期刊
NEUROIMAGE
卷 117, 期 -, 页码 439-448出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2015.03.071
关键词
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资金
- 16 NIH Institutes and Centers, NIH Blueprint for Neuroscience Research [1U54MH091657]
- McDonnell Center for Systems Neuroscience at Washington University
- Research Councils UK Digital Economy Programme (Centre for Doctoral Training in Healthcare Innovation) [EP/G036861/1]
- Medical Research Council UK New Investigator Grant [MR/M006301/1]
- Welcome Trust Strategic Award [098369/Z/12/Z]
- NIHR Oxford Biomedical Research Centre
- Welcome Trust [092753]
- MRC UK MEG Partnership Grant [MR/K005464/1]
- Engineering and Physical Sciences Research Council [1243649] Funding Source: researchfish
- Medical Research Council [MR/K005464/1, MR/M006301/1] Funding Source: researchfish
- MRC [MR/M006301/1, MR/K005464/1] Funding Source: UKRI
- Wellcome Trust [098369/Z/12/Z] Funding Source: Wellcome Trust
Ambiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections. (C) 2015 The Authors. Published by Elsevier Inc.
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