Functional magnetic resonance imaging (fMRI) is widely used to identify neural correlates of cognitive tasks. However, the analysis of functional connectivity is crucial to understanding neural dynamics. Although many studies of cerebral circuitry have revealed adaptative behavior, which can change during the course of the experiment, most of contemporary connectivity studies are based on corre...

Measures of network structural integrity useful in the analysis and synthesis of power systems are discussed. Signal flow methodology is applied to derive an expression for the paths between sources and sinks in a power network. Connectivity and reach ability properties of the network are obtained using the minors of a modified connectivity matrix. Node-connectivity, branch connectivity and mix...

The application of multivoxel pattern analysis methods has attracted increasing attention, particularly for brain state prediction and real-time functional MRI applications. Support vector classification is the most popular of these techniques, owing to reports that it has better prediction accuracy and is less sensitive to noise. Support vector classification was applied to learn functional co...

Introduction: Tinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation of the brain...

A method is described that combines linear source estimation (beamformers) with non-parametric statistical significance testing to yield vector time series estimates for brain regions of interest. These source time series are a suitable starting point for functional connectivity analysis. Keywords—EMEG beamformers, non-parametric significance testing, functional connectivity analysis

Connectivity networks have been recently used for classification of neurodegenerative diseases, e.g., mild cognitive impairment (MCI). In typical connectivity network-based classification, features are often extracted from (multiple) connectivity networks and concatenated into a long vector for subsequent feature selection and classification. However, some useful network topological information...