Article ID Journal Published Year Pages File Type
6030383 NeuroImage 2012 11 Pages PDF
Abstract

Connectivity analysis using functional magnetic resonance imaging (fMRI) data is an important area, useful for the identification of biomarkers for various mental disorders, including schizophrenia. Most studies to date have focused on resting data, while the study of functional connectivity during task and the differences between task and rest are of great interest as well. In this work, we examine the graph-theoretical properties of the connectivity maps constructed using spatial components derived from independent component analysis (ICA) for healthy controls and patients with schizophrenia during an auditory oddball task (AOD) and at extended rest. We estimate functional connectivity using the higher-order statistical dependence, i.e., mutual information among the ICA spatial components, instead of the typically used temporal correlation. We also define three novel topological metrics based on the modules of brain networks obtained using a clustering approach. Our experimental results show that although the schizophrenia patients preserve the small-world property, they present a significantly lower small-worldness during both AOD task and rest when compared to the healthy controls, indicating a consistent tendency towards a more random organization of brain networks. In addition, the task-induced modulations to topological measures of several components involving motor, cerebellum and parietal regions are altered in patients relative to controls, providing further evidence for the aberrant connectivity in schizophrenia.

► Functional connectivity is quantified using higher-order spatial information. ► Connectivity during task and rest is studied using graph-theoretical analysis. ► Novel metrics are proposed based on the modules obtained by a clustering approach. ► Meaningful task-induced modulations to connectivity are found in the healthy group. ► Altered topological properties of connectivity are observed in schizophrenia group.

Related Topics
Life Sciences Neuroscience Cognitive Neuroscience
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