Identification of Imaging Biomarkers in Schizophre

Authors: Dae Il Kim Jing Sui Srinivas Rachakonda Tonya White Dara S Manoach V P Clark BengChoon Ho S Charles Schulz Vince D Calhoun

Publish Date: 2010/07/07

Volume: 8, Issue: 4, Pages: 213-229

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Abstract

A number of recent studies have combined multiple experimental paradigms and modalities to find relevant biological markers for schizophrenia In this study we extracted fMRI features maps from the analysis of three experimental paradigms auditory oddball Sternberg item recognition sensorimotor for a large number n = 154 of patients with schizophrenia and matched healthy controls We used the general linear model GLM and independent component analysis ICA to extract feature maps ie ICA component maps and GLM contrast maps which were then subjected to a coefficientconstrained independent component analysis CCICA to identify potential neurobiological markers A total of 29 different feature maps were extracted for each subject Our results show a number of optimal feature combinations that reflect a set of brain regions that significantly discriminate between patients and controls in the spatial heterogeneity and amplitude of their feature signals Spatial heterogeneity was seen in regions such as the superior/middle temporal and frontal gyri bilateral parietal lobules and regions of the thalamus Most strikingly an ICA feature representing a bilateral frontal pole network was consistently seen in the ten highest feature results when ranked on differences found in the amplitude of their feature signals The implication of this frontal pole network and the spatial variability which spans regions comprising of bilateral frontal/temporal lobes and parietal lobules suggests that they might play a significant role in the pathophysiology of schizophrenia

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