623. Classification of First-Episode Schizophrenia Spectrum Disorders and Controls from Whole Brain White Matter Fractional Anisotropy Using Machine Learning

Biological Psychiatry

Friday Abstracts

Keywords: Polygenic Risk, Schizophrenia, BOLD fMRI, GWAS, Dorsolateral Prefrontal Cortex

622. Neural Correlates of Self-Reflection in Schizophrenia: A Functional Magnetic Resonance Imaging Study Chaitra Hiremath, Naren Rao, Arpitha Jacob, Avyarthana Dey, Shivarama Varambally, Ganesan Venkatasubramanian, Rose Bharath, and Gangadhar Bangalore National Institute of Mental Health and Neurosciences Background: Self-reflection is the process of conscious evaluation of one’s traits, abilities and attitudes.Deficient selfreflective processes might underlie lack of insight in schizophrenia.The limited literature on neural correlates of self-reflection in schizophrenia is inconclusive.In this study, we investigated the neural correlates of self-reflection in schizophrenia patients attending a tertiary care hospital in India. Methods: 19 male schizophrenia patients and 19 healthy controls participated in the study.Participants performed selfreflection task while undergoing fMRI.The task comprised of 4 domains;Self-reflection,Other-reflection,Affect labelling and Perceptual.Contrasts comparing Self-reflection with other domains were modelled at the individual subject level. In second level, independent t test was used to compare 2 groups.The results were thresholded at p,0.001 (uncorrected, cluster size 6 voxels). Results: For Self-reflection.Other-reflection contrast, patients demonstrated greater activation of both superior parietal lobules (BA 5,7),right inferior parietal lobule (BA 39),left parahippocampal gyrus (BA 36) and left premotor cortex (BA 6).For Selfreflection.Affect labelling contrast,patients showed greater activation of precuneus (BA 7) and right inferior occipital gyrus (BA 19),lesser activation of left inferior frontal gyrus (BA 45,47). For Self-reflection.Perceptual contrast,patients showed greater activation of left middle frontal gyrus (BA 10),left posterior cingulate gyrus (BA 31),right superior parietal lobule (BA 7),both inferior parietal lobules (BA 39,40) and left premotor cortex (BA6). Conclusions: The results indicate that patients have aberrant activity in brain regions subserving self-reflection.The greater activation of posterior brain areas might suggest that schizophrenia is associated with an anterior-to-posterior shift in introspection-related activation,as seen from earlier studies. Further studies with a larger sample are needed to examine neural processes underlying self-reflection abnormalities in schizophrenia. Supported By: Department of Science and Technology, Govt. of India - INSPIRE Faculty Award (IFA12-LSBM) Keywords: Self-reflection, Schizophrenia, fMRI, insight

623. Classification of First-Episode Schizophrenia Spectrum Disorders and Controls from Whole Brain White Matter Fractional Anisotropy Using Machine Learning Pavol Mikolas1, Jaroslav Hlinka2, Zbynek Pitra3, Antonin Skoch4, Thomas Frodl5, Filip Spaniel6, and Tomas Hajek7

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National Institute of Mental Health, 3rd Faculty of Medicine, Charles University, Prague, Czech Republic, 2Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic, 3Faculty of Nuclear Sciences and Physical Engineering Czech Technical University in Prague, Prague, Czech Republic, 4MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic, 5 Department of Psychiatry and Psychotherapy, Otto von Guericke University, Magdeburg, Germany, 6National Institute of Mental Health, Klecany, Czech Republic, 7Dalhousie University Background: Early diagnosis of schizophrenia could improve the prognosis of the illness. Applying machine learning (ML) to MRI data allows for identification of subtle disease patterns on a single subject level, which could help realize the diagnostic potential of MRI in psychiatry. Machine learning has mostly been tested for diagnostic purposes in combination with graymatter structural or functional MRI. Here, we used ML to differentiate participants with a first episode of schizophreniaspectrum disorder (FES) from healthy controls based on diffusion tensor imaging data. Methods: We attempted a diagnostic classification of 77 FES patients and 77 age and sex matched healthy controls, by applying linear support-vector machine (SVM) to fractional anisotropy data. We further analyzed the effect of medication and symptoms on the classification performance using standard statistical measures (t-test, linear regression) and machine learning (kernel-ridge regression). Results: The SVM distinguished between patients and controls with significant accuracy of 62.34 % (p50.005). There was no association between the classification performance and medication nor symptoms. Conclusions: This is a proof of concept that SVM in combination with brain white-matter fractional anisotropy might help differentiate FES from HC, even early in the course of schizophrenia. The classification was probably based on trait rather than state markers, as symptoms or medications were not significantly associated with classification accuracy. Supported By: Nr. LO1611 with a financial support from the MEYS under the NPU I program. Keywords: first episode schizophrenia, Machine learning, Fractional Anisotropy, Diffusion Tensor Imaging (DTI), Support Vector Machines

624. Accelerated Brain Ageing in First Episode Psychosis. Association with Metabolic Parameters Marian Kolenic1, Katja Franke2, Martin Matějka3, Jana Čapková3, Filip Špeniel3, Jaroslav Hlinka4, Petra Fürstová3, Martin Alda5, and Tomas Hajek5 1

National Institute of Mental Health, Prague, Czech Republic, 2Structural Brain Mapping Group, Department of Neurology, University Hospital Jena, Jena, Germany, 3National Institute of Mental Health, Klecany, Czech Republic, 4 Department of Nonlinear Dynamics and Complex Systems,

Biological Psychiatry May 15, 2017; 81:S140–S276 www.sobp.org/journal