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Mapping Brain Networks Using Multimodal Data

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Handbook of Neuroengineering

Abstract

Brains of human, as well as of other species, are all known to be organized into distinct neural networks, which have been found to serve as the basis for various brain functions and behaviors. More importantly, changes in brain networks are widely reported to be associated with almost all neurological and psychiatric disorders. Till now, numerous studies have been conducted to develop novel neuroimaging instruments and computational algorithms for both characterizing brain networks and investigating their behaviors in various populations of participants and patients. In this chapter, we discuss the state-of-the-art of these technologies in studying human brain networks and their applications to address fundamental questions in both basic and clinical neurosciences. We start our discussions on brain network mapping using individual neuroimaging modalities, i.e., functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), electroencephalography, electrocorticography (ECoG), positron emission tomography (PET), and functional near-infrared spectroscopy (fNIRS). We then continue with arguments on the value of multimodal neuroimaging technologies in studying human brain networks, which could achieve a “greater than the sum of its parts” knowledge about human brain networks due to complementary information provided by individual neuroimaging modalities. We discuss in particular on approaches deriving and characterizing intrinsic brain networks and their clinical applications, which are among the most significant findings about human brain networks in the past two decades. At the end, we further present our prospects on future works to address challenges in studying human brain networks, which could pave the way for the broad applications of brain networks in clinical and other real-world applications.

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Reference

  1. Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34(4), 537–541 (1995). https://doi.org/10.1002/mrm.1910340409

    Article  Google Scholar 

  2. Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., Shulman, G.L.: A default mode of brain function. Proc. Natl. Acad. Sci. U. S. A. 98(2), 676–682 (2001). https://doi.org/10.1073/pnas.98.2.676

    Article  Google Scholar 

  3. Damoiseaux, J.S., Rombouts, S.A., Barkhof, F., Scheltens, P., Stam, C.J., Smith, S.M., Beckmann, C.F.: Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. U. S. A. 103(37), 13848–13853 (2006). https://doi.org/10.1073/pnas.0601417103

    Article  Google Scholar 

  4. Fox, M.D., Raichle, M.E.: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8(9), 700–711 (2007). https://doi.org/10.1038/nrn2201

    Article  Google Scholar 

  5. Varela, F., Lachaux, J.P., Rodriguez, E., Martinerie, J.: The brainweb: phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2(4), 229–239 (2001). https://doi.org/10.1038/35067550

    Article  Google Scholar 

  6. Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A., Arieli, A.: Spontaneously emerging cortical representations of visual attributes. Nature. 425(6961), 954–956 (2003). https://doi.org/10.1038/nature02078

    Article  Google Scholar 

  7. Raichle, M.E., Mintun, M.A.: Brain work and brain imaging. Annu. Rev. Neurosci. 29, 449–476 (2006). https://doi.org/10.1146/annurev.neuro.29.051605.112819

    Article  Google Scholar 

  8. Raichle, M.E., Snyder, A.Z.: A default mode of brain function: a brief history of an evolving idea. NeuroImage. 37(4), 1083–1090.; discussion 1097-1089 (2007). https://doi.org/10.1016/j.neuroimage.2007.02.041

    Article  Google Scholar 

  9. Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E.: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U. S. A. 102(27), 9673–9678 (2005). https://doi.org/10.1073/pnas.0504136102

    Article  Google Scholar 

  10. Jafri, M.J., Pearlson, G.D., Stevens, M., Calhoun, V.D.: A method for functional network connectivity among spatially independent resting-state components in schizophrenia. NeuroImage. 39(4), 1666–1681 (2008). https://doi.org/10.1016/j.neuroimage.2007.11.001

    Article  Google Scholar 

  11. Luckhoo, H., Hale, J.R., Stokes, M.G., Nobre, A.C., Morris, P.G., Brookes, M.J., Woolrich, M.W.: Inferring task-related networks using independent component analysis in magnetoencephalography. NeuroImage. 62(1), 530–541 (2012). https://doi.org/10.1016/j.neuroimage.2012.04.046

    Article  Google Scholar 

  12. Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., Filippini, N., Watkins, K.E., Toro, R., Laird, A.R., Beckmann, C.F.: Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl. Acad. Sci. U. S. A. 106(31), 13040–13045 (2009). https://doi.org/10.1073/pnas.0905267106

    Article  Google Scholar 

  13. Zhang, N., Rane, P., Huang, W., Liang, Z., Kennedy, D., Frazier, J.A., King, J.: Mapping resting-state brain networks in conscious animals. J. Neurosci. Methods. 189(2), 186–196 (2010). https://doi.org/10.1016/j.jneumeth.2010.04.001

    Article  Google Scholar 

  14. Vincent, J.L., Patel, G.H., Fox, M.D., Snyder, A.Z., Baker, J.T., Van Essen, D.C., Zempel, J.M., Snyder, L.H., Corbetta, M., Raichle, M.E.: Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 447(7140), 83–86 (2007). https://doi.org/10.1038/nature05758

    Article  Google Scholar 

  15. Fox, M.D., Snyder, A.Z., Zacks, J.M., Raichle, M.E.: Coherent spontaneous activity accounts for trial-to-trial variability in human evoked brain responses. Nat. Neurosci. 9(1), 23–25 (2006). https://doi.org/10.1038/nn1616

    Article  Google Scholar 

  16. Fox, M.D., Snyder, A.Z., Vincent, J.L., Raichle, M.E.: Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior. Neuron. 56(1), 171–184 (2007). https://doi.org/10.1016/j.neuron.2007.08.023

    Article  Google Scholar 

  17. Mason, M.F., Norton, M.I., Van Horn, J.D., Wegner, D.M., Grafton, S.T., Macrae, C.N.: Wandering minds: the default network and stimulus-independent thought. Science. 315(5810), 393–395 (2007). https://doi.org/10.1126/science.1131295

    Article  Google Scholar 

  18. Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L.: The brain’s default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 1–38 (2008). https://doi.org/10.1196/annals.1440.011

    Article  Google Scholar 

  19. Douw, L., van Dellen, E., Gouw, A.A., Griffa, A., de Haan, W., van den Heuvel, M., Hillebrand, A., Van Mieghem, P., Nissen, I.A., Otte, W.M., Reijmer, Y.D., Schoonheim, M.M., Senden, M., van Straaten, E.C.W., Tijms, B.M., Tewarie, P., Stam, C.J.: The road ahead in clinical network neuroscience. Netw Neurosci. 3(4), 969–993 (2019). https://doi.org/10.1162/netn_a_00103

    Article  Google Scholar 

  20. Fox, M.D., Buckner, R.L., Liu, H., Chakravarty, M.M., Lozano, A.M., Pascual-Leone, A.: Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proc. Natl. Acad. Sci. U. S. A. 111(41), E4367–E4375 (2014). https://doi.org/10.1073/pnas.1405003111

    Article  Google Scholar 

  21. Fox, M.D., Greicius, M.: Clinical applications of resting state functional connectivity. Front. Syst. Neurosci. 4, 19 (2010). https://doi.org/10.3389/fnsys.2010.00019

    Article  Google Scholar 

  22. Stam, C.J.: Modern network science of neurological disorders. Nat. Rev. Neurosci. 15(10), 683–695 (2014). https://doi.org/10.1038/nrn3801

    Article  Google Scholar 

  23. Tost, H., Bilek, E., Meyer-Lindenberg, A.: Brain connectivity in psychiatric imaging genetics. NeuroImage. 62(4), 2250–2260 (2012). https://doi.org/10.1016/j.neuroimage.2011.11.007

    Article  Google Scholar 

  24. Zhang, D., Raichle, M.E.: Disease and the brain’s dark energy. Nat. Rev. Neurol. 6(1), 15–28 (2010). https://doi.org/10.1038/nrneurol.2009.198

    Article  Google Scholar 

  25. Aiello, M., Cavaliere, C., Salvatore, M.: Hybrid PET/MR imaging and brain connectivity. Front. Neurosci. 10, 64 (2016). https://doi.org/10.3389/fnins.2016.00064

    Article  Google Scholar 

  26. Bassett, D.S., Bullmore, E.T.: Human brain networks in health and disease. Curr. Opin. Neurol. 22(4), 340–347 (2009). https://doi.org/10.1097/WCO.0b013e32832d93dd

    Article  Google Scholar 

  27. Stam, C.J.: Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders. J. Neurol. Sci. 289(1-2), 128–134 (2010). https://doi.org/10.1016/j.jns.2009.08.028

    Article  Google Scholar 

  28. Nugent, A.C., Robinson, S.E., Coppola, R., Furey, M.L., Zarate Jr., C.A.: Group differences in MEG-ICA derived resting state networks: application to major depressive disorder. NeuroImage. 118, 1–12 (2015). https://doi.org/10.1016/j.neuroimage.2015.05.051

    Article  Google Scholar 

  29. Drysdale, A.T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R.N., Zebley, B., Oathes, D.J., Etkin, A., Schatzberg, A.F., Sudheimer, K., Keller, J., Mayberg, H.S., Gunning, F.M., Alexopoulos, G.S., Fox, M.D., Pascual-Leone, A., Voss, H.U., Casey, B.J., Dubin, M.J., Liston, C.: Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23(1), 28–38 (2017). https://doi.org/10.1038/nm.4246

    Article  Google Scholar 

  30. Wang, L., Hermens, D.F., Hickie, I.B., Lagopoulos, J.: A systematic review of resting-state functional-MRI studies in major depression. J. Affect. Disord. 142(1-3), 6–12 (2012). https://doi.org/10.1016/j.jad.2012.04.013

    Article  Google Scholar 

  31. Anand, A., Li, Y., Wang, Y., Wu, J., Gao, S., Bukhari, L., Mathews, V.P., Kalnin, A., Lowe, M.J.: Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study. Biol. Psychiatry. 57(10), 1079–1088 (2005). https://doi.org/10.1016/j.biopsych.2005.02.021

    Article  Google Scholar 

  32. Greicius, M.D., Flores, B.H., Menon, V., Glover, G.H., Solvason, H.B., Kenna, H., Reiss, A.L., Schatzberg, A.F.: Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry. 62(5), 429–437 (2007). https://doi.org/10.1016/j.biopsych.2006.09.020

    Article  Google Scholar 

  33. Sheline, Y.I., Raichle, M.E.: Resting state functional connectivity in preclinical Alzheimer’s disease. Biol. Psychiatry. 74(5), 340–347 (2013). https://doi.org/10.1016/j.biopsych.2012.11.028

    Article  Google Scholar 

  34. Foo, H., Mather, K.A., Jiang, J., Thalamuthu, A., Wen, W., Sachdev, P.S.: Genetic influence on ageing-related changes in resting-state brain functional networks in healthy adults: a systematic review. Neurosci. Biobehav. Rev. 113, 98–110 (2020). https://doi.org/10.1016/j.neubiorev.2020.03.011

    Article  Google Scholar 

  35. Dennis, E.L., Thompson, P.M.: Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychol. Rev. 24(1), 49–62 (2014). https://doi.org/10.1007/s11065-014-9249-6

    Article  Google Scholar 

  36. Mandal, P.K., Banerjee, A., Tripathi, M., Sharma, A.: A comprehensive review of magnetoencephalography (MEG) studies for brain functionality in healthy aging and Alzheimer’s Disease (AD). Front. Comput. Neurosci. 12, 60 (2018). https://doi.org/10.3389/fncom.2018.00060

    Article  Google Scholar 

  37. Greicius, M.D., Srivastava, G., Reiss, A.L., Menon, V.: Default-mode network activity distinguishes Alzheimer’s Disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. U. S. A. 101(13), 4637–4642 (2004). https://doi.org/10.1073/pnas.0308627101

    Article  Google Scholar 

  38. Buckner, R.L., Snyder, A.Z., Shannon, B.J., LaRossa, G., Sachs, R., Fotenos, A.F., Sheline, Y.I., Klunk, W.E., Mathis, C.A., Morris, J.C., Mintun, M.A.: Molecular, structural, and functional characterization of Alzheimer’s Disease: evidence for a relationship between default activity, amyloid, and memory. J. Neurosci. 25(34), 7709–7717 (2005). https://doi.org/10.1523/JNEUROSCI.2177-05.2005

    Article  Google Scholar 

  39. Del Guerra, A., Ahmad, S., Avram, M., Belcari, N., Berneking, A., Biagi, L., Bisogni, M.G., Brandl, F., Cabello, J., Camarlinghi, N., Cerello, P., Choi, C.H., Coli, S., Colpo, S., Fleury, J., Gagliardi, V., Giraudo, G., Heekeren, K., Kawohl, W., Kostou, T., Lefaucheur, J.L., Lerche, C., Loudos, G., Morrocchi, M., Muller, J., Mustafa, M., Neuner, I., Papadimitroulas, P., Pennazio, F., Rajkumar, R., Brambilla, C.R., Rivoire, J., Kops, E.R., Scheins, J., Schimpf, R., Shah, N.J., Sorg, C., Sportelli, G., Tosetti, M., Trinchero, R., Wyss, C., Ziegler, S., Consortium, T.: TRIMAGE: a dedicated trimodality (PET/MR/EEG) imaging tool for schizophrenia. Eur Psychiatry. 50, 7–20 (2018). https://doi.org/10.1016/j.eurpsy.2017.11.007

    Article  Google Scholar 

  40. Whitfield-Gabrieli, S., Thermenos, H.W., Milanovic, S., Tsuang, M.T., Faraone, S.V., McCarley, R.W., Shenton, M.E., Green, A.I., Nieto-Castanon, A., LaViolette, P., Wojcik, J., Gabrieli, J.D., Seidman, L.J.: Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc. Natl. Acad. Sci. U. S. A. 106(4), 1279–1284 (2009). https://doi.org/10.1073/pnas.0809141106

    Article  Google Scholar 

  41. Lynall, M.E., Bassett, D.S., Kerwin, R., McKenna, P.J., Kitzbichler, M., Muller, U., Bullmore, E.: Functional connectivity and brain networks in schizophrenia. J. Neurosci. 30(28), 9477–9487 (2010). https://doi.org/10.1523/JNEUROSCI.0333-10.2010

    Article  Google Scholar 

  42. Calhoun, V.D., Eichele, T., Pearlson, G.: Functional brain networks in schizophrenia: a review. Front. Hum. Neurosci. 3, 17 (2009). https://doi.org/10.3389/neuro.09.017.2009

    Article  Google Scholar 

  43. Jalili, M., Knyazeva, M.G.: EEG-based functional networks in schizophrenia. Comput. Biol. Med. 41(12), 1178–1186 (2011). https://doi.org/10.1016/j.compbiomed.2011.05.004

    Article  Google Scholar 

  44. Li, S., Hu, N., Zhang, W., Tao, B., Dai, J., Gong, Y., Tan, Y., Cai, D., Lui, S.: Dysconnectivity of multiple brain networks in schizophrenia: a meta-analysis of resting-state functional connectivity. Front Psychiatry. 10, 482 (2019). https://doi.org/10.3389/fpsyt.2019.00482

    Article  Google Scholar 

  45. Salvador, R., Martinez, A., Pomarol-Clotet, E., Sarro, S., Suckling, J., Bullmore, E.: Frequency based mutual information measures between clusters of brain regions in functional magnetic resonance imaging. NeuroImage. 35(1), 83–88 (2007). https://doi.org/10.1016/j.neuroimage.2006.12.001

    Article  Google Scholar 

  46. Hull, J.V., Dokovna, L.B., Jacokes, Z.J., Torgerson, C.M., Irimia, A., Van Horn, J.D.: Resting-state functional connectivity in autism spectrum disorders: a review. Front Psychiatry. 7, 205 (2016). https://doi.org/10.3389/fpsyt.2016.00205

    Article  Google Scholar 

  47. Keehn, B., Wagner, J.B., Tager-Flusberg, H., Nelson, C.A.: Functional connectivity in the first year of life in infants at-risk for autism: a preliminary near-infrared spectroscopy study. Front. Hum. Neurosci. 7, 444 (2013). https://doi.org/10.3389/fnhum.2013.00444

    Article  Google Scholar 

  48. Shou, G., Mosconi, M.W., Wang, J., Ethridge, L.E., Sweeney, J.A., Ding, L.: Electrophysiological signatures of atypical intrinsic brain connectivity networks in autism. J. Neural Eng. 14(4), 046010 (2017). https://doi.org/10.1088/1741-2552/aa6b6b

    Article  Google Scholar 

  49. Ye, A.X., Leung, R.C., Schafer, C.B., Taylor, M.J., Doesburg, S.M.: Atypical resting synchrony in autism spectrum disorder. Hum. Brain Mapp. 35(12), 6049–6066 (2014). https://doi.org/10.1002/hbm.22604

    Article  Google Scholar 

  50. Kitzbichler, M.G., Khan, S., Ganesan, S., Vangel, M.G., Herbert, M.R., Hamalainen, M.S., Kenet, T.: Altered development and multifaceted band-specific abnormalities of resting state networks in autism. Biol. Psychiatry. 77(9), 794–804 (2015). https://doi.org/10.1016/j.biopsych.2014.05.012

    Article  Google Scholar 

  51. Cherkassky, V.L., Kana, R.K., Keller, T.A., Just, M.A.: Functional connectivity in a baseline resting-state network in autism. Neuroreport. 17(16), 1687–1690 (2006). https://doi.org/10.1097/01.wnr.0000239956.45448.4c

    Article  Google Scholar 

  52. Horstmann, M.T., Bialonski, S., Noennig, N., Mai, H., Prusseit, J., Wellmer, J., Hinrichs, H., Lehnertz, K.: State dependent properties of epileptic brain networks: comparative graph-theoretical analyses of simultaneously recorded EEG and MEG. Clin. Neurophysiol. 121(2), 172–185 (2010). https://doi.org/10.1016/j.clinph.2009.10.013

    Article  Google Scholar 

  53. Centeno, M., Carmichael, D.W.: Network connectivity in epilepsy: resting state fMRI and EEG-fMRI contributions. Front. Neurol. 5, 93 (2014). https://doi.org/10.3389/fneur.2014.00093

    Article  Google Scholar 

  54. Waites, A.B., Briellmann, R.S., Saling, M.M., Abbott, D.F., Jackson, G.D.: Functional connectivity networks are disrupted in left temporal lobe epilepsy. Ann. Neurol. 59(2), 335–343 (2006). https://doi.org/10.1002/ana.20733

    Article  Google Scholar 

  55. Thut, G., Schyns, P.G., Gross, J.: Entrainment of perceptually relevant brain oscillations by non-invasive rhythmic stimulation of the human brain. Front. Psychol. 2, 170 (2011). https://doi.org/10.3389/fpsyg.2011.00170

    Article  Google Scholar 

  56. Rosazza, C., Minati, L.: Resting-state brain networks: literature review and clinical applications. Neurol. Sci. 32(5), 773–785 (2011). https://doi.org/10.1007/s10072-011-0636-y

    Article  Google Scholar 

  57. Hasson, U., Malach, R., Heeger, D.J.: Reliability of cortical activity during natural stimulation. Trends Cogn. Sci. 14(1), 40–48 (2010). https://doi.org/10.1016/j.tics.2009.10.011

    Article  Google Scholar 

  58. Kucyi, A., Schrouff, J., Bickel, S., Foster, B.L., Shine, J.M., Parvizi, J.: Intracranial electrophysiology reveals reproducible intrinsic functional connectivity within human brain networks. J. Neurosci. 38(17), 4230–4242 (2018). https://doi.org/10.1523/JNEUROSCI.0217-18.2018

    Article  Google Scholar 

  59. Watabe, T., Hatazawa, J.: Evaluation of functional connectivity in the brain using positron emission tomography: a mini-review. Front. Neurosci. 13, 775 (2019). https://doi.org/10.3389/fnins.2019.00775

    Article  Google Scholar 

  60. Eggebrecht, A.T., Ferradal, S.L., Robichaux-Viehoever, A., Hassanpour, M.S., Dehghani, H., Snyder, A.Z., Hershey, T., Culver, J.P.: Mapping distributed brain function and networks with diffuse optical tomography. Nat. Photonics. 8(6), 448–454 (2014). https://doi.org/10.1038/nphoton.2014.107

    Article  Google Scholar 

  61. Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A.: Neurophysiological investigation of the basis of the fMRI signal. Nature. 412(6843), 150–157 (2001). https://doi.org/10.1038/35084005

    Article  Google Scholar 

  62. Boto, E., Holmes, N., Leggett, J., Roberts, G., Shah, V., Meyer, S.S., Munoz, L.D., Mullinger, K.J., Tierney, T.M., Bestmann, S., Barnes, G.R., Bowtell, R., Brookes, M.J.: Moving magnetoencephalography towards real-world applications with a wearable system. Nature. 555(7698), 657–661 (2018). https://doi.org/10.1038/nature26147

    Article  Google Scholar 

  63. Vitali, P., Di Perri, C., Vaudano, A.E., Meletti, S., Villani, F.: Integration of multimodal neuroimaging methods: a rationale for clinical applications of simultaneous EEG-fMRI. Funct. Neurol. 30(1), 9–20 (2015)

    Google Scholar 

  64. Meir-Hasson, Y., Kinreich, S., Podlipsky, I., Hendler, T., Intrator, N.: An EEG finger-print of fMRI deep regional activation. NeuroImage. 102(Pt 1), 128–141 (2014). https://doi.org/10.1016/j.neuroimage.2013.11.004

    Article  Google Scholar 

  65. Schlemmer, H.P., Pichler, B.J., Schmand, M., Burbar, Z., Michel, C., Ladebeck, R., Jattke, K., Townsend, D., Nahmias, C., Jacob, P.K., Heiss, W.D., Claussen, C.D.: Simultaneous MR/PET imaging of the human brain: feasibility study. Radiology. 248(3), 1028–1035 (2008). https://doi.org/10.1148/radiol.2483071927

    Article  Google Scholar 

  66. Ahlfors, S.P., Han, J., Belliveau, J.W., Hamalainen, M.S.: Sensitivity of MEG and EEG to source orientation. Brain Topogr. 23(3), 227–232 (2010). https://doi.org/10.1007/s10548-010-0154-x

    Article  Google Scholar 

  67. Neuner, I., Rajkumar, R., Brambilla, C.R., Ramkiran, S., Ruch, A., Orth, L., Farrher, E., Mauler, J., Wyss, C., Kops, E.R., Scheins, J.: Simultaneous PET-MR-EEG: technology, challenges and application in clinical neuroscience. IEEE Trans Radiat Plasma Med Sci. 3(3), 377–385 (2019)

    Article  Google Scholar 

  68. Shah, N.J., Arrubla, J., Rajkumar, R., Farrher, E., Mauler, J., Kops, E.R., Tellmann, L., Scheins, J., Boers, F., Dammers, J., Sripad, P., Lerche, C., Langen, K.J., Herzog, H., Neuner, I.: Multimodal fingerprints of resting state networks as assessed by simultaneous trimodal MR-PET-EEG imaging. Sci. Rep. 7(1), 6452 (2017). https://doi.org/10.1038/s41598-017-05484-w

    Article  Google Scholar 

  69. van den Heuvel, M.P., Hulshoff Pol, H.E.: Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20(8), 519–534 (2010). https://doi.org/10.1016/j.euroneuro.2010.03.008

    Article  Google Scholar 

  70. Rossini, P.M., Di Iorio, R., Bentivoglio, M., Bertini, G., Ferreri, F., Gerloff, C., Ilmoniemi, R.J., Miraglia, F., Nitsche, M.A., Pestilli, F., Rosanova, M., Shirota, Y., Tesoriero, C., Ugawa, Y., Vecchio, F., Ziemann, U., Hallett, M.: Methods for analysis of brain connectivity: an IFCN-sponsored review. Clin. Neurophysiol. 130(10), 1833–1858 (2019). https://doi.org/10.1016/j.clinph.2019.06.006

    Article  Google Scholar 

  71. Lee, M.H., Smyser, C.D., Shimony, J.S.: Resting-state fMRI: a review of methods and clinical applications. AJNR Am. J. Neuroradiol. 34(10), 1866–1872 (2013). https://doi.org/10.3174/ajnr.A3263

    Article  Google Scholar 

  72. Bandettini, P.A., Wong, E.C., Hinks, R.S., Tikofsky, R.S., Hyde, J.S.: Time course EPI of human brain function during task activation. Magn. Reson. Med. 25(2), 390–397 (1992). https://doi.org/10.1002/mrm.1910250220

    Article  Google Scholar 

  73. Kwong, K.K., Belliveau, J.W., Chesler, D.A., Goldberg, I.E., Weisskoff, R.M., Poncelet, B.P., Kennedy, D.N., Hoppel, B.E., Cohen, M.S., Turner, R., et al.: Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. U. S. A. 89(12), 5675–5679 (1992). https://doi.org/10.1073/pnas.89.12.5675

    Article  Google Scholar 

  74. Ogawa, S., Tank, D.W., Menon, R., Ellermann, J.M., Kim, S.G., Merkle, H., Ugurbil, K.: Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. U. S. A. 89(13), 5951–5955 (1992). https://doi.org/10.1073/pnas.89.13.5951

    Article  Google Scholar 

  75. Biswal, B.B., Van Kylen, J., Hyde, J.S.: Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps. NMR Biomed. 10(4-5), 165–170 (1997). https://doi.org/10.1002/(sici)1099-1492(199706/08)10:4/5<165::aid-nbm454>3.0.co;2-7

    Article  Google Scholar 

  76. Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J.: A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14(3), 140–151 (2001). https://doi.org/10.1002/hbm.1048

    Article  Google Scholar 

  77. Beckmann, C.F., DeLuca, M., Devlin, J.T., Smith, S.M.: Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 360(1457), 1001–1013 (2005). https://doi.org/10.1098/rstb.2005.1634

    Article  Google Scholar 

  78. Cordes, D., Haughton, V., Carew, J.D., Arfanakis, K., Maravilla, K.: Hierarchical clustering to measure connectivity in fMRI resting-state data. Magn. Reson. Imaging. 20(4), 305–317 (2002). https://doi.org/10.1016/s0730-725x(02)00503-9

    Article  Google Scholar 

  79. Salvador, R., Suckling, J., Coleman, M.R., Pickard, J.D., Menon, D., Bullmore, E.: Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb. Cortex. 15(9), 1332–1342 (2005). https://doi.org/10.1093/cercor/bhi016

    Article  Google Scholar 

  80. van den Heuvel, M., Mandl, R., Hulshoff Pol, H.: Normalized cut group clustering of resting-state FMRI data. PLoS One. 3(4), e2001 (2008). https://doi.org/10.1371/journal.pone.0002001

    Article  Google Scholar 

  81. Lee, M.H., Hacker, C.D., Snyder, A.Z., Corbetta, M., Zhang, D., Leuthardt, E.C., Shimony, J.S.: Clustering of resting state networks. PLoS One. 7(7), e40370 (2012). https://doi.org/10.1371/journal.pone.0040370

    Article  Google Scholar 

  82. Choi, S., Cichocki, A., Park, H.J., Lee, S.Y.: Blind source separation and independent component analysis: a review. Neural Inf Process-Lett Rev. 6(1), 1–57 (2005)

    Google Scholar 

  83. Kelly, R.E., Wang, Z., Alexopoulos, G.S., Gunning, F.M., Murphy, C.F., Morimoto, S.S., Kanellopoulos, D., Jia, Z., Lim, K.O., Hoptman, M.J.: Hybrid ICA-seed-based methods for fMRI functional connectivity assessment: a feasibility study. Int J Biomed Imaging. 2010, 868976 (2010). https://doi.org/10.1155/2010/868976

    Article  Google Scholar 

  84. Hipp, J.F., Hawellek, D.J., Corbetta, M., Siegel, M., Engel, A.K.: Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat. Neurosci. 15(6), 884–890 (2012). https://doi.org/10.1038/nn.3101

    Article  Google Scholar 

  85. O’Neill, G.C., Barratt, E.L., Hunt, B.A., Tewarie, P.K., Brookes, M.J.: Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods. Phys. Med. Biol. 60(21), R271–R295 (2015). https://doi.org/10.1088/0031-9155/60/21/R271

    Article  Google Scholar 

  86. Sun, F.T., Miller, L.M., D’Esposito, M.: Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. NeuroImage. 21(2), 647–658 (2004). https://doi.org/10.1016/j.neuroimage.2003.09.056

    Article  Google Scholar 

  87. Shaw, J.C.: Correlation and coherence analysis of the EEG: a selective tutorial review. Int. J. Psychophysiol. 1(3), 255–266 (1984). https://doi.org/10.1016/0167-8760(84)90045-x

    Article  Google Scholar 

  88. Srinivasan, R., Winter, W.R., Ding, J., Nunez, P.L.: EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J. Neurosci. Methods. 166(1), 41–52 (2007). https://doi.org/10.1016/j.jneumeth.2007.06.026

    Article  Google Scholar 

  89. Brookes, M.J., Woolrich, M., Luckhoo, H., Price, D., Hale, J.R., Stephenson, M.C., Barnes, G.R., Smith, S.M., Morris, P.G.: Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc. Natl. Acad. Sci. U. S. A. 108(40), 16783–16788 (2011). https://doi.org/10.1073/pnas.1112685108

    Article  Google Scholar 

  90. Onton, J., Westerfield, M., Townsend, J., Makeig, S.: Imaging human EEG dynamics using independent component analysis. Neurosci. Biobehav. Rev. 30(6), 808–822 (2006). https://doi.org/10.1016/j.neubiorev.2006.06.007

    Article  Google Scholar 

  91. Ding, L., Shou, G., Yuan, H., Urbano, D., Cha, Y.H.: Lasting modulation effects of rTMS on neural activity and connectivity as revealed by resting-state EEG. IEEE Trans. Biomed. Eng. 61(7), 2070–2080 (2014). https://doi.org/10.1109/TBME.2014.2313575

    Article  Google Scholar 

  92. Liu, Q.Y., Farahibozorg, S., Porcaro, C., Wenderoth, N., Mantini, D.: Detecting large-scale networks in the human brain using high-density electroencephalography. Hum. Brain Mapp. 38(9), 4631–4643 (2017). https://doi.org/10.1002/hbm.23688

    Article  Google Scholar 

  93. Sockeel, S., Schwartz, D., Pelegrini-Issac, M., Benali, H.: Large-scale functional networks identified from resting-state EEG using spatial ICA. PLoS One. 11(1), e0146845 (2016). https://doi.org/10.1371/journal.pone.0146845

    Article  Google Scholar 

  94. Britz, J., Van De Ville, D., Michel, C.M.: BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage. 52(4), 1162–1170 (2010). https://doi.org/10.1016/j.neuroimage.2010.02.052

    Article  Google Scholar 

  95. Musso, F., Brinkmeyer, J., Mobascher, A., Warbrick, T., Winterer, G.: Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks. NeuroImage. 52(4), 1149–1161 (2010). https://doi.org/10.1016/j.neuroimage.2010.01.093

    Article  Google Scholar 

  96. Li, C., Yuan, H., Shou, G., Cha, Y.H., Sunderam, S., Besio, W., Ding, L.: Cortical statistical correlation tomography of EEG resting state networks. Front. Neurosci. 12, 365 (2018). https://doi.org/10.3389/fnins.2018.00365

    Article  Google Scholar 

  97. Shou, G., Yuan, H., Li, C., Chen, Y., Chen, Y., Ding, L.: Whole-brain electrophysiological functional connectivity dynamics in resting-state EEG. J. Neural Eng. 17(2), 026016 (2020). https://doi.org/10.1088/1741-2552/ab7ad3

    Article  Google Scholar 

  98. Yuan, H., Ding, L., Zhu, M., Zotev, V., Phillips, R., Bodurka, J.: Reconstructing large-scale brain resting-state networks from high-resolution EEG: spatial and temporal comparisons with fMRI. Brain Connect. 6(2), 122–135 (2016). https://doi.org/10.1089/brain.2014.0336

    Article  Google Scholar 

  99. Gusnard, D.A., Raichle, M.E., Raichle, M.E.: Searching for a baseline: functional imaging and the resting human brain. Nat. Rev. Neurosci. 2(10), 685–694 (2001). https://doi.org/10.1038/35094500

    Article  Google Scholar 

  100. Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V.: Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. U. S. A. 100(1), 253–258 (2003). https://doi.org/10.1073/pnas.0135058100

    Article  Google Scholar 

  101. Stevens, W.D., Buckner, R.L., Schacter, D.L.: Correlated low-frequency BOLD fluctuations in the resting human brain are modulated by recent experience in category-preferential visual regions. Cereb. Cortex. 20(8), 1997–2006 (2010). https://doi.org/10.1093/cercor/bhp270

    Article  Google Scholar 

  102. Cordes, D., Haughton, V.M., Arfanakis, K., Wendt, G.J., Turski, P.A., Moritz, C.H., Quigley, M.A., Meyerand, M.E.: Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR Am. J. Neuroradiol. 21(9), 1636–1644 (2000)

    Google Scholar 

  103. Zanto, T.P., Gazzaley, A.: Fronto-parietal network: flexible hub of cognitive control. Trends Cogn. Sci. 17(12), 602–603 (2013). https://doi.org/10.1016/j.tics.2013.10.001

    Article  Google Scholar 

  104. Raichle, M.E.: The brain’s default mode network. Annu. Rev. Neurosci. 38, 433–447 (2015). https://doi.org/10.1146/annurev-neuro-071013-014030

    Article  Google Scholar 

  105. Buckner, R.L., DiNicola, L.M.: The brain’s default network: updated anatomy, physiology and evolving insights. Nat. Rev. Neurosci. 20(10), 593–608 (2019). https://doi.org/10.1038/s41583-019-0212-7

    Article  Google Scholar 

  106. Badhwar, A., Tam, A., Dansereau, C., Orban, P., Hoffstaedter, F., Bellec, P.: Resting-state network dysfunction in Alzheimer’s disease: a systematic review and meta-analysis. Alzheimers Dement (Amst). 8, 73–85 (2017). https://doi.org/10.1016/j.dadm.2017.03.007

    Article  Google Scholar 

  107. Hull, J.V., Dokovna, L.B., Jacokes, Z.J., Torgerson, C.M., Irimia, A., Van Horn, J.D., Consortium, G.R.: Corrigendum: resting-state functional connectivity in autism spectrum disorders: a review. Front Psychiatry. 9, 268 (2018). https://doi.org/10.3389/fpsyt.2018.00268

    Article  Google Scholar 

  108. Heine, L., Soddu, A., Gomez, F., Vanhaudenhuyse, A., Tshibanda, L., Thonnard, M., Charland-Verville, V., Kirsch, M., Laureys, S., Demertzi, A.: Resting state networks and consciousness: alterations of multiple resting state network connectivity in physiological, pharmacological, and pathological consciousness States. Front. Psychol. 3, 295 (2012). https://doi.org/10.3389/fpsyg.2012.00295

    Article  Google Scholar 

  109. Bagshaw, A.P., Cavanna, A.E.: Resting state networks in paroxysmal disorders of consciousness. Epilepsy Behav. 26(3), 290–294 (2013). https://doi.org/10.1016/j.yebeh.2012.09.020

    Article  Google Scholar 

  110. Stevens, J.S., Jovanovic, T., Fani, N., Ely, T.D., Glover, E.M., Bradley, B., Ressler, K.J.: Disrupted amygdala-prefrontal functional connectivity in civilian women with posttraumatic stress disorder. J. Psychiatr. Res. 47(10), 1469–1478 (2013). https://doi.org/10.1016/j.jpsychires.2013.05.031

    Article  Google Scholar 

  111. Lanius, R.A., Williamson, P.C., Bluhm, R.L., Densmore, M., Boksman, K., Neufeld, R.W., Gati, J.S., Menon, R.S.: Functional connectivity of dissociative responses in posttraumatic stress disorder: a functional magnetic resonance imaging investigation. Biol. Psychiatry. 57(8), 873–884 (2005). https://doi.org/10.1016/j.biopsych.2005.01.011

    Article  Google Scholar 

  112. Cha, Y.H., Chakrapani, S., Craig, A., Baloh, R.W.: Metabolic and functional connectivity changes in mal de debarquement syndrome. PLoS One. 7(11), e49560 (2012). https://doi.org/10.1371/journal.pone.0049560

    Article  Google Scholar 

  113. Yuan, H., Shou, G., Gleghorn, D., Ding, L., Cha, Y.H.: Resting state functional connectivity signature of treatment effects of repetitive transcranial magnetic stimulation in Mal de Debarquement syndrome. Brain Connect. 7(9), 617–626 (2017). https://doi.org/10.1089/brain.2017.0514

    Article  Google Scholar 

  114. Anticevic, A., Cole, M.W., Murray, J.D., Corlett, P.R., Wang, X.J., Krystal, J.H.: The role of default network deactivation in cognition and disease. Trends Cogn. Sci. 16(12), 584–592 (2012). https://doi.org/10.1016/j.tics.2012.10.008

    Article  Google Scholar 

  115. Sheline, Y.I., Price, J.L., Yan, Z., Mintun, M.A.: Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc. Natl. Acad. Sci. U. S. A. 107(24), 11020–11025 (2010). https://doi.org/10.1073/pnas.1000446107

    Article  Google Scholar 

  116. Seeley, W.W., Crawford, R.K., Zhou, J., Miller, B.L., Greicius, M.D.: Neurodegenerative diseases target large-scale human brain networks. Neuron. 62(1), 42–52 (2009). https://doi.org/10.1016/j.neuron.2009.03.024

    Article  Google Scholar 

  117. Kuperberg, G., Heckers, S.: Schizophrenia and cognitive function. Curr. Opin. Neurobiol. 10(2), 205–210 (2000). https://doi.org/10.1016/s0959-4388(00)00068-4

    Article  Google Scholar 

  118. Fingelkurts, A.A., Fingelkurts, A.A., Rytsala, H., Suominen, K., Isometsa, E., Kahkonen, S.: Impaired functional connectivity at EEG alpha and theta frequency bands in major depression. Hum. Brain Mapp. 28(3), 247–261 (2007). https://doi.org/10.1002/hbm.20275

    Article  Google Scholar 

  119. Mumtaz, W., Ali, S.S.A., Yasin, M.A.M., Malik, A.S.: A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med. Biol. Eng. Comput. 56(2), 233–246 (2018). https://doi.org/10.1007/s11517-017-1685-z

    Article  Google Scholar 

  120. Stam, C.J., Jones, B.F., Manshanden, I., van Cappellen van Walsum, A.M., Montez, T., Verbunt, J.P., de Munck, J.C., van Dijk, B.W., Berendse, H.W., Scheltens, P.: Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer’s Disease. NeuroImage. 32(3), 1335–1344 (2006). https://doi.org/10.1016/j.neuroimage.2006.05.033

    Article  Google Scholar 

  121. Micheloyannis, S., Pachou, E., Stam, C.J., Breakspear, M., Bitsios, P., Vourkas, M., Erimaki, S., Zervakis, M.: Small-world networks and disturbed functional connectivity in schizophrenia. Schizophr. Res. 87(1-3), 60–66 (2006). https://doi.org/10.1016/j.schres.2006.06.028

    Article  Google Scholar 

  122. Brookes, M.J., Tewarie, P.K., Hunt, B.A.E., Robson, S.E., Gascoyne, L.E., Liddle, E.B., Liddle, P.F., Morris, P.G.: A multi-layer network approach to MEG connectivity analysis. NeuroImage. 132, 425–438 (2016). https://doi.org/10.1016/j.neuroimage.2016.02.045

    Article  Google Scholar 

  123. Hinkley, L.B., Owen, J.P., Fisher, M., Findlay, A.M., Vinogradov, S., Nagarajan, S.S.: Cognitive impairments in schizophrenia as assessed through activation and connectivity measures of magnetoencephalography (MEG) data. Front. Hum. Neurosci. 3, 73 (2010). https://doi.org/10.3389/neuro.09.073.2009

    Article  Google Scholar 

  124. Cha, Y.H., Shou, G., Gleghorn, D., Doudican, B.C., Yuan, H., Ding, L.: Electrophysiological signatures of intrinsic functional connectivity related to rTMS treatment for Mal de Debarquement syndrome. Brain Topogr. 31(6), 1047–1058 (2018). https://doi.org/10.1007/s10548-018-0671-6

    Article  Google Scholar 

  125. Chen, Y., Cha, Y.H., Li, C., Shou, G., Gleghorn, D., Ding, L., Yuan, H.: Multimodal imaging of repetitive transcranial magnetic stimulation effect on brain network: a combined electroencephalogram and functional magnetic resonance imaging study. Brain Connect. 9(4), 311–321 (2019). https://doi.org/10.1089/brain.2018.0647

    Article  Google Scholar 

  126. Harrison, A.H., Connolly, J.F.: Finding a way in: a review and practical evaluation of fMRI and EEG for detection and assessment in disorders of consciousness. Neurosci. Biobehav. Rev. 37(8), 1403–1419 (2013). https://doi.org/10.1016/j.neubiorev.2013.05.004

    Article  Google Scholar 

  127. Bai, Y., Xia, X., Li, X.: A review of resting-state electroencephalography analysis in disorders of consciousness. Front. Neurol. 8, 471 (2017). https://doi.org/10.3389/fneur.2017.00471

    Article  Google Scholar 

  128. Engdahl, B., Leuthold, A.C., Tan, H.R., Lewis, S.M., Winskowski, A.M., Dikel, T.N., Georgopoulos, A.P.: Post-traumatic stress disorder: a right temporal lobe syndrome? J. Neural Eng. 7(6), 066005 (2010). https://doi.org/10.1088/1741-2560/7/6/066005

    Article  Google Scholar 

  129. Yuan, H., Phillips, R., Wong, C.K., Zotev, V., Misaki, M., Wurfel, B., Krueger, F., Feldner, M., Bodurka, J.: Tracking resting state connectivity dynamics in veterans with PTSD. Neuroimage Clin. 19, 260–270 (2018). https://doi.org/10.1016/j.nicl.2018.04.014

    Article  Google Scholar 

  130. Lehmann, D., Ozaki, H., Pal, I.: EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr. Clin. Neurophysiol. 67(3), 271–288 (1987). https://doi.org/10.1016/0013-4694(87)90025-3

    Article  Google Scholar 

  131. Lehmann, D.: Functional states of the brain: their determinants. Elsevier/North-Holland Biomedical Press, Amsterdam (1980)

    Google Scholar 

  132. Khanna, A., Pascual-Leone, A., Michel, C.M., Farzan, F.: Microstates in resting-state EEG: current status and future directions. Neurosci. Biobehav. Rev. 49, 105–113 (2015). https://doi.org/10.1016/j.neubiorev.2014.12.010

    Article  Google Scholar 

  133. Yuan, H., Zotev, V., Phillips, R., Drevets, W.C., Bodurka, J.: Spatiotemporal dynamics of the brain at rest–exploring EEG microstates as electrophysiological signatures of BOLD resting state networks. NeuroImage. 60(4), 2062–2072 (2012). https://doi.org/10.1016/j.neuroimage.2012.02.031

    Article  Google Scholar 

  134. Chen, J.L., Ros, T., Gruzelier, J.H.: Dynamic changes of ICA-derived EEG functional connectivity in the resting state. Hum. Brain Mapp. 34(4), 852–868 (2013). https://doi.org/10.1002/hbm.21475

    Article  Google Scholar 

  135. Hiltunen, T., Kantola, J., Abou Elseoud, A., Lepola, P., Suominen, K., Starck, T., Nikkinen, J., Remes, J., Tervonen, O., Palva, S., Kiviniemi, V., Palva, J.M.: Infra-slow EEG fluctuations are correlated with resting-state network dynamics in fMRI. J. Neurosci. 34(2), 356–362 (2014). https://doi.org/10.1523/JNEUROSCI.0276-13.2014

    Article  Google Scholar 

  136. Shou, G., Ding, L., Dasari, D.: Probing neural activations from continuous EEG in a real-world task: time-frequency independent component analysis. J. Neurosci. Methods. 209(1), 22–34 (2012). https://doi.org/10.1016/j.jneumeth.2012.05.022

    Article  Google Scholar 

  137. Dasari, D., Shou, G., Ding, L.: ICA-derived EEG correlates to mental fatigue, effort, and workload in a realistically simulated air traffic control task. Front. Neurosci. 11, 297 (2017). https://doi.org/10.3389/fnins.2017.00297

    Article  Google Scholar 

  138. Huang, R.S., Jung, T.P., Delorme, A., Makeig, S.: Tonic and phasic electroencephalographic dynamics during continuous compensatory tracking. NeuroImage. 39(4), 1896–1909 (2008). https://doi.org/10.1016/j.neuroimage.2007.10.036

    Article  Google Scholar 

  139. Ponomarev, V.A., Mueller, A., Candrian, G., Grin-Yatsenko, V.A., Kropotov, J.D.: Group Independent Component Analysis (gICA) and Current Source Density (CSD) in the study of EEG in ADHD adults. Clin. Neurophysiol. 125(1), 83–97 (2014). https://doi.org/10.1016/j.clinph.2013.06.015

    Article  Google Scholar 

  140. Delorme, A., Palmer, J., Onton, J., Oostenveld, R., Makeig, S.: Independent EEG sources are dipolar. PLoS One. 7(2), e30135 (2012). https://doi.org/10.1371/journal.pone.0030135

    Article  Google Scholar 

  141. Sarvas, J.: Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Phys. Med. Biol. 32(1), 11–22 (1987). https://doi.org/10.1088/0031-9155/32/1/004

    Article  Google Scholar 

  142. van den Broek, S.P., Reinders, F., Donderwinkel, M., Peters, M.J.: Volume conduction effects in EEG and MEG. Electroencephalogr. Clin. Neurophysiol. 106(6), 522–534 (1998). https://doi.org/10.1016/s0013-4694(97)00147-8

    Article  Google Scholar 

  143. Lai, M., Demuru, M., Hillebrand, A., Fraschini, M.: A comparison between scalp- and source-reconstructed EEG networks. Sci. Rep. 8(1), 12269 (2018). https://doi.org/10.1038/s41598-018-30869-w

    Article  Google Scholar 

  144. Aoki, Y., Ishii, R., Pascual-Marqui, R.D., Canuet, L., Ikeda, S., Hata, M., Imajo, K., Matsuzaki, H., Musha, T., Asada, T., Iwase, M., Takeda, M.: Detection of EEG-resting state independent networks by eLORETA-ICA method. Front. Hum. Neurosci. 9, 31 (2015). https://doi.org/10.3389/fnhum.2015.00031

    Article  Google Scholar 

  145. de Pasquale, F., Della Penna, S., Snyder, A.Z., Lewis, C., Mantini, D., Marzetti, L., Belardinelli, P., Ciancetta, L., Pizzella, V., Romani, G.L., Corbetta, M.: Temporal dynamics of spontaneous MEG activity in brain networks. Proc. Natl. Acad. Sci. U. S. A. 107(13), 6040–6045 (2010). https://doi.org/10.1073/pnas.0913863107

    Article  Google Scholar 

  146. Wens, V., Bourguignon, M., Goldman, S., Marty, B., Op de Beeck, M., Clumeck, C., Mary, A., Peigneux, P., Van Bogaert, P., Brookes, M.J., De Tiege, X.: Inter- and intra-subject variability of neuromagnetic resting state networks. Brain Topogr. 27(5), 620–634 (2014). https://doi.org/10.1007/s10548-014-0364-8

    Article  Google Scholar 

  147. Brookes, M.J., Hale, J.R., Zumer, J.M., Stevenson, C.M., Francis, S.T., Barnes, G.R., Owen, J.P., Morris, P.G., Nagarajan, S.S.: Measuring functional connectivity using MEG: methodology and comparison with fcMRI. NeuroImage. 56(3), 1082–1104 (2011). https://doi.org/10.1016/j.neuroimage.2011.02.054

    Article  Google Scholar 

  148. Baillet, S., Mosher, J.C., Leahy, R.M.: electromagnetic brain mapping. IEEE Signal Process. Mag. 18, 14–30 (2001)

    Article  Google Scholar 

  149. Ding, L., Yuan, H.: Inverse source imaging methods in recovering distributed brain sources. Biomed. Eng. Lett. 2(1), 2–7 (2012)

    Article  Google Scholar 

  150. Liu, Q., Ganzetti, M., Wenderoth, N., Mantini, D.: Detecting large-scale brain networks using EEG: impact of electrode density, head modeling and source localization. Front Neuroinform. 12, 4 (2018). https://doi.org/10.3389/fninf.2018.00004

    Article  Google Scholar 

  151. Cho, J.H., Vorwerk, J., Wolters, C.H., Knosche, T.R.: Influence of the head model on EEG and MEG source connectivity analyses. NeuroImage. 110, 60–77 (2015). https://doi.org/10.1016/j.neuroimage.2015.01.043

    Article  Google Scholar 

  152. He, B., Sohrabpour, A., Brown, E., Liu, Z.: Electrophysiological source imaging: a noninvasive window to brain dynamics. Annu. Rev. Biomed. Eng. 20, 171–196 (2018). https://doi.org/10.1146/annurev-bioeng-062117-120853

    Article  Google Scholar 

  153. Michel, C.M., Brunet, D.: EEG source imaging: a practical review of the analysis steps. Front. Neurol. 10, 325 (2019). https://doi.org/10.3389/fneur.2019.00325

    Article  Google Scholar 

  154. He, B., Ding, L., Sohrabpour, A.: Electrophysiological mapping and source imaging. In: He, B. (ed.) Neural Engineering. Kluwer Academic/Plenum Publishers (2020)

    Chapter  Google Scholar 

  155. Hamalainen, M.S., Ilmoniemi, R.J.: Interpreting magnetic-fields of the brain – minimum norm estimates. Med. Biol. Eng. Comput. 32(1), 35–42 (1994). https://doi.org/10.1007/Bf02512476

    Article  Google Scholar 

  156. van Drongelen, W., Yuchtman, M., Van Veen, B.D., van Huffelen, A.C.: A spatial filtering technique to detect and localize multiple sources in the brain. Brain Topogr. 9, 39–49 (1996)

    Article  Google Scholar 

  157. Ding, L., Yuan, H.: Simultaneous EEG and MEG source reconstruction in sparse electromagnetic source imaging. Hum. Brain Mapp. 34(4), 775–795 (2013). https://doi.org/10.1002/hbm.21473

    Article  Google Scholar 

  158. Zhu, M., Zhang, W., Dickens, D.L., Ding, L.: Reconstructing spatially extended brain sources via enforcing multiple transform sparseness. NeuroImage. 86, 280–293 (2014). https://doi.org/10.1016/j.neuroimage.2013.09.070

    Article  Google Scholar 

  159. Palva, J.M., Wang, S.H., Palva, S., Zhigalov, A., Monto, S., Brookes, M.J., Schoffelen, J.M., Jerbi, K.: Ghost interactions in MEG/EEG source space: a note of caution on inter-areal coupling measures. NeuroImage. 173, 632–643 (2018). https://doi.org/10.1016/j.neuroimage.2018.02.032

    Article  Google Scholar 

  160. Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., Hallett, M.: Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin. Neurophysiol. 115(10), 2292–2307 (2004). https://doi.org/10.1016/j.clinph.2004.04.029

    Article  Google Scholar 

  161. Colclough, G.L., Brookes, M.J., Smith, S.M., Woolrich, M.W.: A symmetric multivariate leakage correction for MEG connectomes. NeuroImage. 117, 439–448 (2015). https://doi.org/10.1016/j.neuroimage.2015.03.071

    Article  Google Scholar 

  162. Coquelet, N., De Tiege, X., Destoky, F., Roshchupkina, L., Bourguignon, M., Goldman, S., Peigneux, P., Wens, V.: Comparing MEG and high-density EEG for intrinsic functional connectivity mapping. NeuroImage. 210, 116556 (2020). https://doi.org/10.1016/j.neuroimage.2020.116556

    Article  Google Scholar 

  163. van Diessen, E., Numan, T., van Dellen, E., van der Kooi, A.W., Boersma, M., Hofman, D., van Lutterveld, R., van Dijk, B.W., van Straaten, E.C., Hillebrand, A., Stam, C.J.: Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin. Neurophysiol. 126(8), 1468–1481 (2015). https://doi.org/10.1016/j.clinph.2014.11.018

    Article  Google Scholar 

  164. Lachaux, J.P., Rodriguez, E., Martinerie, J., Varela, F.J.: Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8(4), 194–208 (1999). https://doi.org/10.1002/(sici)1097-0193(1999)8:4<194::aid-hbm4>3.0.co;2-c

    Article  Google Scholar 

  165. Jonmohamadi, Y., Poudel, G., Innes, C., Jones, R.: Source-space ICA for EEG source separation, localization, and time-course reconstruction. NeuroImage. 101, 720–737 (2014). https://doi.org/10.1016/j.neuroimage.2014.07.052

    Article  Google Scholar 

  166. Jonmohamadi, Y., Jones, R.D.: Source-space ICA for MEG source imaging. J. Neural Eng. 13(1), 016005 (2016). https://doi.org/10.1088/1741-2560/13/1/016005

    Article  Google Scholar 

  167. Shou, G., Ding, L.: Reconstructing cortical intrinsic connectivity networks using a regression method combining EEG data from sensor and source levels(.). Conf Proc IEEE Eng Med Biol Soc. 2019, 1698–1701 (2019). https://doi.org/10.1109/EMBC.2019.8857687

    Article  Google Scholar 

  168. Shou, G., Ding, L.: Reconstructing cortical intrinsic connectivity networks using a regression method combining EEG data from sensor and source levels. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1698–1701, Berlin (2019)

    Google Scholar 

  169. Yeo, B.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zollei, L., Polimeni, J.R., Fischl, B., Liu, H., Buckner, R.L.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106(3), 1125–1165 (2011). https://doi.org/10.1152/jn.00338.2011

    Article  Google Scholar 

  170. Marino, M., Arcara, G., Porcaro, C., Mantini, D.: Hemodynamic correlates of electrophysiological activity in the default mode network. Front. Neurosci. 13, 1060 (2019). https://doi.org/10.3389/fnins.2019.01060

    Article  Google Scholar 

  171. Baker, A.P., Brookes, M.J., Rezek, I.A., Smith, S.M., Behrens, T., Probert Smith, P.J., Woolrich, M.: Fast transient networks in spontaneous human brain activity. elife. 3, e01867 (2014). https://doi.org/10.7554/eLife.01867

    Article  Google Scholar 

  172. O’Neill, G.C., Tewarie, P., Vidaurre, D., Liuzzi, L., Woolrich, M.W., Brookes, M.J.: Dynamics of large-scale electrophysiological networks: a technical review. NeuroImage. 180(Pt B), 559–576 (2018). https://doi.org/10.1016/j.neuroimage.2017.10.003

    Article  Google Scholar 

  173. Dai, Z., de Souza, J., Lim, J., Ho, P.M., Chen, Y., Li, J., Thakor, N., Bezerianos, A., Sun, Y.: EEG cortical connectivity analysis of working memory reveals topological reorganization in Theta and Alpha Bands. Front. Hum. Neurosci. 11, 237 (2017). https://doi.org/10.3389/fnhum.2017.00237

    Article  Google Scholar 

  174. Mantini, D., Perrucci, M.G., Del Gratta, C., Romani, G.L., Corbetta, M.: Electrophysiological signatures of resting state networks in the human brain. Proc. Natl. Acad. Sci. U. S. A. 104(32), 13170–13175 (2007). https://doi.org/10.1073/pnas.0700668104

    Article  Google Scholar 

  175. Cohen, M.X., van Gaal, S.: Dynamic interactions between large-scale brain networks predict behavioral adaptation after perceptual errors. Cereb. Cortex. 23(5), 1061–1072 (2013). https://doi.org/10.1093/cercor/bhs069

    Article  Google Scholar 

  176. Schafer, C.B., Morgan, B.R., Ye, A.X., Taylor, M.J., Doesburg, S.M.: Oscillations, networks, and their development: MEG connectivity changes with age. Hum. Brain Mapp. 35(10), 5249–5261 (2014). https://doi.org/10.1002/hbm.22547

    Article  Google Scholar 

  177. Sakakibara, E., Homae, F., Kawasaki, S., Nishimura, Y., Takizawa, R., Koike, S., Kinoshita, A., Sakurada, H., Yamagishi, M., Nishimura, F., Yoshikawa, A., Inai, A., Nishioka, M., Eriguchi, Y., Matsuoka, J., Satomura, Y., Okada, N., Kakiuchi, C., Araki, T., Kan, C., Umeda, M., Shimazu, A., Uga, M., Dan, I., Hashimoto, H., Kawakami, N., Kasai, K.: Detection of resting state functional connectivity using partial correlation analysis: a study using multi-distance and whole-head probe near-infrared spectroscopy. NeuroImage. 142, 590–601 (2016). https://doi.org/10.1016/j.neuroimage.2016.08.011

    Article  Google Scholar 

  178. Alamian, G., Hincapie, A.S., Pascarella, A., Thiery, T., Combrisson, E., Saive, A.L., Martel, V., Althukov, D., Haesebaert, F., Jerbi, K.: Measuring alterations in oscillatory brain networks in schizophrenia with resting-state MEG: state-of-the-art and methodological challenges. Clin. Neurophysiol. 128(9), 1719–1736 (2017). https://doi.org/10.1016/j.clinph.2017.06.246

    Article  Google Scholar 

  179. Cha, Y.H., Gleghorn, D., Doudican, B.: Occipital and cerebellar theta burst stimulation for Mal De debarquement syndrome. Otol Neurotol. 40(9), e928–e937 (2019). https://doi.org/10.1097/MAO.0000000000002341

    Article  Google Scholar 

  180. Tong, S.N.T.: Quantitative EEG Analysis Methods and Clinical Applications. Artech House (2009)

    Google Scholar 

  181. Noachtar, S., Remi, J.: The role of EEG in epilepsy: a critical review. Epilepsy Behav. 15(1), 22–33 (2009). https://doi.org/10.1016/j.yebeh.2009.02.035

    Article  Google Scholar 

  182. Fox, K.C.R., Foster, B.L., Kucyi, A., Daitch, A.L., Parvizi, J.: Intracranial electrophysiology of the human default network. Trends Cogn. Sci. 22(4), 307–324 (2018). https://doi.org/10.1016/j.tics.2018.02.002

    Article  Google Scholar 

  183. Ortega, G.J., Sola, R.G., Pastor, J.: Complex network analysis of human ECoG data. Neurosci. Lett. 447(2-3), 129–133 (2008). https://doi.org/10.1016/j.neulet.2008.09.080

    Article  Google Scholar 

  184. Hill, N.J., Gupta, D., Brunner, P., Gunduz, A., Adamo, M.A., Ritaccio, A., Schalk, G.: Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping. J. Vis. Exp. 64 (2012). https://doi.org/10.3791/3993

  185. Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S.: Functional connectivity: the principal-component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13(1), 5–14 (1993). https://doi.org/10.1038/jcbfm.1993.4

    Article  Google Scholar 

  186. Huang, C., Mattis, P., Tang, C., Perrine, K., Carbon, M., Eidelberg, D.: Metabolic brain networks associated with cognitive function in Parkinson’s disease. NeuroImage. 34(2), 714–723 (2007). https://doi.org/10.1016/j.neuroimage.2006.09.003

    Article  Google Scholar 

  187. White, B.R., Liao, S.M., Ferradal, S.L., Inder, T.E., Culver, J.P.: Bedside optical imaging of occipital resting-state functional connectivity in neonates. NeuroImage. 59(3), 2529–2538 (2012). https://doi.org/10.1016/j.neuroimage.2011.08.094

    Article  Google Scholar 

  188. Kuang, L., Zhao, D., Xing, J., Chen, Z., Xiong, F., Han, X.: Metabolic brain network analysis of FDG-PET in Alzheimer’s Disease using Kernel-based persistent features. Molecules. 24(12) (2019). https://doi.org/10.3390/molecules24122301

  189. Obrig, H., Villringer, A.: Beyond the visible–imaging the human brain with light. J. Cereb. Blood Flow Metab. 23(1), 1–18 (2003). https://doi.org/10.1097/01.WCB.0000043472.45775.29

    Article  Google Scholar 

  190. Scholkmann, F., Kleiser, S., Metz, A.J., Zimmermann, R., Mata Pavia, J., Wolf, U., Wolf, M.: A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage. 85(Pt 1), 6–27 (2014). https://doi.org/10.1016/j.neuroimage.2013.05.004

    Article  Google Scholar 

  191. Lu, C.M., Zhang, Y.J., Biswal, B.B., Zang, Y.F., Peng, D.L., Zhu, C.Z.: Use of fNIRS to assess resting state functional connectivity. J. Neurosci. Methods. 186(2), 242–249 (2010). https://doi.org/10.1016/j.jneumeth.2009.11.010

    Article  Google Scholar 

  192. Mesquita, R.C., Franceschini, M.A., Boas, D.A.: Resting state functional connectivity of the whole head with near-infrared spectroscopy. Biomed Opt Express. 1(1), 324–336 (2010). https://doi.org/10.1364/BOE.1.000324

    Article  Google Scholar 

  193. Obrig, H., Neufang, M., Wenzel, R., Kohl, M., Steinbrink, J., Einhaupl, K., Villringer, A.: Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults. NeuroImage. 12(6), 623–639 (2000). https://doi.org/10.1006/nimg.2000.0657

    Article  Google Scholar 

  194. Sasai, S., Homae, F., Watanabe, H., Taga, G.: Frequency-specific functional connectivity in the brain during resting state revealed by NIRS. NeuroImage. 56(1), 252–257 (2011). https://doi.org/10.1016/j.neuroimage.2010.12.075

    Article  Google Scholar 

  195. White, B.R., Snyder, A.Z., Cohen, A.L., Petersen, S.E., Raichle, M.E., Schlaggar, B.L., Culver, J.P.: Resting-state functional connectivity in the human brain revealed with diffuse optical tomography. NeuroImage. 47(1), 148–156 (2009). https://doi.org/10.1016/j.neuroimage.2009.03.058

    Article  Google Scholar 

  196. Zhang, H., Zhang, Y.J., Lu, C.M., Ma, S.Y., Zang, Y.F., Zhu, C.Z.: Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements. NeuroImage. 51(3), 1150–1161 (2010). https://doi.org/10.1016/j.neuroimage.2010.02.080

    Article  Google Scholar 

  197. Homae, F., Watanabe, H., Otobe, T., Nakano, T., Go, T., Konishi, Y., Taga, G.: Development of global cortical networks in early infancy. J. Neurosci. 30(14), 4877–4882 (2010). https://doi.org/10.1523/JNEUROSCI.5618-09.2010

    Article  Google Scholar 

  198. Molavi, B., May, L., Gervain, J., Carreiras, M., Werker, J.F., Dumont, G.A.: Analyzing the resting state functional connectivity in the human language system using near infrared spectroscopy. Front. Hum. Neurosci. 7, 921 (2013). https://doi.org/10.3389/fnhum.2013.00921

    Article  Google Scholar 

  199. Watanabe, H., Shitara, Y., Aoki, Y., Inoue, T., Tsuchida, S., Takahashi, N., Taga, G.: Hemoglobin phase of oxygenation and deoxygenation in early brain development measured using fNIRS. Proc. Natl. Acad. Sci. U. S. A. 114(9), E1737–E1744 (2017). https://doi.org/10.1073/pnas.1616866114

    Article  Google Scholar 

  200. Irani, F., Platek, S.M., Bunce, S., Ruocco, A.C., Chute, D.: Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders. Clin. Neuropsychol. 21(1), 9–37 (2007). https://doi.org/10.1080/13854040600910018

    Article  Google Scholar 

  201. Horwitz, B., Poeppel, D.: How can EEG/MEG and fMRI/PET data be combined? Hum. Brain Mapp. 17(1), 1–3 (2002). https://doi.org/10.1002/hbm.10057

    Article  Google Scholar 

  202. Nicholson, A.A., Rabellino, D., Densmore, M., Frewen, P.A., Paret, C., Kluetsch, R., Schmahl, C., Theberge, J., Neufeld, R.W., McKinnon, M.C., Reiss, J., Jetly, R., Lanius, R.A.: The neurobiology of emotion regulation in posttraumatic stress disorder: Amygdala downregulation via real-time fMRI neurofeedback. Hum. Brain Mapp. 38(1), 541–560 (2017). https://doi.org/10.1002/hbm.23402

    Article  Google Scholar 

  203. Keynan, J.N., Meir-Hasson, Y., Gilam, G., Cohen, A., Jackont, G., Kinreich, S., Ikar, L., Or-Borichev, A., Etkin, A., Gyurak, A., Klovatch, I., Intrator, N., Hendler, T.: Limbic activity modulation guided by functional magnetic resonance imaging-inspired electroencephalography improves implicit emotion regulation. Biol. Psychiatry. 80(6), 490–496 (2016). https://doi.org/10.1016/j.biopsych.2015.12.024

    Article  Google Scholar 

  204. Keynan, J.N., Cohen, A., Jackont, G., Green, N., Goldway, N., Davidov, A., Meir-Hasson, Y., Raz, G., Intrator, N., Fruchter, E., Ginat, K., Laska, E., Cavazza, M., Hendler, T.: Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience. Nat. Hum. Behav. 3(1), 63–73 (2019). https://doi.org/10.1038/s41562-018-0484-3

    Article  Google Scholar 

  205. Wirsich, J., Giraud, A.L., Sadaghiani, S.: Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics. NeuroImage, 116998 (2020). https://doi.org/10.1016/j.neuroimage.2020.116998

  206. Goldman, R.I., Stern, J.M., Engel Jr., J., Cohen, M.S.: Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport. 13(18), 2487–2492 (2002). https://doi.org/10.1097/01.wnr.0000047685.08940.d0

    Article  Google Scholar 

  207. Goncalves, S.I., de Munck, J.C., Pouwels, P.J., Schoonhoven, R., Kuijer, J.P., Maurits, N.M., Hoogduin, J.M., Van Someren, E.J., Heethaar, R.M., Lopes da Silva, F.H.: Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: inter-subject variability. NeuroImage. 30(1), 203–213 (2006). https://doi.org/10.1016/j.neuroimage.2005.09.062

    Article  Google Scholar 

  208. Sadaghiani, S., Scheeringa, R., Lehongre, K., Morillon, B., Giraud, A.L., Kleinschmidt, A.: Intrinsic connectivity networks, alpha oscillations, and tonic alertness: a simultaneous electroencephalography/functional magnetic resonance imaging study. J. Neurosci. 30(30), 10243–10250 (2010). https://doi.org/10.1523/JNEUROSCI.1004-10.2010

    Article  Google Scholar 

  209. Yuan, H., Zotev, V., Phillips, R., Bodurka, J.: Correlated slow fluctuations in respiration, EEG, and BOLD fMRI. NeuroImage. 79, 81–93 (2013). https://doi.org/10.1016/j.neuroimage.2013.04.068

    Article  Google Scholar 

  210. Custo, A., Van De Ville, D., Wells, W.M., Tomescu, M.I., Brunet, D., Michel, C.M.: Electroencephalographic resting-state networks: source localization of microstates. Brain Connect. 7(10), 671–682 (2017). https://doi.org/10.1089/brain.2016.0476

    Article  Google Scholar 

  211. Bisdorff, A., Von Brevern, M., Lempert, T., Newman-Toker, D.E.: Classification of vestibular symptoms: towards an international classification of vestibular disorders. J. Vestib. Res. 19(1-2), 1–13 (2009). https://doi.org/10.3233/VES-2009-0343

    Article  Google Scholar 

  212. Cha, Y.H., Deblieck, C., Wu, A.D.: Double-Blind Sham-controlled crossover trial of repetitive transcranial magnetic stimulation for Mal de Debarquement Syndrome. Otol Neurotol. 37(6), 805–812 (2016). https://doi.org/10.1097/MAO.0000000000001045

    Article  Google Scholar 

  213. Nunez, P.L.: Neocortical Dynamics and Human EEG Rhythms. Oxford University Press, Oxford (1995)

    Google Scholar 

  214. Winter, W.R., Nunez, P.L., Ding, J., Srinivasan, R.: Comparison of the effect of volume conduction on EEG coherence with the effect of field spread on MEG coherence. Stat. Med. 26(21), 3946–3957 (2007). https://doi.org/10.1002/sim.2978

    Article  MathSciNet  Google Scholar 

  215. Cohen, D., Cuffin, B.N.: A method for combining MEG and EEG to determine the sources. Phys. Med. Biol. 32(1), 85–89 (1987). https://doi.org/10.1088/0031-9155/32/1/013

    Article  Google Scholar 

  216. Huizenga, H.M., van Zuijen, T.L., Heslenfeld, D.J., Molenaar, P.C.: Simultaneous MEG and EEG source analysis. Phys. Med. Biol. 46(7), 1737–1751 (2001). https://doi.org/10.1088/0031-9155/46/7/301

    Article  Google Scholar 

  217. Molins, A., Stufflebeam, S.M., Brown, E.N., Hamalainen, M.S.: Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation. NeuroImage. 42(3), 1069–1077 (2008). https://doi.org/10.1016/j.neuroimage.2008.05.064

    Article  Google Scholar 

  218. Marquetand, J., Vannoni, S., Carboni, M., Li Hegner, Y., Stier, C., Braun, C., Focke, N.K.: Reliability of magnetoencephalography and high-density electroencephalography resting-state functional connectivity metrics. Brain Connect. 9(7), 539–553 (2019). https://doi.org/10.1089/brain.2019.0662

    Article  Google Scholar 

  219. Muthuraman, M., Moliadze, V., Mideksa, K.G., Anwar, A.R., Stephani, U., Deuschl, G., Freitag, C.M., Siniatchkin, M.: EEG-MEG integration enhances the characterization of functional and effective connectivity in the resting state network. PLoS One. 10(10), e0140832 (2015). https://doi.org/10.1371/journal.pone.0140832

    Article  Google Scholar 

  220. Muthuraman, M., Hellriegel, H., Hoogenboom, N., Anwar, A.R., Mideksa, K.G., Krause, H., Schnitzler, A., Deuschl, G., Raethjen, J.: Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements. PLoS One. 9(3), e91441 (2014). https://doi.org/10.1371/journal.pone.0091441

    Article  Google Scholar 

  221. Pizzo, F., Roehri, N., Medina Villalon, S., Trebuchon, A., Chen, S., Lagarde, S., Carron, R., Gavaret, M., Giusiano, B., McGonigal, A., Bartolomei, F., Badier, J.M., Benar, C.G.: Deep brain activities can be detected with magnetoencephalography. Nat. Commun. 10(1), 971 (2019). https://doi.org/10.1038/s41467-019-08665-5

    Article  Google Scholar 

  222. Catana, C., Drzezga, A., Heiss, W.D., Rosen, B.R.: PET/MRI for neurologic applications. J. Nucl. Med. 53(12), 1916–1925 (2012). https://doi.org/10.2967/jnumed.112.105346

    Article  Google Scholar 

  223. Wehrl, H.F., Hossain, M., Lankes, K., Liu, C.C., Bezrukov, I., Martirosian, P., Schick, F., Reischl, G., Pichler, B.J.: Simultaneous PET-MRI reveals brain function in activated and resting state on metabolic, hemodynamic and multiple temporal scales. Nat. Med. 19(9), 1184–1189 (2013). https://doi.org/10.1038/nm.3290

    Article  Google Scholar 

  224. Savio, A., Funger, S., Tahmasian, M., Rachakonda, S., Manoliu, A., Sorg, C., Grimmer, T., Calhoun, V., Drzezga, A., Riedl, V., Yakushev, I.: Resting-state networks as simultaneously measured with functional MRI and PET. J. Nucl. Med. 58(8), 1314–1317 (2017). https://doi.org/10.2967/jnumed.116.185835

    Article  Google Scholar 

  225. Yokoi, T., Watanabe, H., Yamaguchi, H., Bagarinao, E., Masuda, M., Imai, K., Ogura, A., Ohdake, R., Kawabata, K., Hara, K., Riku, Y., Ishigaki, S., Katsuno, M., Miyao, S., Kato, K., Naganawa, S., Harada, R., Okamura, N., Yanai, K., Yoshida, M., Sobue, G.: Involvement of the precuneus/posterior cingulate cortex is significant for the development of Alzheimer’s disease: a PET (THK5351, PiB) and resting fMRI study. Front. Aging Neurosci. 10, 304 (2018). https://doi.org/10.3389/fnagi.2018.00304

    Article  Google Scholar 

  226. Kim, J.H., Joo, Y.H., Son, Y.D., Kim, J.H., Kim, Y.K., Kim, H.K., Lee, S.Y., Ido, T.: In vivo metabotropic glutamate receptor 5 availability-associated functional connectivity alterations in drug-naive young adults with major depression. Eur. Neuropsychopharmacol. 29(2), 278–290 (2019). https://doi.org/10.1016/j.euroneuro.2018.12.001

    Article  Google Scholar 

  227. Rajkumar, R., Régio Brambilla, C., Veselinović, T., Bierbrier, J., Wyss, C., Ramkiran, S., Orth, L., Lang, M., Kops, E.R., Mauler, J., Scheins, J., Neumaier, B., Ermert, J., Herzog, H., Langen, K.-J., Binkofski, F.C., Lerche, C., Shah, N.J., Neuner, I.: Excitatory-inhibitory balance within EEG microstates and resting-state fMRI networks: assessed via simultaneous PET-MR-EEG imaging. Transl Psychiatry. 11(60) (2020). https://doi.org/10.1101/20200522109413

  228. von Ellenrieder, N., Muravchik, C.H., Wagner, M., Nehorai, A.: Effect of head shape variations among individuals on the EEG/MEG forward and inverse problems. IEEE Trans. Biomed. Eng. 56(3), 587–597 (2009). https://doi.org/10.1109/TBME.2009.2008445

    Article  Google Scholar 

  229. Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., Woods, R., Paus, T., Simpson, G., Pike, B., Holmes, C., Collins, L., Thompson, P., MacDonald, D., Iacoboni, M., Schormann, T., Amunts, K., Palomero-Gallagher, N., Geyer, S., Parsons, L., Narr, K., Kabani, N., Le Goualher, G., Feidler, J., Smith, K., Boomsma, D., Hulshoff Pol, H., Cannon, T., Kawashima, R., Mazoyer, B.: A four-dimensional probabilistic atlas of the human brain. J. Am. Med. Inform. Assoc. 8(5), 401–430 (2001). https://doi.org/10.1136/jamia.2001.0080401

    Article  Google Scholar 

  230. Le Bihan, D.: Looking into the functional architecture of the brain with diffusion MRI. Nat. Rev. Neurosci. 4(6), 469–480 (2003). https://doi.org/10.1038/nrn1119

    Article  Google Scholar 

  231. Assaf, Y., Pasternak, O.: Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J. Mol. Neurosci. 34(1), 51–61 (2008). https://doi.org/10.1007/s12031-007-0029-0

    Article  Google Scholar 

  232. Damoiseaux, J.S., Greicius, M.D.: Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity. Brain Struct. Funct. 213(6), 525–533 (2009). https://doi.org/10.1007/s00429-009-0208-6

    Article  Google Scholar 

  233. Suarez, L.E., Markello, R.D., Betzel, R.F., Misic, B.: Linking structure and function in macroscale brain networks. Trends Cogn. Sci. 24(4), 302–315 (2020). https://doi.org/10.1016/j.tics.2020.01.008

    Article  Google Scholar 

  234. Straathof, M., Sinke, M.R., Dijkhuizen, R.M., Otte, W.M.: A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains. J. Cereb. Blood Flow Metab. 39(2), 189–209 (2019). https://doi.org/10.1177/0271678X18809547

    Article  Google Scholar 

  235. Greicius, M.D., Supekar, K., Menon, V., Dougherty, R.F.: Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex. 19(1), 72–78 (2009). https://doi.org/10.1093/cercor/bhn059

    Article  Google Scholar 

  236. Khalsa, S., Mayhew, S.D., Chechlacz, M., Bagary, M., Bagshaw, A.P.: The structural and functional connectivity of the posterior cingulate cortex: comparison between deterministic and probabilistic tractography for the investigation of structure-function relationships. NeuroImage. 102(Pt 1), 118–127 (2014). https://doi.org/10.1016/j.neuroimage.2013.12.022

    Article  Google Scholar 

  237. van den Heuvel, M.P., Mandl, R.C., Kahn, R.S., Hulshoff Pol, H.E.: Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum. Brain Mapp. 30(10), 3127–3141 (2009). https://doi.org/10.1002/hbm.20737

    Article  Google Scholar 

  238. Lowe, M.J., Beall, E.B., Sakaie, K.E., Koenig, K.A., Stone, L., Marrie, R.A., Phillips, M.D.: Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity. Hum. Brain Mapp. 29(7), 818–827 (2008). https://doi.org/10.1002/hbm.20576

    Article  Google Scholar 

  239. Honey, C.J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J.P., Meuli, R., Hagmann, P.: Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. U. S. A. 106(6), 2035–2040 (2009). https://doi.org/10.1073/pnas.0811168106

    Article  Google Scholar 

  240. Skudlarski, P., Jagannathan, K., Calhoun, V.D., Hampson, M., Skudlarska, B.A., Pearlson, G.: Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations. NeuroImage. 43(3), 554–561 (2008). https://doi.org/10.1016/j.neuroimage.2008.07.063

    Article  Google Scholar 

  241. Sundgren, P.C., Dong, Q., Gomez-Hassan, D., Mukherji, S.K., Maly, P., Welsh, R.: Diffusion tensor imaging of the brain: review of clinical applications. Neuroradiology. 46(5), 339–350 (2004). https://doi.org/10.1007/s00234-003-1114-x

    Article  Google Scholar 

  242. Abdelnour, F., Dayan, M., Devinsky, O., Thesen, T., Raj, A.: Functional brain connectivity is predictable from anatomic network’s Laplacian eigen-structure. NeuroImage. 172, 728–739 (2018). https://doi.org/10.1016/j.neuroimage.2018.02.016

    Article  Google Scholar 

  243. Mechelli, A., Friston, K.J., Frackowiak, R.S., Price, C.J.: Structural covariance in the human cortex. J. Neurosci. 25(36), 8303–8310 (2005). https://doi.org/10.1523/JNEUROSCI.0357-05.2005

    Article  Google Scholar 

  244. Mayeli, A., Zotev, V., Refai, H., Bodurka, J.: Real-time EEG artifact correction during fMRI using ICA. J. Neurosci. Methods. 274, 27–37 (2016). https://doi.org/10.1016/j.jneumeth.2016.09.012

    Article  Google Scholar 

  245. Rosa, M.J., Daunizeau, J., Friston, K.J.: EEG-fMRI integration: a critical review of biophysical modeling and data analysis approaches. J. Integr. Neurosci. 9(4), 453–476 (2010). https://doi.org/10.1142/s0219635210002512

    Article  Google Scholar 

  246. Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., Calhoun, V.D.: Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex. 24(3), 663–676 (2014). https://doi.org/10.1093/cercor/bhs352

    Article  Google Scholar 

  247. Keilholz, S., Caballero-Gaudes, C., Bandettini, P., Deco, G., Calhoun, V.: Time-resolved resting-state functional magnetic resonance imaging analysis: current status, challenges, and new directions. Brain Connect. 7(8), 465–481 (2017). https://doi.org/10.1089/brain.2017.0543

    Article  Google Scholar 

  248. O’Neill, G.C., Tewarie, P., Vidaurre, D., Liuzzi, L., Woolrich, M.W., Brookes, M.J.: Dynamics of large-scale electrophysiological networks: a technical review. NeuroImage. (2017). https://doi.org/10.1016/j.neuroimage.2017.10.003

  249. Iraji, A., Deramus, T.P., Lewis, N., Yaesoubi, M., Stephen, J.M., Erhardt, E., Belger, A., Ford, J.M., McEwen, S., Mathalon, D.H., Mueller, B.A., Pearlson, G.D., Potkin, S.G., Preda, A., Turner, J.A., Vaidya, J.G., van Erp, T.G.M., Calhoun, V.D.: The spatial chronnectome reveals a dynamic interplay between functional segregation and integration. Hum. Brain Mapp. 40(10), 3058–3077 (2019). https://doi.org/10.1002/hbm.24580

    Article  Google Scholar 

  250. Chen, H., Nomi, J.S., Uddin, L.Q., Duan, X.J., Chen, H.F.: Intrinsic functional connectivity variance and state-specific under-connectivity in autism. Hum. Brain Mapp. 38(11), 5740–5755 (2017). https://doi.org/10.1002/hbm.23764

    Article  Google Scholar 

  251. Jin, C., Jia, H., Lanka, P., Rangaprakash, D., Li, L., Liu, T., Hu, X., Deshpande, G.: Dynamic brain connectivity is a better predictor of PTSD than static connectivity. Hum. Brain Mapp. 38(9), 4479–4496 (2017). https://doi.org/10.1002/hbm.23676

    Article  Google Scholar 

  252. Spiers, H.J., Maguire, E.A.: Decoding human brain activity during real-world experiences. Trends Cogn. Sci. 11(8), 356–365 (2007). https://doi.org/10.1016/j.tics.2007.06.002

    Article  Google Scholar 

  253. Mayr, N.P., Martin, K., Kurz, J., Tassani, P.: Monitoring of cerebral oxygen saturation during closed-chest and open-chest CPR. Resuscitation. 82(5), 635–636 (2011). https://doi.org/10.1016/j.resuscitation.2011.01.017

    Article  Google Scholar 

  254. Boto, E., Seedat, Z.A., Holmes, N., Leggett, J., Hill, R.M., Roberts, G., Shah, V., Fromhold, T.M., Mullinger, K.J., Tierney, T.M., Barnes, G.R., Bowtell, R., Brookes, M.J.: Wearable neuroimaging: combining and contrasting magnetoencephalography and electroencephalography. NeuroImage. 201, 116099 (2019). https://doi.org/10.1016/j.neuroimage.2019.116099

    Article  Google Scholar 

  255. Roberts, G., Holmes, N., Alexander, N., Boto, E., Leggett, J., Hill, R.M., Shah, V., Rea, M., Vaughan, R., Maguire, E.A., Kessler, K., Beebe, S., Fromhold, M., Barnes, G.R., Bowtell, R., Brookes, M.J.: Towards OPM-MEG in a virtual reality environment. NeuroImage. 199, 408–417 (2019). https://doi.org/10.1016/j.neuroimage.2019.06.010

    Article  Google Scholar 

  256. Besio, W.G., Koka, K., Aakula, R., Dai, W.: Tri-polar concentric ring electrode development for laplacian electroencephalography. IEEE Trans. Biomed. Eng. 53(5), 926–933 (2006). https://doi.org/10.1109/TBME.2005.863887

    Article  Google Scholar 

  257. Pedroni, A., Bahreini, A., Langer, N.: Automagic: standardized preprocessing of big EEG data. NeuroImage. 200, 460–473 (2019). https://doi.org/10.1016/j.neuroimage.2019.06.046

    Article  Google Scholar 

  258. Zhang, S., Hennig, J., LeVan, P.: Direct modelling of gradient artifacts for EEG-fMRI denoising and motion tracking. J. Neural Eng. 16(5), 056010 (2019). https://doi.org/10.1088/1741-2552/ab2b21

    Article  Google Scholar 

  259. Nicholson, A.A., Densmore, M., Frewen, P.A., Theberge, J., Neufeld, R.W., McKinnon, M.C., Lanius, R.A.: The dissociative subtype of posttraumatic stress disorder: unique resting-state functional connectivity of basolateral and centromedial Amygdala complexes. Neuropsychopharmacology. 40(10), 2317–2326 (2015). https://doi.org/10.1038/npp.2015.79

    Article  Google Scholar 

  260. Maron-Katz, A., Zhang, Y., Narayan, M., Wu, W., Toll, R.T., Naparstek, S., De Los, A.C., Longwell, P., Shpigel, E., Newman, J., Abu-Amara, D., Marmar, C., Etkin, A.: Individual patterns of abnormality in resting-state functional connectivity reveal two data-driven PTSD subgroups. Am. J. Psychiatry. 177(3), 244–253 (2020). https://doi.org/10.1176/appi.ajp.2019.19010060

    Article  Google Scholar 

  261. Gonzalez-Castillo, J., Saad, Z.S., Handwerker, D.A., Inati, S.J., Brenowitz, N., Bandettini, P.A.: Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis. Proc. Natl. Acad. Sci. U. S. A. 109(14), 5487–5492 (2012). https://doi.org/10.1073/pnas.1121049109

    Article  Google Scholar 

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Acknowledgments

We gratefully acknowledge the financial support from NSF RII Track-2 FEC 1539068 and NSF CAREER ECCS-0955260.

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Authors and Affiliations

  1. Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA

    Guofa Shou

  2. Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA

    Han Yuan & Lei Ding

  3. Institute of Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, OK, USA

    Han Yuan & Lei Ding

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  1. Guofa Shou
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  2. Han Yuan
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  3. Lei Ding
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Correspondence to Lei Ding .

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Editors and Affiliations

  1. Neuroengineering and Biomedical Instrumentation Laboratory Department of Biomedical Engineering and Department of Electrical and Computer Engineering, Neurology, Johns Hopkins University, Baltimore, MD, USA

    Nitish V. Thakor

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Shou, G., Yuan, H., Ding, L. (2023). Mapping Brain Networks Using Multimodal Data. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-5540-1_83

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