Dynamic Functional Network Connectivity in Schizophrenia with Magnetoencephalography and Functional Magnetic Resonance Imaging: Do Different Timescales Tell a Different Story?
Year: 2019
Type: article
Study design: Observational Study
Abstract: The importance of how brain networks function together to create brain states has become increasingly recognized. Therefore, an investigation of eyes-open resting-state dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) via both functional magnetic resonance imaging (fMRI) and a novel magnetoencephalography (MEG) pipeline was completed. The fMRI analysis used a spatial independent component analysis (ICA) to determine the networks on which the dFNC was based. The MEG analysis utilized a source space activity estimate (minimum norm estimate [MNE]/dynamic statistical parametric mapping [dSPM]) whose result was the input to a spatial ICA, on which the networks of the MEG dFNC were based. We found that dFNC measures reveal significant differences between HC and SP, which depended on the imaging modality. Consistent with previous findings, a dFNC analysis predicated on fMRI data revealed HC and SP remain in different overall brain states (defined by a k-means clustering of network correlations) for significantly different periods of time, with SP spending less time in a highly connected state. The MEG dFNC, in contrast, revealed group differences in more global statistics: SP changed between meta-states (k-means cluster states that are allowed to overlap in time) significantly more often and to states that were more different, relative to HC. MEG dFNC also revealed a highly connected state where a significant difference was observed in interindividual variability, with greater variability among SP. Overall, our results show that fMRI and MEG reveal between-group functional connectivity differences in distinct ways, highlighting the utility of using each of the modalities individually, or potentially a combination of modalities, to better inform our understanding of disorders such as schizophrenia.
Source: Brain Connectivity
Institutions: Mind Research Network, University of New Mexico
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funders: National Science Foundation, National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism, National Institute of Mental Health, National Institute of General Medical Sciences, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources
Awards: G1533216475, Unified multivariate data-driven solutions for static and dynamic brain connectivity, Neural Mechanism of Schizophrenia: Use of Multiple Neuroimaging Tools to Examine, Imaging Brain Activity in Substance Use Treatment, RII Track-2 FEC: Developmental Chronnecto-Genomics (Dev-CoG): A Next Generation Framework for Quantifying Brain Dynamics and Related Genetic Factors in Childhood, Multimodal Imaging of Neuropsychiatric Disorders (MIND): Mechanisms &, G527314314, Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders, G772009288, G8420544453, Multivariate methods for identifying multitask/multimodal brain imaging biomarkers
OpenAlex ID: W2943163728