“School of Cognitive Sciences”
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| Paper IPM / Cognitive Sciences / 14236 |
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College of Engineering, University of Tehran, Tehran, Iran. The brain network consists of spatially distinct but functionally connected regions that continuously communicate with each other through cognitive and behavioral processes. Various imaging modalities have been extensively used to assess the existence of functional interactions between neural regions. Resting-state functional magnetic resonance imaging (RS-fMRI), with a high temporal-spatial resolution, is one of the most promising neuroimaging techniques to evaluate complex networks in the brain functional connectivity studies. To discover RS networks, RS-fMRI focuses on the assessment of low-frequency (<0.1 Hz) oscillations of the blood-oxygen-level-dependent (BOLD) signals in the absence of a task or stimulus. Various techniques exist for performing RS-fMRI analysis, including seed-based approaches, independent component analysis (ICA), graph theoretical approaches, clustering algorithms, and multivariate pattern analysis (MVPA), each with its own inherent advantages and disadvantages. Despite its early stage of development, a broad range of RS-fMRI clinical applications is reported, from pre-surgical planning for patients with brain tumors and epilepsy to providing diagnostic and prognostic data for a wide spectrum of neurological and psychiatric disorders. The first part of this talk will provide a brief overview of some of the statistical and mathematical approaches applied to the RS-fMRI data. The second part will summarize the studies that have illustrated potential clinical applications of RS-fMRI.
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