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Published Online: 5 April 2019

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

Publication: Brain Connectivity
Volume 9, Issue Number 3


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.

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Abrol A, Chaze C, Damaraju E, Calhoun VD. 2016. The chronnectome: evaluating replicability of dynamic connectivity patterns in 7500 resting fMRI datasets. Conf Proc IEEE Eng Med Biol Soc 2016:5571–5574.
Ahonen AI, Hamalainen MS, Ilmoniemi RJ, Kajola MJ, Knuutila JE, Simola JT, Vilkman VA. 1993. Sampling theory for neuromagnetic detector arrays. IEEE Trans Biomed Eng 40:859–869.
Aine CJ, Bockholt HJ, Bustillo JR, Canive JM, Caprihan A, Gasparovic C, et al. 2017. Multimodal neuroimaging in schizophrenia: description and dissemination. Neuroinformatics 15:343–364.
Alamian G, Hincapie AS, Combrisson E, Thiery T, Martel V, Althukov D, Jerbi K. 2017. Alterations of intrinsic brain connectivity patterns in depression and bipolar disorders: a critical assessment of magnetoencephalography-based evidence. Front Psychiatry 8:41.
Allen EA, Damaraju E, Eichele T, Wu L, Calhoun VD. 2018. EEG signatures of dynamic functional network connectivity states. Brain Topogr 31:101–116.
Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. 2014. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24:663–676.
Allen EA, Erhardt EB, Damaraju E, Gruner W, Segall JM, Silva RF, et al. 2011. A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci 5:2.
Baker AP, Brookes MJ, Rezek IA, Smith SM, Behrens T, Probert Smith PJ, Woolrich M. 2014. Fast transient networks in spontaneous human brain activity, eLife 3:e01867.
Besl PJ, McKay ND. 1992. A method for registration of 3-D shapes. In IEEE Transactions on Pattern Analysis and Machine Intelligence—Special Issue on Interpretation of 3-D Scenes—Part II Archive, vol. 14, IEEE Computer Soceity, pp. 239–256.
Birn RM, Molloy EK, Patriat R, Parker T, Meier TB, Kirk GR, et al. 2013. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage 83:550–558.
Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541.
Biswal BB. 2012. Resting state fMRI: a personal history. Neuroimage 62:938–944.
Bridwell DA, Wu L, Eichele T, Calhoun VD. 2013. The spatiospectral characterization of brain networks: fusing concurrent EEG spectra and fMRI maps. Neuroimage 69:101–111.
Brookes MJ, Hale JR, Zumer JM, Stevenson CM, Francis ST, Barnes GR, et al. 2011a. Measuring functional connectivity using MEG: methodology and comparison with fcMRI. Neuroimage 56:1082–1104.
Brookes MJ, Woolrich M, Luckhoo H, Price D, Hale JR, Stephenson MC, et al. 2011b. Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc Natl Acad Sci U S A 108:16783–16788.
Bullmore ET, Frangou S, Murray RM, 1997. The dysplastic net hypothesis: an integration of developmental and dysconnectivity theories of schizophrenia. Schizophr Res 28:143–156.
Buzsáki G, Draguhn A. 2004. Neuronal oscillations in cortical networks. Science 304:1926–1929.
Cabral C, Kambeitz-Ilankovic L, Kambeitz J, Calhoun VD, Dwyer DB, von Saldern S, et al. 2016. Classifying schizophrenia using multimodal multivariate pattern recognition analysis: evaluating the impact of individual clinical profiles on the neurodiagnostic performance. Schizophr Bull 42(Suppl 1):S110–S117.
Calhoun VD, Adali T, Pearlson GD, Pekar JJ. 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14:140–151.
Calhoun VD, Miller R, Pearlson G, Adali T. 2014. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84:262–274.
Calhoun VD, Sui J. 2016. Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol Psychiatry Cogn Neurosci Neuroimaging 1:230–244.
Cetin MS, Houck JM, Rashid B, Agacoglu O, Stephen JM, Sui J, et al. 2016. Multimodal classification of schizophrenia patients with MEG and fMRI data using static and dynamic connectivity measures. Front Neurosci 10:466.
Conner CR, Ellmore TM, Pieters TA, DiSano MA, Tandon N. 2011. Variability of the relationship between electrophysiology and BOLD-fMRI across cortical regions in humans. J Neurosci 31:12855–12865.
Dale AM, Fischl B, Sereno MI. 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179–194.
Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW, Lewine JD, Halgren E. 2000. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26:55–67.
Damaraju E, Allen EA, Belger A, Ford JM, McEwen S, Mathalon DH, et al. 2014. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin 5:298–308.
Dong D, Wang Y, Chang X, Luo C, Yao D. 2018. Dysfunction of large-scale brain networks in schizophrenia: a meta-analysis of resting-state functional connectivity. Schizophr Bull 44:168–181.
Du Y, Pearlson GD, Yu Q, He H, Lin D, Sui J, et al. 2016. Interaction among subsystems within default mode network diminished in schizophrenia patients: a dynamic connectivity approach. Schizophr Res 170:55–65.
Eichenbaum H, Yonelinas AP, Ranganath C. 2007. The medial temporal lobe and recognition memory. Annu Rev Neurosci 30:123–152.
Engel AK, Singer W. 2001. Temporal binding and the neural correlates of sensory awareness. Trends Cogn Sci 5:16–25.
Erhardt EB, Allen EA, Damaraju E, Calhoun VD. 2011a. On network derivation, classification, and visualization: a response to Habeck and Moeller. Brain Connect 1:105–110.
Erhardt EB, Rachakonda S, Bedrick EJ, Allen EA, Adali T, Calhoun VD. 2011b. Comparison of multi-subject ICA methods for analysis of fMRI data. Hum Brain Mapp 32:2075–2095.
First M, Gibbon M, Spitzer RL, Williams JBW, Benjamin LS. 1997. Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II). Washington, DC: American Psychiatric Press, Inc.
Fischl B, Sereno MI, Dale AM. 1999. Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9:195–207.
Fox JM, Abram SV, Reilly JL, Eack S, Goldman MB, Csernansky JG, et al. 2017. Default mode functional connectivity is associated with social functioning in schizophrenia. J Abnorm Psychol 126:392–405.
Friston KJ, Frith CD, 1995. Schizophrenia: a disconnection syndrome? Clin Neurosci 3:89–97.
Gardner DM, Murphy AL, O'Donnell H, Centorrino F, Baldessarini RJ. 2010. International consensus study of antipsychotic dosing. Am J Psychiatry 167:686–693.
Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, et al. 2014. MNE software for processing MEG and EEG data. Neuroimage 86:446–460.
Hall EL, Robson SE, Morris PG, Brookes MJ. 2014. The relationship between MEG and fMRI. Neuroimage 102(Pt 1):80–91.
Hamalainen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV. 1993. Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human rain. Rev Mod Phys 65:413–497.
Hamalainen MS, Sarvas J. 1989. Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Trans Biomed Eng 36:165–171.
Hanlon FM, Houck JM, Klimaj SD, Caprihan A, Mayer AR, Weisend MP, et al. 2012. Frontotemporal anatomical connectivity and working-relational memory performance predict everyday functioning in schizophrenia. Psychophysiology 49:1340–1352.
Hansen EC, Battaglia D, Spiegler A, Deco G, Jirsa VK. 2015. Functional connectivity dynamics: modeling the switching behavior of the resting state. Neuroimage 105:525–535.
Harvey BM, Vansteensel MJ, Ferrier CH, Petridou N, Zuiderbaan W, Aarnoutse EJ, et al. 2013. Frequency specific spatial interactions in human electrocorticography: V1 alpha oscillations reflect surround suppression. Neuroimage 65:424–432.
Himberg J, Hyvarinen A, Esposito F. 2004. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage 22:1214–1222.
Hincapié A-S, Kujala J, Mattout J, Pascarella A, Daligault S, Delpuech C, et al. 2017. The impact of MEG source reconstruction method on source-space connectivity estimation: a comparison between minimum-norm solution and beamforming. Neuroimage 156:29–42.
Houck JM, Cetin MS, Mayer AR, Bustillo JR, Stephen J, Aine C, et al. 2017. Magnetoencephalographic and functional MRI connectomics in schizophrenia via intra- and inter-network connectivity. Neuroimage 145(Pt A):96–106.
Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, et al. 2013. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80:360–378.
Jafri MJ, Pearlson GD, Stevens M, Calhoun VD. 2008. A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage 39:1666–1681.
Khanna A, Pascual-Leone A, Michel CM, Farzan F. 2015. Microstates in resting-state EEG: current status and future directions. Neurosci Biobehav Rev 49:105–113.
Kunii N, Kamada K, Ota T, Kawai K, Saito N. 2013. Characteristic profiles of high gamma activity and blood oxygenation level-dependent responses in various language areas. Neuroimage 65:242–249.
Lachaux JP, Fonlupt P, Kahane P, Minotti L, Hoffmann D, Bertrand O, Baciu M. 2007. Relationship between task-related gamma oscillations and BOLD signal: new insights from combined fMRI and intracranial EEG. Hum Brain Mapp 28:1368–1375.
Leonardi N, Van De Ville D. 2015. On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage 104:430–436.
Liao X, Cao M, Xia M, He Y. 2017. Individual differences and time-varying features of modular brain architecture. Neuroimage 152:94–107.
Liuzzi L, Gascoyne LE, Tewarie PK, Barratt EL, Boto E, Brookes MJ. 2017. Optimising experimental design for MEG resting state functional connectivity measurement. Neuroimage 155:565–576.
Lloyd SP. 1982. Least squares quantization in PCM. IEEE Trans Inform Theory 28:129–137.
Madden DJ, Parks EL, Tallman CW, Boylan MA, Hoagey DA, Cocjin SB, et al. 2017. Sources of disconnection in neurocognitive aging: cerebral white-matter integrity, resting-state functional connectivity, and white-matter hyperintensity volume. Neurobiol Aging 54:199–213.
McAvoy M, Larson-Prior L, Ludwikow M, Zhang D, Snyder AZ., Gusnard DL. 2012. Dissociated mean and functional connectivity BOLD signals in visual cortex during eyes closed and fixation. J Neurophysiol 108:2363–2372.
Meier J, Tewarie P, Hillebrand A, Douw L, van Dijk BW, Stufflebeam SM, Van Mieghem P. 2016. A mapping between structural and functional brain networks. Brain Connect 6:298–311.
Miller RL, Yaesoubi M, Calhoun VD. 2014. Higher dimensional analysis shows reduced dynamism of time-varying network connectivity in schizophrenia patients. Conf Proc IEEE Eng Med Biol Soc 2014:3837–3840.
Miller RL, Yaesoubi M, Turner JA, Mathalon D, Preda A, Pearlson G, et al. 2016. Higher dimensional meta-state analysis reveals reduced resting fMRI connectivity dynamism in schizophrenia patients. PLoS One 2016;11:e0149849.
Mosher JC, Leahy RM, Lewis PS. 1999. EEG and MEG: forward solutions for inverse methods. IEEE Trans Biomed Eng 46:245–259.
Muthukumaraswamy SD, Singh KD. 2008. Spatiotemporal frequency tuning of BOLD and gamma band MEG responses compared in primary visual cortex. Neuroimage 40:1552–1560.
Nashiro K, Sakaki M, Braskie MN, Mather M. 2017. Resting-state networks associated with cognitive processing show more age-related decline than those associated with emotional processing. Neurobiol Aging 54:152–162.
Niessing J, Ebisch B, Schmidt KE, Niessing M, Singer W, Galuske RA. 2005. Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science 309:948–951.
Nishida K, Morishima Y, Yoshimura M, Isotani T, Irisawa S, Jann K, et al. 2013. EEG microstates associated with salience and frontoparietal networks in frontotemporal dementia, schizophrenia and Alzheimer's disease. Clin Neurophysiol 124:1106–1114.
Nugent AC, Luber B, Carver FW, Robinson SE, Coppola R, Zarate CA Jr. 2017. Deriving frequency-dependent spatial patterns in MEG-derived resting state sensorimotor network: a novel multiband ICA technique. Hum Brain Mapp 38:779–791.
Palaniyappan L, Simmonite M, White TP, Liddle EB, Liddle PF. 2013. Neural primacy of the salience processing system in schizophrenia. Neuron 79:814–828.
Preti MG, Bolton TA, Van De Ville D. 2017. The dynamic functional connectome: state-of-the-art and perspectives. Neuroimage 160:41–54.
Rieger K, Diaz Hernandez L, Baenninger A, Koenig T. 2016. 15 Years of microstate research in schizophrenia—where are we? A meta-analysis. Front Psychiatry 7:22.
Roiser JP, Wigton R, Kilner JM, Mendez MA, Hon N, Friston KJ, Joyce EM. 2013. Dysconnectivity in the frontoparietal attention network in schizophrenia. Front Psychiatry 4:176.
Roopun AK, Cunningham MO, Racca C, Alter K, Traub RD, Whittington MA. 2008. Region-specific changes in gamma and beta2 rhythms in NMDA receptor dysfunction models of schizophrenia. Schizophr Bull 34:962–973.
Sakoğlu U, Pearlson GD, Kiehl KA, Wang YM, Michael AM, Calhoun VD. 2010. A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. MAGMA 23:351–366.
Scheeringa R, Fries P, Petersson KM, Oostenveld R, Grothe I, Norris DG, et al. 2011. Neuronal dynamics underlying high- and low-frequency EEG oscillations contribute independently to the human BOLD signal. Neuron 69:572–583.
Scott A, Courtney W, Wood D, de la Garza R, Lane S, King M, et al. 2011. COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Front Neuroinform 5:33.
Taulu S, Kajola M, Simola J. 2004. Suppression of interference and artifacts by the Signal Space Separation Method. Brain Topogr 16:269–275.
Taulu S, Simola J. 2006. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys Med Biol 51:1759–1768.
Uusitalo MA, Ilmoniemi RJ. 1997. Signal-space projection method for separating MEG or EEG into components. Med Biol Eng Comput 35:135–140.
Van de Ville D, Britz J, Michel CM. 2010. EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proc Natl Acad Sci U S A 107:18179–18184.
Vergara VM, Miller R, Calhoun V. 2017. An information theory framework for dynamic functional domain connectivity. J Neurosci Methods 284:103–111.
Vidaurre D, Quinn AJ, Baker AP, Dupret D, Tejero-Cantero A, Woolrich MW. 2016. Spectrally resolved fast transient brain states in electrophysiological data. Neuroimage 126:81–95.
Wang L, Saalmann YB, Pinsk MA, Arcaro MJ, Kastner S. 2012. Electrophysiological low-frequency coherence and cross-frequency coupling contribute to BOLD connectivity. Neuron 76:1010–1020.
Werner S, Malaspina D, Rabinowitz J. 2007. Socioeconomic status at birth is associated with risk of schizophrenia: population-based multilevel study. Schizophr Bull 33:1373–1378.
Wu XJ, Zeng LL, Shen H, Yuan L, Qin J, Zhang P, Hu D. 2017. Functional network connectivity alterations in schizophrenia and depression. Psychiatry Res 263:113–120.
Zaehle T, Frund I, Schadow J, Tharig S, Schoenfeld MA, Herrmann CS. 2009. Inter- and intra-individual covariations of hemodynamic and oscillatory gamma responses in the human cortex. Front Hum Neurosci 3:8.
Zhu Z, Zumer JM, Lowenthal ME, Padberg J, Recanzone GH, Krubitzer LA, et al. 2009. The relationship between magnetic and electrophysiological responses to complex tactile stimuli. BMC Neurosci 10:4.

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Published In

cover image Brain Connectivity
Brain Connectivity
Volume 9Issue Number 3April 2019
Pages: 251 - 262
PubMed: 30632385


Published online: 5 April 2019
Published in print: April 2019
Published ahead of production: 11 January 2019


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Lori Sanfratello [email protected]
The Mind Research Network, Albuquerque, New Mexico.
Jon M. Houck
The Mind Research Network, Albuquerque, New Mexico.
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico.
Vince D. Calhoun
The Mind Research Network, Albuquerque, New Mexico.
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico.
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico.


Address correspondence to: Lori Sanfratello, The Mind Research Network, 1101 Yale Boulevard Northeast, Albuquerque, NM 87016 [email protected]

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