Neuroscience perspectives| Volume 56, ISSUE 9, P613-619, November 01, 2004

New approaches for exploring anatomical and functional connectivity in the human brain

  • Narender Ramnani
    Address reprint requests to Dr. Narender Ramnani, Centre for Functional Magnetic Resonance Imaging of the Brain, Department of Clinical Neurology, University of Oxford, Headley Way, Oxford OX3 9DU, United Kingdom
    Centre for Functional Magnetic Resonance Imaging of the Brain, Department of Clinical Neurology, University of Oxford, Oxford, United Kingdom
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  • Timothy E.J. Behrens
    Centre for Functional Magnetic Resonance Imaging of the Brain, Department of Clinical Neurology, University of Oxford, Oxford, United Kingdom
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  • Will Penny
    Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, London, United Kingdom
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  • Paul M. Matthews
    Centre for Functional Magnetic Resonance Imaging of the Brain, Department of Clinical Neurology, University of Oxford, Oxford, United Kingdom
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      Information processing in the primate brain is based on the complementary principles of modular and distributed information processing. The former emphasizes the specialization of functions within different brain areas. The latter emphasizes the massively parallel nature of brain networks and the fact that function also emerges from the flow of information between brain areas. The localization of function to specific brain areas (“functional segregation”) is the commonest approach to investigating function; however, an emerging, complementary approach (“functional integration”) describes function in terms of the information flow across networks of areas. Here, we highlight recent advances in neuroimaging methodology that have made it possible to investigate the anatomical architecture of networks in the living human brain with diffusion tensor imaging (DTI). We also highlight recent thinking on the ways in which functional imaging can be used to characterize information transmission across networks in the human brain (functional and effective connectivity).


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        • Basser P.J.
        • Mattiello J.
        • Le Bihan D.
        Estimation of the effective self-diffusion tensor from the NMR spin echo.
        J Magn Reson B. 1994; 103: 247-254
        • Basser P.J.
        • Pajevic S.
        • Pierpaoli C.
        • Duda J.
        • Aldroubi A.
        In vivo fiber tractography using DT-MRI data.
        Magn Reson Med. 2000; 44: 625-632
        • Beaulieu C.
        • Allen P.S.
        Determinants of anisotropic water diffusion in nerves.
        Magn Reson Med. 1994; 31: 394-400
        • Behrens T.E.
        • Johansen-Berg H.
        • Woolrich M.W.
        • Smith S.M.
        • Wheeler-Kingshott C.A.
        • Boulby P.A.
        • et al.
        Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging.
        Nat Neurosci. 2003; 6: 750-757
        • Behrens T.E.J.
        • Woolrich M.W.
        • Jenkinson M.
        • Johansen-Berg H.
        • Nunes R.G.
        • Clare S.
        • et al.
        Characterisation and propagation of uncertainty in diffusion weighted MR imaging.
        Magn Reson Med. 2003; 50: 1077-1088
        • Bressler S.
        • Scott Kelso J.A.
        Cortical coordination dynamics and cognition.
        Trends Cogn Sci. 2001; 5: 26-36
        • Buchel C.
        • Coull J.
        • Friston K.J.
        The predictive value of changes in effective connectivity for human learning.
        Science. 1999; 283: 1538-1541
        • Burns J.
        • Job D.
        • Bastin M.E.
        • Whalley H.
        • Macgillivray T.
        • Johnstone E.C.
        • Lawrie S.M.
        Structural disconnectivity in schizophrenia.
        Br J Psychiatry. 2003; 182: 439-443
        • Catani M.
        • Howard R.J.
        • Pajevic S.
        • Jones D.K.
        Virtual in vivo interactive dissection of white matter fasciculi in the human brain.
        Neuroimage. 2002; 17: 77-94
        • Conturo T.E.
        • Lori N.F.
        • Cull T.S.
        • Akbudak E.
        • Snyder A.Z.
        • Shimony J.S.
        • et al.
        Tracking neuronal fiber pathways in the living human brain.
        Proc Natl Acad Sci U S A. 1999; 96: 10422-10427
        • Frackowiak R.
        • Friston K.J.
        • Frith C.
        • Dolan R.
        • Price C.
        • Zeki S.
        • et al.
        Human Brain Function. 2nd ed. Elsevier Academic Press, Amsterdam2003
        • Friston K.J.
        Characterizing distributed functional systems.
        in: Frackowiak R. Friston K.J. Frith C. Dolan R. Mazziota J. Human Brain Function. Elsevier Academic Press, Amersterdam1997: 107-126
        • Friston K.J.
        • Frith C.
        • Liddle F.P.
        • Frackowiak R.
        The principal component analysis of large (PET) data sets.
        J Cereb Blood Flow Metab. 1993; 13: 5-14
        • Friston K.J.
        • Harrison L.
        • Penny W.
        Dynamic causal modeling.
        Neuroimage. 2003; 19: 1273-1302
        • Goncalves M.S.
        • Hull D.A.
        Connectivity analysis with structural equation modeling.
        Neuroimage. 2003; 20: 1455-1467
        • Harrison L.
        • Penny W.
        • Friston K.J.
        Multivariate autoregressive modeling of fMRI time series.
        Neuroimage. 2003; 19: 1477-1491
        • Honey G.D.
        • Suckling J.
        • Zelaya F.
        • Long C.
        • Routledge C.
        • Jackson S.
        • et al.
        Dopaminergic drug effects on physiological connectivity in a human cortico-striato-thalamic system.
        Brain. 2003; 126: 1767-1781
        • Jones D.K.
        Determining and visualising uncertainty in estimates of fiber orientation from diffusion tensor MRI.
        Magn Reson Med. 2003; 49: 7-12
        • Kobbert C.
        • Apps R.
        • Bechmann I.
        • Lanciego J.L.
        • Mey J.
        • Thanos S.
        Current concepts in neuroanatomical tracing.
        Prog Neurobiol. 2000; 62: 327-351
        • Le Bihan D.
        Looking into the functional architecture of the brain with diffusion MRI.
        Nat Rev Neurosci. 2003; 4: 469-480
        • Lim K.O.
        • Helpern J.A.
        Neuropsychiatric applications of DTI—a review.
        NMR Biomed. 2002; 15: 587-593
        • McIntosh A.R.
        • Gonzalez-Lima F.
        Structural equation modeling and its application to network analysis in functional brain imaging.
        Hum Brain Mapp. 1994; 2: 2-22
        • McKeown M.J.
        • Makeig S.
        • Brown G.G.
        • Jung T.P.
        • Kindermann S.S.
        • Bell A.J.
        • Sejnowski T.J.
        Analysis of fMRI data by blind separation into independent spatial components.
        Hum Brain Mapp. 1998; 6: 160-188
        • Mechelli A.
        • Price C.J.
        • Noppeney U.
        • Friston K.J.
        A dynamic causal modelling study on category effects.
        J Cogn Neurosci. 2003; 15: 925-934
        • Mori S.
        • Crain B.J.
        • Chacko V.P.
        • van Zijl P.C.
        Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging.
        Ann Neurol. 1999; 45: 265-269
        • Pautler R.G.
        • Silva A.C.
        • Koretsky A.P.
        In vivo neuronal tract tracing using manganese-enhanced magnetic resonance imaging.
        Magn Reson Med. 1998; 40: 740-748
        • Ramnani N.
        • Miall C.
        Expanding cerebellar horizons.
        Trends Cogn Sci. 2001; 5: 135-136
        • Saleem K.S.
        • Pauls J.M.
        • Augath M.
        • Trinath T.
        • Prause B.A.
        • Hashikawa T.
        • Logothetis N.K.
        Magnetic resonance imaging of neuronal connections in the macaque monkey.
        Neuron. 2002; 34: 685-700
        • Scannell J.W.
        • Blakemore C.
        • Young M.P.
        Analysis of connectivity in the cat cerebral cortex.
        J Neurosci. 1995; 15: 1463-1483
        • Stejskal E.O.
        • Tanner J.E.
        Spin diffusion measurements.
        J Chem Phys. 1965; 42: 288-292
        • Stieltjes B.
        • Kauffman W.E.
        • van Zijl P.C.
        • Frederickson K.
        • Pearlson G.D.
        • Solaiyappan M.
        • Mori S.
        Diffusion tensor imaging and axonal tracking in the human brainstem.
        Neuroimage. 2001; 14: 723-725
        • Tuch D.S.
        • Reese T.G.
        • Wiegell W.R.
        • Wedeen V.J.
        Diffusion MRI of complex neural architecture.
        Neuron. 2003; 40: 885-895
        • Ungerleider L.G.
        • Mishkin M.
        Ingle D.J. Goodale M.A. Mansfield R.J.W. Analysis of Visual Behavior. MIT Press, Cambridge, MA1982: 549-586
        • Xu D.
        • Mori S.
        • Shen D.
        • van Zijl P.C.
        • Davatzikos C.
        Spatial normalization of diffusion tensor fields.
        Magn Reson Med. 2003; 50: 175-182
        • Young M.P.
        • Scannell J.W.
        • Burns G.A.
        • Blakemore C.
        Analysis of connectivity.
        Rev Neurosci. 1994; 5: 227-250
        • Zeki S.
        • Watson J.D.
        • Lueck C.J.
        • Friston K.J.
        • Kennard C.
        • Frackowiak R.S.
        A direct demonstration of functional specialization in human visual cortex.
        J Neurosci. 1991; 11: 641-649