Literature DB >> 30632385

Dynamic Functional Network Connectivity in Schizophrenia with Magnetoencephalography and Functional Magnetic Resonance Imaging: Do Different Timescales Tell a Different Story?

Lori Sanfratello1, Jon M Houck1,2, Vince D Calhoun1,2,3.   

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.

Entities:  

Keywords:  dynamic functional network connectivity (dFNC); functional magnetic resonance imaging (fMRI); magnetoencephalography (MEG); schizophrenia

Mesh:

Year:  2019        PMID: 30632385      PMCID: PMC6479258          DOI: 10.1089/brain.2018.0608

Source DB:  PubMed          Journal:  Brain Connect        ISSN: 2158-0014


  82 in total

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5.  Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity.

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9.  Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements.

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  11 in total

1.  Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia.

Authors:  L Sanfratello; J M Houck; V D Calhoun
Journal:  Schizophr Res       Date:  2019-05-24       Impact factor: 4.939

2.  Identification of minimal hepatic encephalopathy based on dynamic functional connectivity.

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Authors:  Felicha T Candelaria-Cook; Megan E Schendel; Cesar J Ojeda; Juan R Bustillo; Julia M Stephen
Journal:  Schizophr Res       Date:  2019-11-06       Impact factor: 4.939

4.  Decreased Cross-Domain Mutual Information in Schizophrenia From Dynamic Connectivity States.

Authors:  Mustafa S Salman; Victor M Vergara; Eswar Damaraju; Vince D Calhoun
Journal:  Front Neurosci       Date:  2019-08-22       Impact factor: 4.677

5.  Assessing Brain Networks by Resting-State Dynamic Functional Connectivity: An fNIRS-EEG Study.

Authors:  Yujin Zhang; Chaozhe Zhu
Journal:  Front Neurosci       Date:  2020-01-24       Impact factor: 4.677

6.  Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia.

Authors:  Golnoush Alamian; Annalisa Pascarella; Tarek Lajnef; Laura Knight; James Walters; Krish D Singh; Karim Jerbi
Journal:  Neuroimage Clin       Date:  2020-11-05       Impact factor: 4.881

7.  Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity.

Authors:  Mohammad S E Sendi; Elaheh Zendehrouh; Charles A Ellis; Zhijia Liang; Zening Fu; Daniel H Mathalon; Judith M Ford; Adrian Preda; Theo G M van Erp; Robyn L Miller; Godfrey D Pearlson; Jessica A Turner; Vince D Calhoun
Journal:  Front Neural Circuits       Date:  2021-03-18       Impact factor: 3.492

8.  Combining Dynamic Network Analysis and Cerebral Carryover Effect to Evaluate the Impacts of Reading Social Media Posts and Science Fiction in the Natural State on the Human Brain.

Authors:  Bo Hu; Yu-Ling Cui; Ying Yu; Yu-Ting Li; Lin-Feng Yan; Jing-Ting Sun; Qian Sun; Jing Zhang; Wen Wang; Guang-Bin Cui
Journal:  Front Neurosci       Date:  2022-02-21       Impact factor: 4.677

9.  Increased Excursions to Functional Networks in Schizophrenia in the Absence of Task.

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10.  Dynamic neural circuit disruptions associated with antisocial behaviors.

Authors:  Weixiong Jiang; Han Zhang; Ling-Li Zeng; Hui Shen; Jian Qin; Kim-Han Thung; Pew-Thian Yap; Huasheng Liu; Dewen Hu; Wei Wang; Dinggang Shen
Journal:  Hum Brain Mapp       Date:  2020-10-16       Impact factor: 5.399

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