and composed the Introduction and Discussion. Specific sections were composed by GP, SAM, VDC; ESG, NA-R, SK, HA; CT, RG, EII, MH-H; MK; JRB, PL; BAC, JM in order. All authors commented and edited the whole paper extensively.

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Correspondence to Carol A. Tamminga.

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Tamminga, C.A., Clementz, B.A., Pearlson, G. et al. Biotyping in psychosis: using multiple computational approaches with one data set. Neuropsychopharmacol. 46, 143–155 (2021).

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