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Neuroimage. 2017 Jan 15;145(Pt A):96-106. doi: 10.1016/j.neuroimage.2016.10.011. Epub 2016 Oct 8.

Magnetoencephalographic and functional MRI connectomics in schizophrenia via intra- and inter-network connectivity.

Author information

1
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico, United States; Mind Research Network, Albuquerque, New Mexico, United States. Electronic address: jhouck@unm.edu.
2
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico, United States; Mind Research Network, Albuquerque, New Mexico, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States.
3
Mind Research Network, Albuquerque, New Mexico, United States.
4
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico, United States; Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States.
5
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico, United States; Mind Research Network, Albuquerque, New Mexico, United States; Department of Radiology, University of New Mexico, Albuquerque, New Mexico, United States.
6
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico, United States; Department of Neurosciences, University of New Mexico, Albuquerque, New Mexico, United States.
7
Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States; University of Nottingham, United Kingdom.
8
Mind Research Network, Albuquerque, New Mexico, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States.

Abstract

Examination of intrinsic functional connectivity using functional MRI (fMRI) has provided important findings regarding dysconnectivity in schizophrenia. Extending these results using a complementary neuroimaging modality, magnetoencephalography (MEG), we present the first direct comparison of functional connectivity between schizophrenia patients and controls, using these two modalities combined. We developed a novel MEG approach for estimation of networks using MEG that incorporates spatial independent component analysis (ICA) and pairwise correlations between independent component timecourses, to estimate intra- and intern-network connectivity. This analysis enables group-level inference and testing of between-group differences. Resting state MEG and fMRI data were acquired from a large sample of healthy controls (n=45) and schizophrenia patients (n=46). Group spatial ICA was performed on fMRI and MEG data to extract intrinsic fMRI and MEG networks and to compensate for signal leakage in MEG. Similar, but not identical spatial independent components were detected for MEG and fMRI. Analysis of functional network connectivity (FNC; i.e., pairwise correlations in network (ICA component) timecourses) revealed a differential between-modalities pattern, with greater connectivity among occipital networks in fMRI and among frontal networks in MEG. Most importantly, significant differences between controls and patients were observed in both modalities. MEG FNC results in particular indicated dysfunctional hyperconnectivity within frontal and temporal networks in patients, while in fMRI FNC was always greater for controls than for patients. This is the first study to apply group spatial ICA as an approach to leakage correction, and as such our results may be biased by spatial leakage effects. Results suggest that combining these two neuroimaging modalities reveals additional disease-relevant patterns of connectivity that were not detectable with fMRI or MEG alone.

KEYWORDS:

Functional connectivity; Imaging; MEG; Resting state connectivity; Schizophrenia

PMID:
27725313
PMCID:
PMC5179295
DOI:
10.1016/j.neuroimage.2016.10.011
[Indexed for MEDLINE]
Free PMC Article

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