Constructing brain connectivity group graphs from EEG time series
File(s)
Accepted version
Author(s)
Walden, Andrew
Zhuang, Linjie
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.
Date Issued
2019-04-26
Date Acceptance
2018-10-09
Citation
Journal of Applied Statistics, 2019, 46 (6), pp.1107-1128
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: https://www.tandfonline.com/doi/full/10.1080/02664763.2018.1536198
Subjects
Science & Technology
Physical Sciences
Statistics & Probability
Mathematics
Brain functional connectivity
EEG time series
graphical model
multivariable power spectra
schizophrenia
spectral matrix clustering
FUNCTIONAL CONNECTIVITY
SCHIZOPHRENIA
NETWORKS
COHERENCE
Statistics & Probability
0104 Statistics
Publication Status
Published
Date Publish Online
2018-10-19