Skip to main content
×
Home
    • Aa
    • Aa

Modern Methods for Interrogating the Human Connectome

  • Mark J. Lowe (a1), Ken E. Sakaie (a1), Erik B. Beall (a1), Vince D. Calhoun (a2) (a3), David A. Bridwell (a2) (a3), Mikail Rubinov (a4) and Stephen M. Rao (a5)...
Abstract
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)

Copyright
Corresponding author
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: raos2@ccf.org
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

S. Achard , R. Salvador , B. Whitcher , J. Suckling , & E. Bullmore (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. The Journal of Neuroscience, 26(1), 6372.

A. Alexander-Bloch , J.N. Giedd , & E. Bullmore (2013). Imaging structural co-variance between human brain regions. [Review]. Nature Reviews Neuroscience, 14(5), 322336.

E.A. Allen , E.B. Erhardt , E. Damaraju , W. Gruner , J.M. Segall , R.F. Silva , & V.D. Calhoun (2011). A baseline for the multivariate comparison of resting-state networks. Frontiers in Systems Neuroscience, 5, 2.

D. Barazany , P.J. Basser , & Y. Assaf (2009). In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. Brain, 132(Pt 5), 12101220.

P.J. Basser , J. Mattiello , & D. LeBihan (1994). MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1), 259267.

P.J. Basser , S. Pajevic , C. Pierpaoli , J. Duda , & A. Aldroubi (2000). In vivo fiber tractography using DT-MRI data. Magnetic Resonance in Medicine, 44(4), 625632.

D.S. Bassett , A. Meyer-Lindenberg , S. Achard , T. Duke , & E. Bullmore (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), 1951819523.

E.B. Beall , & M.J. Lowe (2007). Isolating physiologic noise sources with independently determined spatial measures. Neuroimage, 37(4), 12861300.

E.B. Beall , & M.J. Lowe (2010). The non-separability of physiologic noise in functional connectivity MRI with spatial ICA at 3T. Journal of Neuroscience Methods, 191(2), 263276.

C.F. Beckmann , & S.M. Smith (2005). Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage, 25(1), 294311.

T.E. Behrens , & O. Sporns (2012). Human connectomics. Current Opinion in Neurobiology, 22(1), 144153.

B.B. Biswal , & A.G. Hudetz (1996). Synchronous oscillations in cerebrocortical capillary red blood cell velocity after nitric oxide synthase inhibition. Microvascular Research, 52(1), 112.

B.B. Biswal , J. Van Kylen , & J.S. Hyde (1997). Simultaneous assessment of flow and BOLD signals in resting-state. NMR in Biomedicine, 10(4-5), 165170.

B.B. Biswal , F.Z. Yetkin , V.M. Haughton , & J.S. Hyde (1995). Functional connectivity in the motor cortex of resting human brain. Magnetic Resonance in Medicine, 34(4), 537541.

M.D. Budde , J.H. Kim , H.F. Liang , R.E. Schmidt , J.H. Russell , A.H. Cross , && S.K. Song (2007). Toward accurate diagnosis of white matter pathology using diffusion tensor imaging. Magnetic Resonance in Medicine, 57(4), 688695.

G. Buzsaki (2006). Rhythms of the brain. New York: Oxford University Press.

V.D. Calhoun , & T. Adali (2012). Multi-subject independent component analysis of fMRI: A decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Reviews in Biomedical Engineering, 5, 6072.

V.D. Calhoun , & E. Allen (2013). Extracting intrinsic functional networks with feature-based group independent component analysis. Psychometrika, 78(2), 243259.

V.D. Calhoun , K.A. Kiehl , & G.D. Pearlson (2008). Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Human Brain Mapping, 29(7), 828838.

V.D. Calhoun , R. Miller , G. Pearlson , & T. Adalı (2014). The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262274.

M. Catani , R.J. Howard , S. Pajevic , & D.K. Jones (2002). Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage, 17(1), 7794.

C. Chang , & G.H. Glover (2010). Time–frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage, 50(1), 8198.

D. Cohen , & B.N. Cuffin (1983). Demonstration of useful differences between magnetoencephalogram and electroencephalogram. Electroencephalography and Clinical Neurophysiology, 56(1), 3851.

V. Colizza , A. Flammini , M.A. Serrano , & A. Vespignani (2006). Detecting rich-club ordering in complex networks. [10.1038/nphys209]. Nature Physics, 2(2), 110115.

R. Cooper , H.J. Crow , W.G. Walter , & A.L. Winter (1966). Regional control of cerebral vascular reactivity and oxygen supply in man. Brain Research, 3(2), 174191.

R.W. Cox (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162173.

R.C. Craddock , S. Jbabdi , C.-G. Yan , J.T. Vogelstein , F.X. Castellanos , A. Di Martino , & M.P. Milham (2013). Imaging human connectomes at the macroscale. Nature Methods, 10(6), 524539.

E. Damaraju , E.A. Allen , A. Belger , J.M. Ford , S. McEwen , D.H. Mathalon , & V.D. Calhoun (2014). Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage. Clinical, 5, 298308.

G. Douaud , S. Jbabdi , T.E. Behrens , R.A. Menke , A. Gass , A.U. Monsch , & S. Smith (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), 880890.

F. Esposito , T. Scarabino , A. Hyvarinen , J. Himberg , E. Formisano , S. Comani , & F. Salle (2005). Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage, 25(1), 193205.

M.D. Fox , & M.E. Raichle (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8(9), 700711.

K.J. Friston , C.D. Frith , P.F. Liddle , & R.S. Frackowiak (1993). Functional connectivity: The principal-component analysis of large (PET) data sets. Journal of Cerebral Blood Flow & Metabolism, 13(1), 514.

N. Geschwind (1965a). Disconnexion syndromes in animals and man. I. Brain, 88(2), 237294.

G. Gong , Y. He , L. Concha , C. Lebel , D.W. Gross , A.C. Evans , && C. Beaulieu (2009). Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cerebral Cortex, 19(3), 524536.

Y. Guo , & G. Pagnoni (2008). A unified framework for group independent component analysis for multi-subject fMRI data. Neuroimage, 42(3), 10781093.

D.A. Gusnard , E. Akbudak , G.L. Shulman , & M.E. Raichle (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), 42594264.

P. Hagmann , L. Cammoun , X. Gigandet , S. Gerhard , P.E. Grant , V.J. Wedeen , & O. Sporns (2010). MR connectomics: Principles and challenges. Journal of Neuroscience Methods, 194(1), 3445.

P. Hagmann , M. Kurant , X. Gigandet , P. Thiran , V.J. Wedeen , R. Meuli , && J.P. Thiran (2007). Mapping human whole-brain structural networks with diffusion MRI. PLoS One, 2(7), e597.

J.H. Halsey Jr., & S. McFarland (1974). Oxygen cycles and metabolic autoregulation. Stroke, 5(2), 219225.

D.L. Harrington , M. Rubinov , S. Durgerian , L. Mourany , C. Reece , K. Koenig , & S.M. Rao (2015). Network topology and functional connectivity disturbances precede the onset of Huntington’s disease. Brain, 138(Pt 8), 23322346.

Y. He , Z.J. Chen , & A.C. Evans (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex, 17(10), 24072419.

R.M. Hutchison , T. Womelsdorf , E.A. Allen , P.A. Bandettini , V.D. Calhoun , M. Corbetta , & C. Chang (2013). Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage, 80, 360378.

A. Hyvarinen , J. Karhunen , & E. Oja (2001). Independent component analysis. New York: John Wiley & Sons.

T.R. Insel , S.C. Landis , & F.S. Collins (2013). Research priorities. The NIH BRAIN Initiative. Science, 340(6133), 687688.

Y. Iturria-Medina , R.C. Sotero , E.J. Canales-Rodriguez , Y. Aleman-Gomez , & L. Melie-Garcia (2008). Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory. Neuroimage, 40(3), 10641076.

D.K. Jones (2004). The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study. Magnetic Resonance in Medicine, 51(4), 807815.

D.K. Jones (2010). Diffusion MRI: Theory, methods, and applications. New York: Oxford University Press.

T. Kenet , D. Bibitchkov , M. Tsodyks , A. Grinvald , & A. Arieli (2003). Spontaneously emerging cortical representations of visual attributes. Nature, 425(6961), 954956.

H. Laufs , K. Krakow , P. Sterzer , E. Eger , A. Beyerle , A. Salek-Haddadi , && A. Kleinschmidt (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), 1105311058.

N. Leonardi , W.R. Shirer , l.D. Greicius , & D. Van De Ville (2014). Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time: Disentangling dynamic networks. Human Brain Mapping, 35(12), 59845995.

D.A. Leopold , Y. Murayama , & N.K. Logothetis (2003). Very slow activity fluctuations in monkey visual cortex: Implications for functional brain imaging. Cerebral Cortex, 13(4), 422433.

Y. Liu , M. Liang , Y. Zhou , Y. He , Y. Hao , M. Song , & T. Jiang (2008). Disrupted small-world networks in schiz+ophrenia. Brain, 131(Pt 4), 945961.

N.K. Logothetis (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), 10031037.

N.K. Logothetis , J. Pauls , M. Augath , T. Trinath , & A. Oeltermann (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature, 412(6843), 150157.

M.J. Lowe , E.B. Beall , K.E. Sakaie , K.A. Koenig , L. Stone , R.A. Marrie , && M.D. Phillips (2008). Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity. Human Brain Mapping, 29(7), 818827.

M.J. Lowe , M. Dzemidzic , J.T. Lurito , V.P. Mathews , & M.D. Phillips (2000). Correlations in low-frequency BOLD fluctuations reflect cortico-cortical connections. Neuroimage, 12(5), 582587.

M.J. Lowe , C. Horenstein , J.G. Hirsch , R.A. Marrie , L. Stone , P.K. Bhattacharyya , et al. (2006). Functional pathway-defined MRI diffusion measures reveal increased transverse diffusivity of water in multiple sclerosis. Neuroimage, 32(3), 11271133.

M.J. Lowe , K.A. Koenig , E.B. Beall , K. Sakaie , L. Stone , R. Bermel , && M.D. Phillips (2014). Anatomic connectivity assessed using pathway radial diffusivity is related to functional connectivity in monosynaptic pathways. Brain Connectivity, 4(7), 558565.

M.J. Lowe , B.J. Mock , & J.A. Sorenson (1998). Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. Neuroimage, 7(2), 119132.

M.J. Lowe , & J.A. Sorenson (1997). Spatially filtering functional magnetic resonance imaging data. Magnetic Resonance in Medicine, 37(5), 723729.

S. Makeig , S. Debener , J. Onton , & A. Delorme (2004). Mining event-related brain dynamics. Trends in Cognitive Sciences, 8(5), 204210.

M.J. McKeown , S. Makeig , G.G. Brown , T.P. Jung , S.S. Kindermann , A.J. Bell , && T.J. Sejnowski (1998). Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6, 160629.

D. Meunier , R. Lambiotte , & E.T. Bullmore (2010). Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience, 4, 200.

C.M. Michel , M.M. Murray , G. Lantz , S. Gonzalez , L. Spinelli , & R. Grave de Peralta (2004). EEG source imaging. Clinical Neurophysiology, 115(10), 21952222.

S. Mori , B.J. Crain , V.P. Chacko , & P.C. van Zijl (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 45(2), 265269.

Y.E. Moskalenko (1980). Biophysical aspects of cerebral circulation. Oxford: Pergamon Press.

M. Newman (2003). The structure and function of complex networks. SIAM Review, 45(2), 167256.

P.L. Nunez , R.B. Silberstein , Z. Shi , M.R. Carpenter , R. Srinivasan , D.M. Tucker , & R.S. Wijesinghe (1999). EEG coherency II: Experimental comparisons of multiple measures. Clinical Neurophysiology, 110(3), 469486.

P.L. Nunez , R. Srinivasan , A.F. Westdorp , R.S. Wijesinghe , D.M. Tucker , R. B. Silberstein , && P. J. Cadusch (1997). EEG coherency: I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalography and Clinical Neurophysiology, 103(5), 499515.

H. Obrig , M. Neufang , R. Wenzel , M. Kohl , J. Steinbrink , K. Einhaupl , et al. (2000). Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults. Neuroimage, 12(6), 623639.

I. Oguz , M. Farzinfar , J. Matsui , F. Budin , Z. Liu , G. Gerig , & M. Styner (2014). DTIPrep: Quality control of diffusion-weighted images. Frontiers in Neuroinformatics, 8, 4.

S.J. Peltier , & D.C. Noll (2002). T(2)(*) dependence of low frequency functional connectivity. Neuroimage, 16(4), 985992.

V. Perlbarg , P. Bellec , J.L. Anton , M. Pelegrini-Issac , J. Doyon , & H. Benali (2007). CORSICA: Correction of structured noise in fMRI by automatic identification of ICA components. Magnetic Resonance Imaging, 25(1), 3546.

C. Pierpaoli , A. Barnett , S. Pajevic , R. Chen , L.R. Penix , A. Virta , && P. Basser (2001). Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture. Neuroimage, 13(6 Pt 1), 11741185.

J.D. Power , B.L. Schlaggar , & S.E. Petersen (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage, 105, 536551.

R.H. Pruim , M. Mennes , J.K. Buitelaar , & C.F. Beckmann (2015). Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI. Neuroimage, 112, 278287.

M.E. Raichle , A. M. MacLeod , A. Z. Snyder , W.J. Powers , D.A. Gusnard , & G.L. Shulman (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676682.

B. Rashid , E. Damaraju , G.D. Pearlson , & V.D. Calhoun (2014). Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects. Frontiers in Human Neuroscience, 8, 897.

M. Rubinov , & E. Bullmore (2013). Fledgling pathoconnectomics of psychiatric disorders. Trends in Cognitive Sciences, 17(12), 641647.

M. Rubinov , & O. Sporns (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 10591069.

V.J. Schmithorst , & S.K. Holland (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), 365368.

M.L. Seghier , & K.J. Friston (2013). Network discovery with large DCMs. Neuroimage, 68(C), 181191.

S.M. Smith , M. Jenkinson , H. Johansen-Berg , D. Rueckert , T.E. Nichols , C.E. Mackay , & T.E. Behrens (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage, 31(4), 14871505.

S.M. Smith , K.L. Miller , G. Salimi-Khorshidi , M. Webster , C.F. Beckmann , T.E. Nichols , & M.W. Woolrich (2011). Network modelling methods for FMRI. Neuroimage, 54(2), 875891.

R. Srinivasan , W.R. Winter , J. Ding , & P.L. Nunez (2007). EEG and MEG coherence: Measures of functional connectivity at distinct spatial scales of neocortical dynamics. Journal of Neuroscience Methods, 166(1), 4152.

C.J. Stam (2004). Functional connectivity patterns of human magnetoencephalographic recordings: A ‘small-world’ network? Neuroscience Letters, 355(1–2), 2528.

C.J. Stam (2014). Modern network science of neurological disorders. [Review]. Nature Reviews Neuroscience, 15(10), 683.

C.J. Stam , B.F. Jones , G. Nolte , M. Breakspear , & P. Scheltens (2007). Small-world networks and functional connectivity in Alzheimer’s disease. Cerebral Cortex, 17(1), 9299.

S.C. Strother , J.R. Anderson , K.A. Schaper , J.J. Sidtis , J.S. Liow , R.P. Woods , & D.A. Rottenberg (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), 738753.

B. Thirion , G. Varoquaux , E. Dohmatob , & J.-B. Poline (2014). Which fMRI clustering gives good brain parcellations? [Original Research]. Frontiers in Neuroscience, 8, 167.

J.D. Tournier , S. Mori , & A. Leemans (2011). Diffusion tensor imaging and beyond. Magnetic Resonance in Medicine, 65(6), 15321556.

M.P. van den Heuvel , & O. Sporns (2011). Rich-club organization of the human connectome. The Journal of Neuroscience, 31(44), 1577515786.

M.P. van den Heuvel , & O. Sporns (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683696.

K.R. Van Dijk , T. Hedden , A. Venkataraman , K.C. Evans , S.W. Lazar , & R.L. Buckner (2010). Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization. Journal of Neurophysiology, 103, 297321.

F. Varela , J.P. Lachaux , E. Rodriguez , & J. Martinerie (2001). The brainweb: Phase synchronization and large-scale integration. Nature Reviews. Neuroscience, 2(4), 229239.

B.A. Vern , W.H. Schuette , B. Leheta , V.C. Juel , & M. Radulovacki (1988). Low-frequency oscillations of cortical oxidative metabolism in waking and sleep. Journal of Cerebral Blood Flow & Metabolism, 8(2), 215226.

D.J. Watts , & S.H. Strogatz (1998). Collective dynamics of /“small-world/” networks. Nature, 393(6684), 440442.

M. Yaesoubi , R.L. Miller , & V.D. Calhoun (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, 8594.

H. Yang , X.Y. Long , Y. Yang , H. Yan , C.Z. Zhu , X.P. Zhou , & Q, Y. Gong (2007). Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage, 36(1), 144152.

Y. Zang , T. Jiang , Y. Lu , Y. He , & L. Tian (2004). Regional homogeneity approach to fMRI data analysis. Neuroimage, 22(1), 394400.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Journal of the International Neuropsychological Society
  • ISSN: 1355-6177
  • EISSN: 1469-7661
  • URL: /core/journals/journal-of-the-international-neuropsychological-society
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 9
Total number of PDF views: 89 *
Loading metrics...

Abstract views

Total abstract views: 257 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 25th March 2017. This data will be updated every 24 hours.