Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-19T04:59:21.165Z Has data issue: false hasContentIssue false
  • English
  • Français

Modern Methods for Interrogating the Human Connectome

Published online by Cambridge University Press:  18 February 2016

Mark J. Lowe ,
Ken E. Sakaie ,
Erik B. Beall ,
Vince D. Calhoun ,
David A. Bridwell ,
Mikail Rubinov  and
Stephen M. Rao Open the ORCID record for Stephen M. Rao [Opens in a new window]
Mark J. Lowe
Affiliation:
Imaging Institute, Cleveland Clinic, Cleveland, Ohio
Ken E. Sakaie
Affiliation:
Imaging Institute, Cleveland Clinic, Cleveland, Ohio
Erik B. Beall
Affiliation:
Imaging Institute, Cleveland Clinic, Cleveland, Ohio
Vince D. Calhoun
Affiliation:
The Mind Research Network, Albuquerque, New Mexico Department of ECE, University of New Mexico, Albuquerque, New Mexico
David A. Bridwell
Affiliation:
The Mind Research Network, Albuquerque, New Mexico Department of ECE, University of New Mexico, Albuquerque, New Mexico
Mikail Rubinov
Affiliation:
Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
Stephen M. Rao*
Affiliation:
Neurological Institute, Cleveland Clinic, Cleveland, Ohio
*
Correspondence and reprint requests to: Stephen M. Rao, Schey Center for Cognitive Neuroimaging, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue/U10, Cleveland, OH 44195. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Connectionist theories of brain function took hold with the seminal contributions of Norman Geschwind a half century ago. Modern neuroimaging techniques have expanded the scientific interest in the study of brain connectivity to include the intact as well as disordered brain. Methods: In this review, we describe the most common techniques used to measure functional and structural connectivity, including resting state functional MRI, diffusion MRI, and electroencephalography and magnetoencephalography coherence. We also review the most common analytical approaches used for examining brain interconnectivity associated with these various imaging methods. Results: This review presents a critical analysis of the assumptions, as well as methodological limitations, of each imaging and analysis approach. Conclusions: The overall goal of this review is to provide the reader with an introduction to evaluating the scientific methods underlying investigations that probe the human connectome. (JINS, 2016, 22, 105–119)

Keywords

Human connectomeResting state fMRIDiffusion MRIEEG/MEG coherenceComplex network analysisIndependent components analysis
Type
Critical Reviews
Information
Journal of the International Neuropsychological Society , Volume 22 , Special Issue 2: Human Brain Connectivity in the Modern Era: Relevance to Understanding Health and Disease , February 2016 , pp. 105 - 119
Copyright
Copyright © The International Neuropsychological Society 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abou-Elseoud, A., Starck, T., Remes, J., Nikkinen, J., Tervonen, O., & Kiviniemi, V. (2010). The effect of model order selection in group PICA. Human Brain Mapping, 31, 1207–1216.CrossRefGoogle ScholarPubMed
Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. The Journal of Neuroscience, 26(1), 63–72.CrossRefGoogle ScholarPubMed
Alexander-Bloch, A., Giedd, J.N., & Bullmore, E. (2013). Imaging structural co-variance between human brain regions. [Review]. Nature Reviews Neuroscience, 14(5), 322–336.CrossRefGoogle ScholarPubMed
Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., & Calhoun, V.D. (2014). Tracking Whole-brain connectivity dynamics in the resting state. Cerebral Cortex, 24(3), 663–676.Google Scholar
Allen, E.A., Erhardt, E.B., Damaraju, E., Gruner, W., Segall, J.M., Silva, R.F., & Calhoun, V.D. (2011). A baseline for the multivariate comparison of resting-state networks. Frontiers in Systems Neuroscience, 5, 2.Google Scholar
Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M.-É., & Evans, A.C. (2013). BigBrain: An ultrahigh-resolution 3D human brain model. Science, 340(6139), 1472–1475.Google Scholar
Assaf, Y., Blumenfeld-Katzir, T., Yovel, Y., & Basser, P.J. (2008). AxCaliber: A method for measuring axon diameter distribution from diffusion MRI. Magnetic Resonance in Medicine, 59(6), 1347–1354.CrossRefGoogle ScholarPubMed
Assemlal, H.E., Tschumperle, D., Brun, L., & Siddiqi, K. (2011). Recent advances in diffusion MRI modeling: Angular and radial reconstruction. Medical Image Analysis, 15(4), 369–396.CrossRefGoogle ScholarPubMed
Barazany, D., Basser, P.J., & Assaf, Y. (2009). In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. Brain, 132(Pt 5), 1210–1220.Google Scholar
Basser, P.J., Mattiello, J., & LeBihan, D. (1994). MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1), 259–267.Google Scholar
Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., & Aldroubi, A. (2000). In vivo fiber tractography using DT-MRI data. Magnetic Resonance in Medicine, 44(4), 625–632.Google Scholar
Bassett, D.S., & Bullmore, E. (2006). Small-world brain networks. Neuroscientist, 12(6), 512–523.Google Scholar
Bassett, D.S., Meyer-Lindenberg, A., Achard, S., Duke, T., & Bullmore, E. (2006). Adaptive reconfiguration of fractal small-world human brain functional networks. Proceedings of the National Academy of Sciences of the United States of America, 103(51), 19518–19523.Google Scholar
Beall, E.B., & Lowe, M.J. (2007). Isolating physiologic noise sources with independently determined spatial measures. Neuroimage, 37(4), 1286–1300.Google Scholar
Beall, E.B., & Lowe, M.J. (2010). The non-separability of physiologic noise in functional connectivity MRI with spatial ICA at 3T. Journal of Neuroscience Methods, 191(2), 263–276.Google Scholar
Beall, E.B., & Lowe, M.J. (2014). SimPACE: Generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction. Neuroimage, 101, 21–34.Google Scholar
Beckmann, C.F., & Smith, S.M. (2005). Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage, 25(1), 294–311.Google Scholar
Behrens, T.E., Berg, H.J., Jbabdi, S., Rushworth, M.F., & Woolrich, M.W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage, 34(1), 144–155.Google Scholar
Behrens, T.E., & Sporns, O. (2012). Human connectomics. Current Opinion in Neurobiology, 22(1), 144–153.Google Scholar
Bendat, J.S., & Piersol, A.G. (2000). Random data. Analysis and measurement procedures (3rd ed.). New York: John Wiley & Sons.Google Scholar
Biswal, B.B., & Hudetz, A.G. (1996). Synchronous oscillations in cerebrocortical capillary red blood cell velocity after nitric oxide synthase inhibition. Microvascular Research, 52(1), 1–12.Google Scholar
Biswal, B.B., Hudetz, A.G., Yetkin, F.Z., Haughton, V.M., & Hyde, J.S. (1997). Hypercapnia reversibly suppresses low-frequency fluctuations in the human motor cortex during rest using echo-planar MRI. Journal of Cerebral Blood Flow & Metabolism, 17(3), 301–308.CrossRefGoogle ScholarPubMed
Biswal, B.B., Van Kylen, J., & Hyde, J.S. (1997). Simultaneous assessment of flow and BOLD signals in resting-state. NMR in Biomedicine, 10(4-5), 165–170.Google Scholar
Biswal, B.B., Yetkin, F.Z., Haughton, V.M., & Hyde, J.S. (1995). Functional connectivity in the motor cortex of resting human brain. Magnetic Resonance in Medicine, 34(4), 537–541.Google Scholar
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D.U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4–5), 175–308.CrossRefGoogle Scholar
Boubela, R.N., Kalcher, K., Huf, W., Kronnerwetter, C., Filzmoser, P., & Moser, E. (2013). Beyond noise: Using temporal ICA to extract meaningful information from high-frequency fMRI signal fluctuations during rest. Frontiers in Human Neuroscience, 7, 168.CrossRefGoogle ScholarPubMed
Brookes, M.J., Hale, J.R., Zumer, J.M., Stevenson, C.M., Francis, S.T., Barnes, G.R., & Nagarajan, S.S. (2011). Measuring functional connectivity using MEG: Methodology and comparison with fcMRI. Neuroimage, 56(3), 1082–1104.Google Scholar
Budde, M.D., Kim, J.H., Liang, H.F., Schmidt, R.E., Russell, J.H., Cross, A.H., && Song, S.K. (2007). Toward accurate diagnosis of white matter pathology using diffusion tensor imaging. Magnetic Resonance in Medicine, 57(4), 688–695.Google Scholar
Bullmore, E.T., Brammer, M.J., Rabe-Hesketh, S., Curtis, V.A., Morris, R.G., Williams, S.C., & McGuire, P.K. (1999). Methods for diagnosis and treatment of stimulus-correlated motion in generic brain activation studies using fMRI. Human Brain Mapping, 7(1), 38–48.Google Scholar
Bullmore, E.T., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.Google Scholar
Buzsaki, G. (2006). Rhythms of the brain. New York: Oxford University Press.CrossRefGoogle Scholar
Calhoun, V.D., & Adali, T. (2012). Multi-subject independent component analysis of fMRI: A decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Reviews in Biomedical Engineering, 5, 60–72.Google Scholar
Calhoun, V.D., Adali, T., Pearlson, G.D., & Pekar, J.J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140–151.Google Scholar
Calhoun, V.D., & Allen, E. (2013). Extracting intrinsic functional networks with feature-based group independent component analysis. Psychometrika, 78(2), 243–259.Google Scholar
Calhoun, V.D., Kiehl, K.A., & Pearlson, G.D. (2008). Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Human Brain Mapping, 29(7), 828–838.CrossRefGoogle ScholarPubMed
Calhoun, V.D., Miller, R., Pearlson, G., & Adalı, T. (2014). The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262–274.Google Scholar
Catani, M., Howard, R.J., Pajevic, S., & Jones, D.K. (2002). Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage, 17(1), 77–94.Google Scholar
Chang, C., & Glover, G.H. (2010). Time–frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage, 50(1), 81–98.CrossRefGoogle ScholarPubMed
Cohen, D., & Cuffin, B.N. (1983). Demonstration of useful differences between magnetoencephalogram and electroencephalogram. Electroencephalography and Clinical Neurophysiology, 56(1), 38–51.Google Scholar
Cole, D.M., Smith, S.M., & Beckmann, C.F. (2010). Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers in Systems Neuroscience, 4, 8.Google ScholarPubMed
Colizza, V., Flammini, A., Serrano, M.A., & Vespignani, A. (2006). Detecting rich-club ordering in complex networks. [10.1038/nphys209]. Nature Physics, 2(2), 110–115.Google Scholar
Conturo, T.E., Lori, N.F., Cull, T.S., Akbudak, E., Snyder, A.Z., Shimony, J.S., & Raichle, M.E. (1999). Tracking neuronal fiber pathways in the living human brain. Proceedings of the National Academy of Sciences of the United States of America, 96(18), 10422–10427.CrossRefGoogle ScholarPubMed
Cooper, R., Crow, H.J., Walter, W.G., & Winter, A.L. (1966). Regional control of cerebral vascular reactivity and oxygen supply in man. Brain Research, 3(2), 174–191.Google Scholar
Cordes, D., Haughton, V.M., Arfanakis, K., Carew, J.D., Turski, P.A., Moritz, C.H., & Meyerand, M.E. (2001). Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR American Journal of Neuroradiology, 22(7), 1326–1333.Google Scholar
Cordes, D., Haughton, V.M., Arfanakis, K., Wendt, G.J., Turski, P.A., Moritz, C.H., & Meyerand, M.E. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR American Journal of Neuroradiology, 21(9), 1636–1644.Google Scholar
Cordes, D., Nandy, R.R., Schafer, S., & Wager, T.D. (2014). Characterization and reduction of cardiac- and respiratory-induced noise as a function of the sampling rate (TR) in fMRI. Neuroimage, 89, 314–330.CrossRefGoogle ScholarPubMed
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173.Google Scholar
Craddock, R.C., Jbabdi, S., Yan, C.-G., Vogelstein, J.T., Castellanos, F.X., Di Martino, A., & Milham, M.P. (2013). Imaging human connectomes at the macroscale. Nature Methods, 10(6), 524–539.Google Scholar
Crossley, N.A., Mechelli, A., Scott, J., Carletti, F., Fox, P.T., McGuire, P., && Bullmore, E.T. (2014). The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain, 137(Pt 8), 2382–2395.CrossRefGoogle ScholarPubMed
Csermely, P., London, A., Wu, L.-Y., & Uzzi, B. (2013). Structure and dynamics of core/periphery networks. Journal of Complex Networks, 1(2), 93–123.CrossRefGoogle Scholar
Damaraju, E., Allen, E.A., Belger, A., Ford, J.M., McEwen, S., Mathalon, D.H., & Calhoun, V.D. (2014). Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage. Clinical, 5, 298–308.Google Scholar
Dora, E., & Kovach, A.G. (1981). Metabolic and vascular volume oscillations in the cat brain cortex. Acta Physiology of the Academy of Science of Hungary, 57(3), 261–275.Google Scholar
Douaud, G., Jbabdi, S., Behrens, T.E., Menke, R.A., Gass, A., Monsch, A.U., & Smith, S. (2011). DTI measures in crossing-fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer’s disease. Neuroimage, 55(3), 880–890.CrossRefGoogle ScholarPubMed
Eguiluz, V.M., Chialvo, D.R., Cecchi, G.A., Baliki, M., & Apkarian, A.V. (2005). Scale-free brain functional networks. Physical Review Letters, 94(1), 018102.Google Scholar
Eichele, T., Calhoun, V.D., & Debener, S. (2009). Mining EEG–fMRI using independent component analysis. International Journal of Psychophysiology, 73(1), 53–61.Google Scholar
Engel, A.K., Fries, P., & Singer, W. (2001). Dynamic predictions: Oscillations and synchrony in top-down processing. Nature Reviews Neuroscience, 2, 704–716.Google Scholar
Erhardt, E.B., Rachakonda, S., Bedrick, E.J., Allen, E.A., Adali, T., & Calhoun, V.D. (2011). Comparison of multi-subject ICA methods for analysis of fMRI data. Human Brain Mapping, 32(12), 2075–2095.CrossRefGoogle ScholarPubMed
Esposito, F., Scarabino, T., Hyvarinen, A., Himberg, J., Formisano, E., Comani, S., & Salle, F. (2005). Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage, 25(1), 193–205.CrossRefGoogle ScholarPubMed
Evans, A.C. (2013). Networks of anatomical covariance. Neuroimage, 80, 489–504.CrossRefGoogle ScholarPubMed
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75–174.Google Scholar
Fox, M.D., & Raichle, M.E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8(9), 700–711.Google Scholar
Friston, K.J., Frith, C.D., Liddle, P.F., & Frackowiak, R.S. (1993). Functional connectivity: The principal-component analysis of large (PET) data sets. Journal of Cerebral Blood Flow & Metabolism, 13(1), 5–14.Google Scholar
Geschwind, N. (1965a). Disconnexion syndromes in animals and man. I. Brain, 88(2), 237–294.Google Scholar
Geschwind, N. (1965b). Disconnexion syndromes in animals and man. II. Brain, 88(3), 585–644.Google Scholar
Girvan, M., & Newman, M.E.J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821–7826.Google Scholar
Glover, G.H., Li, T.Q., & Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44(1), 162–167.Google Scholar
Golanov, E.V., Yamamoto, S., & Resi, D.J. (1994). Spontaneous waves of cerebral blood flow associated with patterns of electrocortical activity. American Journal of Physiology, 266, R204–R214.Google ScholarPubMed
Gong, G., He, Y., Concha, L., Lebel, C., Gross, D.W., Evans, A.C., &&Beaulieu, C. (2009). Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cerebral Cortex, 19(3), 524–536.Google Scholar
Greicius, M.D., Krasnow, B., Reiss, A.L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 100(1), 253–258.Google Scholar
Guo, Y., & Pagnoni, G. (2008). A unified framework for group independent component analysis for multi-subject fMRI data. Neuroimage, 42(3), 1078–1093.Google Scholar
Gusnard, D.A., Akbudak, E., Shulman, G.L., & Raichle, M.E. (2001). Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(7), 4259–4264.CrossRefGoogle ScholarPubMed
Hagmann, P., Cammoun, L., Gigandet, X., Gerhard, S., Grant, P.E., Wedeen, V.J., & Sporns, O. (2010). MR connectomics: Principles and challenges. Journal of Neuroscience Methods, 194(1), 34–45.CrossRefGoogle ScholarPubMed
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C.J., Wedeen, V.J., && Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6(7), e159.Google Scholar
Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V.J., Meuli, R., && Thiran, J.P. (2007). Mapping human whole-brain structural networks with diffusion MRI. PLoS One, 2(7), e597.Google Scholar
Halsey, J.H. Jr., & McFarland, S. (1974). Oxygen cycles and metabolic autoregulation. Stroke, 5(2), 219–225.CrossRefGoogle ScholarPubMed
Harrington, D.L., Rubinov, M., Durgerian, S., Mourany, L., Reece, C., Koenig, K., & Rao, S.M. (2015). Network topology and functional connectivity disturbances precede the onset of Huntington’s disease. Brain, 138(Pt 8), 2332–2346.Google Scholar
He, Y., Chen, Z.J., & Evans, A.C. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex, 17(10), 2407–2419.Google Scholar
Heinrichs, H.R., & Zakzanis, K.K. (1998). Neurocognitive deficit in schizophrenia: A quantitative review of the evidence. Neuropsychology, 12(3), 426–445.Google Scholar
Hudetz, A.G., Smith, J.J., Lee, J.G., Bosnjak, Z.J., & Kampine, J.P. (1995). Modification of cerebral laser-Doppler flow oscillations by halothane, PCO2, and nitric oxide synthase blockade. American Journal of Physiology, 269(1 Pt 2), H114–H120.Google Scholar
Humphries, M.D., & Gurney, K. (2008). Network ‘Small-World-Ness’: A quantitative method for determining canonical network equivalence. PLoS One, 3(4), e0002051.Google Scholar
Hutchison, R.M., Womelsdorf, T., Allen, E.A., Bandettini, P.A., Calhoun, V.D., Corbetta, M., & Chang, C. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage, 80, 360–378.Google Scholar
Hyvarinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. New York: John Wiley & Sons.Google Scholar
Insel, T.R., Landis, S.C., & Collins, F.S. (2013). Research priorities. The NIH BRAIN Initiative. Science, 340(6133), 687–688.Google Scholar
Iturria-Medina, Y., Sotero, R.C., Canales-Rodriguez, E.J., Aleman-Gomez, Y., & Melie-Garcia, L. (2008). Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory. Neuroimage, 40(3), 1064–1076.Google Scholar
Jafri, M.J., Pearlson, G.D., Stevens, M., & Calhoun, V.D. (2008). A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage, 39(4), 1666–1681.CrossRefGoogle ScholarPubMed
Johansen-Berg, H., & Behrens, T.E.J. (2009). Diffusion MRI: From quantitative measurement to in-vivo neuroanatomy (1st ed.). Boston: Elsevier/Academic Press.Google Scholar
Jones, D.K. (2004). The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study. Magnetic Resonance in Medicine, 51(4), 807–815.Google Scholar
Jones, D.K. (2010). Diffusion MRI: Theory, methods, and applications. New York: Oxford University Press.Google Scholar
Kalcher, K., Boubela, R.N., Huf, W., Bartova, L., Kronnerwetter, C., Derntl, B., & Moser, E. (2014). The spectral diversity of resting-state fluctuations in the human brain. PLoS One, 9(4), e93375.Google Scholar
Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A., & Arieli, A. (2003). Spontaneously emerging cortical representations of visual attributes. Nature, 425(6961), 954–956.Google Scholar
Kopell, N.J., Gritton, H.J., Whittington, M.A., & Kramer, M.A. (2014). Beyond the connectome: The dynome. Neuron, 83(6), 1319–1328.Google Scholar
Laufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., Salek-Haddadi, A., && Kleinschmidt, A. (2003). Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. Proceedings of the National Academy of Sciences of the United States of America, 100(19), 11053–11058.Google Scholar
Leonardi, N., Shirer, W.R., Greicius, l.D., & Van De Ville, D. (2014). Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time: Disentangling dynamic networks. Human Brain Mapping, 35(12), 5984–5995.Google Scholar
Leopold, D.A., Murayama, Y., & Logothetis, N.K. (2003). Very slow activity fluctuations in monkey visual cortex: Implications for functional brain imaging. Cerebral Cortex, 13(4), 422–433.Google Scholar
Liu, Y., Liang, M., Zhou, Y., He, Y., Hao, Y., Song, M., & Jiang, T. (2008). Disrupted small-world networks in schiz+ophrenia. Brain, 131(Pt 4), 945–961.Google Scholar
Logothetis, N.K. (2002). The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. Philosophical Transactions of the Royal Society B: Biological Sciences, 357(1424), 1003–1037.Google Scholar
Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature, 412(6843), 150–157.Google Scholar
Lowe, M.J., Beall, E.B., Sakaie, K.E., Koenig, K.A., Stone, L., Marrie, R.A., && Phillips, M.D. (2008). Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity. Human Brain Mapping, 29(7), 818–827.Google Scholar
Lowe, M.J., Dzemidzic, M., Lurito, J.T., Mathews, V.P., & Phillips, M.D. (2000). Correlations in low-frequency BOLD fluctuations reflect cortico-cortical connections. Neuroimage, 12(5), 582–587.Google Scholar
Lowe, M.J., Horenstein, C., Hirsch, J.G., Marrie, R.A., Stone, L., Bhattacharyya, P.K., et al. (2006). Functional pathway-defined MRI diffusion measures reveal increased transverse diffusivity of water in multiple sclerosis. Neuroimage, 32(3), 1127–1133.Google Scholar
Lowe, M.J., Koenig, K.A., Beall, E.B., Sakaie, K., Stone, L., Bermel, R., && Phillips, M.D. (2014). Anatomic connectivity assessed using pathway radial diffusivity is related to functional connectivity in monosynaptic pathways. Brain Connectivity, 4(7), 558–565.CrossRefGoogle ScholarPubMed
Lowe, M.J., Mock, B.J., & Sorenson, J.A. (1998). Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. Neuroimage, 7(2), 119–132.Google Scholar
Lowe, M.J., & & Russell, D.P. (1999). Treatment of baseline drifts in fMRI time series analysis. Journal of Computer Assisted Tomography, 23(3), 463–473.Google Scholar
Lowe, M.J., Rutecki, P., Woodard, A., Turski, P., & Sorenson, J.A. (1997). Auditory cortex fMRI noise correlations in callosal agenesis. Human Brain Mapping, 5(4), S194.Google Scholar
Lowe, M.J., & Sorenson, J.A. (1997). Spatially filtering functional magnetic resonance imaging data. Magnetic Resonance in Medicine, 37(5), 723–729.Google Scholar
Makeig, S., Debener, S., Onton, J., & Delorme, A. (2004). Mining event-related brain dynamics. Trends in Cognitive Sciences, 8(5), 204–210.Google Scholar
McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.P., Kindermann, S.S., Bell, A.J., && Sejnowski, T.J. (1998). Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6, 160–629.Google Scholar
Mesholam-Gately, R.I., Giuliano, A.J., Goff, K.P., Faraone, S.V., & Seidman, L.J. (2009). Neurocognition in first-episode schizophrenia: A meta-analytic review. Neurophysiology, 23(3), 315–336.Google Scholar
Meunier, D., Lambiotte, R., & Bullmore, E.T. (2010). Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience, 4, 200.Google Scholar
Michel, C.M., Murray, M.M., Lantz, G., Gonzalez, S., Spinelli, L., & Grave de Peralta, R. (2004). EEG source imaging. Clinical Neurophysiology, 115(10), 2195–2222.Google Scholar
Mori, S., Crain, B.J., Chacko, V.P., & van Zijl, P.C. (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 45(2), 265–269.Google Scholar
Moskalenko, Y.E. (1980). Biophysical aspects of cerebral circulation. Oxford: Pergamon Press.Google Scholar
Newman, M. (2003). The structure and function of complex networks. SIAM Review, 45(2), 167–256.Google Scholar
Newman, M. (2010). Networks: An introduction. Oxford: Oxford University Press.Google Scholar
Nunez, P.L., Silberstein, R.B., Shi, Z., Carpenter, M.R., Srinivasan, R., Tucker, D.M., & Wijesinghe, R.S. (1999). EEG coherency II: Experimental comparisons of multiple measures. Clinical Neurophysiology, 110(3), 469–486.Google Scholar
Nunez, P.L., & Srinivasan, R. (2006). Electric fields of the brain: The neurophysics of EEG (2nd ed.). New York: Oxford University Press.Google Scholar
Nunez, P.L., Srinivasan, R., Westdorp, A.F., Wijesinghe, R.S., Tucker, D.M., Silberstein, R. B., && Cadusch, P. J. (1997). EEG coherency: I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalography and Clinical Neurophysiology, 103(5), 499–515.Google Scholar
Obrig, H., Neufang, M., Wenzel, R., Kohl, M., Steinbrink, J., Einhaupl, K., et al. (2000). Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults. Neuroimage, 12(6), 623–639.Google Scholar
Oguz, I., Farzinfar, M., Matsui, J., Budin, F., Liu, Z., Gerig, G., & Styner, M. (2014). DTIPrep: Quality control of diffusion-weighted images. Frontiers in Neuroinformatics, 8, 4.Google Scholar
Peltier, S.J., & Noll, D.C. (2002). T(2)(*) dependence of low frequency functional connectivity. Neuroimage, 16(4), 985–992.Google Scholar
Perlbarg, V., Bellec, P., Anton, J.L., Pelegrini-Issac, M., Doyon, J., & Benali, H. (2007). CORSICA: Correction of structured noise in fMRI by automatic identification of ICA components. Magnetic Resonance Imaging, 25(1), 35–46.CrossRefGoogle ScholarPubMed
Pierpaoli, C., Barnett, A., Pajevic, S., Chen, R., Penix, L.R., Virta, A., && Basser, P. (2001). Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture. Neuroimage, 13(6 Pt 1), 1174–1185.Google Scholar
Power, J.D., Mitra, A., Laumann, T.O., Snyder, A.Z., Schlaggar, B.L., & Petersen, S.E. (2012). Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage, 84C, 320–341.Google Scholar
Power, J.D., Schlaggar, B.L., & Petersen, S.E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage, 105, 536–551.Google Scholar
Press, W., Teukolsky, S., Vetterling, W., & Flannery, B. (1993). Numerical recipes in C: The art of scientific computing. Cambridge: Cambridge University Press.Google Scholar
Pruim, R.H., Mennes, M., Buitelaar, J.K., & Beckmann, C.F. (2015). Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI. Neuroimage, 112, 278–287.Google Scholar
Quigley, M., Cordes, D., Turski, P., Moritz, C., Haughton, V., Seth, R., &&Meyerand, M.E. (2003). Role of the corpus callosum in functional connectivity. AJNR American Journal of Neuroradiology, 24(2), 208–212.Google Scholar
Raichle, M.E., MacLeod, A. M., Snyder, A. Z., Powers, W.J., Gusnard, D.A., & Shulman, G.L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676–682.Google Scholar
Rashid, B., Damaraju, E., Pearlson, G.D., & Calhoun, V.D. (2014). Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects. Frontiers in Human Neuroscience, 8, 897.CrossRefGoogle ScholarPubMed
Rubinov, M., & Bullmore, E. (2013). Fledgling pathoconnectomics of psychiatric disorders. Trends in Cognitive Sciences, 17(12), 641–647.Google Scholar
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059–1069.Google Scholar
Rubinov, M., & Sporns, O. (2011). Weight-conserving characterization of complex functional brain networks. Neuroimage, 56(4), 2068–2079.Google Scholar
Sakoğlu, Ü., Pearlson, G.D., Kiehl, K.A., Wang, Y.M., Michael, A.M., & Calhoun, V.D. (2010). A method for evaluating dynamic functional network connectivity and task-modulation: Application to schizophrenia. Magnetic Resonance Materials in Physics, Biology and Medicine, 23(5-6), 351–366.Google Scholar
Schmithorst, V.J., & Holland, S.K. (2004). Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data. Journal of Magnetic Resonance Imaging, 19(3), 365–368.CrossRefGoogle ScholarPubMed
Schoffelen, J.-M., & Gross, J. (2009). Source connectivity analysis with MEG and EEG. Human Brain Mapping, 30(6), 1857–1865.Google Scholar
Seghier, M.L., & Friston, K.J. (2013). Network discovery with large DCMs. Neuroimage, 68(C), 181–191.Google Scholar
Siegel, M., Donner, T.H., & Engel, A.K. (2012). Spectral fingerprints of large-scale neuronal interactions. Nature Reviews Neuroscience, 12, 121–134.Google Scholar
Skare, S., & Andersson, J.L. (2001). On the effects of gating in diffusion imaging of the brain using single shot EPI. Magnetic Resonance in Medicine, 19(8), 1125–1128.Google Scholar
Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., & Beckmann, C.F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13040–13045.Google Scholar
Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., & Behrens, T.E. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage, 31(4), 1487–1505.Google Scholar
Smith, S.M., Miller, K.L., Salimi-Khorshidi, G., Webster, M., Beckmann, C.F., Nichols, T.E., & Woolrich, M.W. (2011). Network modelling methods for FMRI. Neuroimage, 54(2), 875–891.Google Scholar
Song, S.K., Sun, S.W., Ju, W.K., Lin, S.J., Cross, A.H., & Neufeld, A.H. (2003). Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage, 20(3), 1714–1722.Google Scholar
Sporns, O. (2012). Discovering the human connectome. Cambridge, MA: MIT Press.Google Scholar
Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: A structural description of the human brain. PLoS Computational Biology, 1(4), e42.Google Scholar
Srinivasan, R., Winter, W.R., Ding, J., & Nunez, P.L. (2007). EEG and MEG coherence: Measures of functional connectivity at distinct spatial scales of neocortical dynamics. Journal of Neuroscience Methods, 166(1), 41–52.Google Scholar
Stam, C.J. (2004). Functional connectivity patterns of human magnetoencephalographic recordings: A ‘small-world’ network? Neuroscience Letters, 355(1–2), 25–28.Google Scholar
Stam, C.J. (2014). Modern network science of neurological disorders. [Review]. Nature Reviews Neuroscience, 15(10), 683.Google Scholar
Stam, C.J., Jones, B.F., Nolte, G., Breakspear, M., & Scheltens, P. (2007). Small-world networks and functional connectivity in Alzheimer’s disease. Cerebral Cortex, 17(1), 92–99.Google Scholar
Stanisz, G.J., Szafer, A., Wright, G.A., & Henkelman, R.M. (1997). An analytical model of restricted diffusion in bovine optic nerve. Magnetic Resonance in Medicine, 37(1), 103–111.Google Scholar
Stone, J.V. (2004). Independent component analysis: A tutorial introduction. Cambridge, MA: MIT press.Google Scholar
Strother, S.C., Anderson, J.R., Schaper, K.A., Sidtis, J.J., Liow, J.S., Woods, R.P., & Rottenberg, D.A. (1995). Principal component analysis and the scaled subprofile model compared to intersubject averaging and statistical parametric mapping: I. “Functional connectivity” of the human motor system studied with [15O]water PET. Journal of Cerebral Blood Flow & Metabolism, 15(5), 738–753.Google Scholar
Thirion, B., Varoquaux, G., Dohmatob, E., & Poline, J.-B. (2014). Which fMRI clustering gives good brain parcellations? [Original Research]. Frontiers in Neuroscience, 8, 167.Google Scholar
Tournier, J.D., Mori, S., & Leemans, A. (2011). Diffusion tensor imaging and beyond. Magnetic Resonance in Medicine, 65(6), 1532–1556.Google Scholar
van den Heuvel, M.P., & Sporns, O. (2011). Rich-club organization of the human connectome. The Journal of Neuroscience, 31(44), 15775–15786.Google Scholar
van den Heuvel, M.P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683–696.Google Scholar
Van Dijk, K.R., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., & Buckner, R.L. (2010). Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization. Journal of Neurophysiology, 103, 297–321.Google Scholar
Varela, F., Lachaux, J.P., Rodriguez, E., & Martinerie, J. (2001). The brainweb: Phase synchronization and large-scale integration. Nature Reviews. Neuroscience, 2(4), 229–239.Google Scholar
Vern, B.A., Schuette, W.H., Leheta, B., Juel, V.C., & Radulovacki, M. (1988). Low-frequency oscillations of cortical oxidative metabolism in waking and sleep. Journal of Cerebral Blood Flow & Metabolism, 8(2), 215–226.Google Scholar
Wang, Y., Wang, Q., Haldar, J.P., Yeh, F.C., Xie, M., Sun, P., & Song, S.K. (2011). Quantification of increased cellularity during inflammatory demyelination. Brain, 134(Pt 12), 3590–3601.Google Scholar
Watts, D.J., & Strogatz, S.H. (1998). Collective dynamics of /“small-world/” networks. Nature, 393(6684), 440–442.Google Scholar
Wheeler-Kingshott, C.A., & Cercignani, M. (2009). About “axial” and “radial” diffusivities. Magnetic Resonance in Medicine, 61(5), 1255–1260.Google Scholar
Yaesoubi, M., Miller, R.L., & Calhoun, V.D. (2015). Mutually temporally independent connectivity patterns: A new framework to study the dynamics of brain connectivity at rest with application to explain group difference based on gender. Neuroimage, 107, 85–94.Google Scholar
Yang, H., Long, X.Y., Yang, Y., Yan, H., Zhu, C.Z., Zhou, X.P., & Gong, Q, Y. (2007). Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage, 36(1), 144–152.Google Scholar
Yendiki, A., Koldewyn, K., Kakunoori, S., Kanwisher, N., & Fischl, B. (2013). Spurious group differences due to head motion in a diffusion MRI study. Neuroimage, 88C, 79–90.Google Scholar
Yendiki, A., Panneck, P., Srinivasan, P., Stevens, A., Zollei, L., Augustinack, J., & Fischl, B. (2011). Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Frontiers in Neuroinformatics, 5, 23.Google Scholar
Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. (2004). Regional homogeneity approach to fMRI data analysis. Neuroimage, 22(1), 394–400.Google Scholar