Constructing brain connectivity group graphs from EEG time series

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Title: Constructing brain connectivity group graphs from EEG time series
Authors: Walden, A
Zhuang, L
Item Type: Journal Article
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.
Issue Date: 26-Apr-2019
Date of Acceptance: 9-Oct-2018
ISSN: 0266-4763
Publisher: Taylor & Francis (Routledge)
Start Page: 1107
End Page: 1128
Journal / Book Title: Journal of Applied Statistics
Volume: 46
Issue: 6
Copyright Statement: © 2018 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript of an article published by Taylor & Francis inJournal of Applied Statistics on 19th October 2018, available online:
Keywords: Science & Technology
Physical Sciences
Statistics & Probability
Brain functional connectivity
EEG time series
graphical model
multivariable power spectra
spectral matrix clustering
Statistics & Probability
0104 Statistics
Publication Status: Published
Online Publication Date: 2018-10-19
Appears in Collections:Mathematics
Faculty of Natural Sciences

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