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• W2896508746 abstract "Graphical analysis of complex brain networks is a fundamental area of modern neuroscience. Functional connectivity is important since many neurological and psychiatric disorders, including schizophrenia, are described as ‘dys-connectivity’ syndromes. Using electroencephalogram time series collected on each of a group of 15 individuals with a common medical diagnosis of positive syndrome schizophrenia we seek to build a single, representative, brain functional connectivity group graph. Disparity/distance measures between spectral matrices are identified and used to define the normalized graph Laplacian enabling clustering of the spectral matrices for detecting ‘outlying’ individuals. Two such individuals are identified. For each remaining individual, we derive a test for each edge in the connectivity graph based on average estimated partial coherence over frequencies, and associated p-values are found. For each edge these are used in a multiple hypothesis test across individuals and the proportion rejecting the hypothesis of no edge is used to construct a connectivity group graph. This study provides a framework for integrating results on multiple individuals into a single overall connectivity structure." @default.
• W2896508746 created "2018-10-26" @default.
• W2896508746 creator A5038584158 @default.
• W2896508746 creator A5044163181 @default.
• W2896508746 date "2018-10-19" @default.
• W2896508746 modified "2023-09-24" @default.
• W2896508746 title "Constructing brain connectivity group graphs from EEG time series" @default.
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• W2896508746 doi "https://doi.org/10.1080/02664763.2018.1536198" @default.
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