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Illness, at-risk and resilience neural markers of early-stage bipolar disorder.

Kangguang Lin,R. Shao,X. Geng,Kun Chen,R. Lu,Yanling Gao,Yanan Bi,Weicong Lu,Lijie Guan,Jiehua Kong,Gui-yun Xu,K. So

Published 2018 in Journal of Affective Disorders

ABSTRACT

BACKGROUND Current knowledge on objective and specific neural markers for bipolar risk and resilience-related processes is lacking, partly due to not subdividing high-risk individuals manifesting different levels of subclinical symptoms who possibly possess different levels of resilience. METHODS We delineated grey matter markers for bipolar illness, genetic high risk (endophenotype) and resilience, through comparing across 42 young non-comorbid bipolar patients, 42 healthy controls, and 72 diagnosis-free, medication-naive high-genetic-risk individuals subdivided into a combined-high-risk group who additionally manifested bipolar risk-relevant subsyndromes (N = 38), and an asymptomatic high-risk group (N = 34). Complementary analyses assessed the additional predictive and classification values of grey matter markers beyond those of clinical scores, through using logistic regression and support vector machine analyses. RESULTS Illness-related effects manifested as reduced grey matter volumes of bilateral temporal limbic-striatal and cerebellar regions, which significantly differentiated bipolar patients from healthy controls and improved clinical classification specificity by 20%. Reduced bilateral cerebellar grey matter volume emerged as a potential endophenotype and (along with parieto-occipital grey matter changes) separated combined-high-risk individuals from healthy and high-risk individuals, and increased clinical classification specificity by approximately 10% and 27%, respectively, while the relatively normalized cerebellar grey matter volumes in the high-risk sample may confer resilience. LIMITATIONS The cross-validation procedure was not performed on an independent sample using independently-derived features. The BD group had different age and sex distributions than some other groups which may not be fully addressable statistically. CONCLUSIONS Our framework can be applied in other measurement domains to derive complete profiles for bipolar patients and at-risk individuals, towards forming strategies for promoting resilience and preclinical intervention.

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