Conference Paper

A Maximal-Correlation Approach Using Ica for Testing Functional Network Connectivity Applied to Schizophrenia.

DOI: 10.1109/ISBI.2007.356890 Conference: Proceedings of the 2007 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Washington, DC, USA, April 12-16, 2007
Source: DBLP


There has been a growing interest in analyzing brain activation differences between patients and controls by studying resting-state fMRI brain networks. Functional connectivity of the resting brain has been studied by analyzing correlation differences in time courses among seed voxels, regions, or volume of interest with other voxels of the brain in patients versus controls. Spatial differences have also been analyzed among component maps derived from independent component analysis (ICA) in patients with schizophrenia and in healthy controls. However, the relationship among ICA component time courses, (which we define as functional network connectivity), has not been studied. We propose a novel technique to determine FNC applied to schizophrenia which does not rely on the time series of specific brain voxels or regions of interest and instead focuses upon the connectivity between functional networks (components) estimated from ICA using maximal correlation between component time series.

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    ABSTRACT: Recently, a functional disconnectivity hypothesis of schizophrenia has been proposed for the physiological explanation of behavioral syndromes of this complex mental disorder. In this paper, we aim at further examining whether syndromes of schizophrenia could be decoded by some special spatiotemporal patterns of resting-state functional connectivity. We designed a data-driven classifier based on machine learning to extract highly discriminative functional connectivity features and to discriminate schizophrenic patients from healthy controls. The proposed classifier consisted of two separate steps. First, we used feature selection based on a correlation coefficient method to extract highly discriminative regions and construct the optimal feature set for classification. Then, an unsupervised-learning classifier combining low-dimensional embedding and self-organized clustering of fMRI was trained to discriminate schizophrenic patients from healthy controls. The performance of the classifier was tested using a leave-one-out cross-validation strategy. The experimental results demonstrated not only high classification accuracy (93.75% for schizophrenic patients, 75.0% for healthy controls), but also good generalization and stability with respect to the number of extracted features. In addition, some functional connectivities between certain brain regions of the cerebellum and frontal cortex were found to exhibit the highest discriminative power, which might provide further evidence for the cognitive dysmetria hypothesis of schizophrenia. This primary study demonstrated that machine learning could extract exciting new information from the resting-state activity of a brain with schizophrenia, which might have potential ability to improve current diagnosis and treatment evaluation of schizophrenia.
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