At macroscopic scales, the human connectome comprises anatomically distinct brain areas, the structural pathways connecting them and their functional interactions. Annotation of phenotypic associations with variation in the connectome and cataloging of neurophenotypes promise to transform our understanding of the human brain. In this Review, we provide a survey of magnetic resonance imaging–based measurements of functional and structural connectivity. We highlight emerging areas of development and inquiry and emphasize the importance of integrating structural and functional perspectives on brain architecture.
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References
Sporns, O., Tononi, G. & Kötter, R. The human connectome: A structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005).
Varela, F., Lachaux, J.P., Rodriguez, E. & Martinerie, J. The brainweb: phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2, 229–239 (2001).
Biswal, B.B. et al. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. USA 107, 4734–4739 (2010).
Behrens, T.E.J. & Sporns, O. Human connectomics. Curr. Opin. Neurobiol. 22, 144–153 (2012).
Kelly, C., Biswal, B.B., Craddock, R.C., Castellanos, X.F. & Milham, M.P. Characterizing variation in the functional connectome: promise and pitfalls. Trends Cogn. Sci. 16, 181–188 (2012).
Talairach, J. & Tournoux, P. Co-planar Stereotaxic Atlas of the Human Brain (Thieme Classics, 1988).
Margulies, D.S. et al. Mapping the functional connectivity of anterior cingulate cortex. Neuroimage 37, 579–588 (2007).
Beckmann, M., Johansen-Berg, H. & Rushworth, M.F.S. Connectivity-based parcellation of human cingulate cortex and its relation to functional specialization. J. Neurosci. 29, 1175–1190 (2009).
Dosenbach, N.U. et al. A core system for the implementation of task sets. Neuron 50, 799–812 (2006).
Bellec, P. et al. Identification of large-scale networks in the brain using fMRI. Neuroimage 29, 1231–1243 (2006).
Craddock, R.C., James, G.A., Holtzheimer, P.E., Hu, X.P. & Mayberg, H.S. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33, 1914–1928 (2012).
Cohen, A.L. et al. Defining functional areas in individual human brains using resting functional connectivity MRI. Neuroimage 41, 45–57 (2008).
Kiviniemi, V. et al. Functional segmentation of the brain cortex using high model order group PICA. Hum. Brain Mapp. 30, 3865–3886 (2009).
Jones, D.K. Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI. Imaging 2, 341–355 (2010).
Andersson, J.L.R., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20, 870–888 (2003).
Alexander, A.L., Tsuruda, J.S. & Parker, D.L. Elimination of eddy current artifacts in diffusion-weighted echo-planar images: the use of bipolar gradients. Magn. Reson. Med. 38, 1016–1021 (1997).
Haselgrove, J.C. & Moore, J.R. Correction for distortion of echo-planar images used to calculate the apparent diffusion coefficient. Magn. Reson. Med. 36, 960–964 (1996).
Anderson, J. et al. A comprehensive Gaussian Process framework for correcting distortions and movements in diffusion images. in Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) 20th Annual Meeting and Exhibition (Melbourne, 2012).
Sotiropoulos, S.N. et al. Effects of image reconstruction on fibre orientation mapping from multichannel diffusion MRI: reducing the noise floor using SENSE. Magn. Reson. Med. advance online publication, doi:10.1002/mrm.24623 (7 February 2013).
Leemans, A. & Jones, D.K. The B-matrix must be rotated when correcting for subject motion in DTI data. Magn. Reson. Med. 61, 1336–1349 2009).
Seunarine, K. & Alexander, D. Multiple fibers: beyond the diffusion tensor. in Diffusion MRI: From Quantitative Measurement to in vivo Neuroanatomy (eds., Johansen-Berg, H. and Behrens, T.E.J.) 55–72 (Academic Press, 2009).
Basser, P.J., Mattiello, J. & LeBihan, D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. B 103, 247–254 (1994).
Tuch, D.S. et al. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 48, 577–582 (2002).
Wedeen, V.J., Hagmann, P., Tseng, W.Y., Reese, T.G. & Weisskoff, R.M. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn. Reson. Med. 54, 1377–1386 (2005).
Behrens, T.E., Berg, H.J., Jbabdi, S., Rushworth, M.F. & Woolrich, M.W. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34, 144–155 (2007).
Catani, M., Howard, R.J., Pajevic, S. & Jones, D.K. Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage 17, 77–94 (2002).
Jbabdi, S. & Johansen-Berg, H. Tractography: where do we go from here? Brain Connect. 1, 169–183 (2011).
Assaf, Y., Blumenfeld-Katzir, T., Yovel, Y. & Basser, P.J. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn. Reson. Med. 59, 1347–1354 (2008).
Jones, D.K., Knosche, T.R. & Turner, R. White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI. Neuroimage 73, 239–254 (2013).
Mikula, S., Binding, J. & Denk, W. Staining and embedding the whole mouse brain for electron microscopy. Nat. Methods 9, 1198–1201 (2012).
Behrens, T. & Jbabdi, S. MR diffusion tractography. in Diffusion MRI: From Quantitative Measurement to in vivo Neuroanatomy (eds., Johansen-Berg, H. and Behrens, T.E.J.) 333–352 (Academic Press, 2009).
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, 5–14 (1993).
Mennes, M., Kelly, C., Colcombe, S., Castellanos, F.X. & Milham, M.P. The extrinsic and intrinsic functional architectures of the human brain are not equivalent. Cereb. Cortex 23, 223–229 (2013).
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, 537–541 (1995).
Toro, R., Fox, P.T. & Paus, T. Functional coactivation map of the human brain. Cereb. Cortex 18, 2553–2559 (2008).
Chuang, K.-H. et al. Mapping resting-state functional connectivity using perfusion MRI. Neuroimage 40, 1595–1605 (2008).
Ogawa, S. et al. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. USA 89, 5951–5955 (1992).
Wu, C.W. et al. Empirical evaluations of slice-timing, smoothing, and normalization effects in seed-based, resting-state functional magnetic resonance imaging analyses. Brain Connect. 1, 401–410 (2011).
Friston, K.J. et al. Psychophysiological and modulatory interactions in neuroimaging. Neuroimage 6, 218–229 (1997).
Friston, K.J., Williams, S., Howard, R., Frackowiak, R.S. & Turner, R. Movement-related effects in fMRI time-series. Magn. Reson. Med. 35, 346–355 (1996).
Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L. & Petersen, S.E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).
Van Dijk, K.R.A., Sabuncu, M.R. & Buckner, R.L. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59, 431–438 (2012).
Satterthwaite, T.D. et al. Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. Neuroimage 60, 623–632 (2012).
Satterthwaite, T.D. et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64, 240–256 (2013).
Yan, C.G. et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76C, 183–201 (2013).
Lund, T.E. fcMRI–mapping functional connectivity or correlating cardiac-induced noise? Magn. Reson. Med. 46, 628–629 (2001).
Birn, R.M. The role of physiological noise in resting-state functional connectivity. Neuroimage 62, 864–870 (2012).
Jo, H.J., Saad, Z.S., Simmons, W.K., Milbury, L.A. & Cox, R.W. Mapping sources of correlation in resting state fMRI, with artifact detection and removal. Neuroimage 52, 571–582 (2010).
Perlbarg, V. et al. CORSICA: correction of structured noise in fMRI by automatic identification of ICA components. Magn. Reson. Imaging 25, 35–46 (2007).
Fox, M.D., Zhang, D., Snyder, A.Z. & Raichle, M.E. The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101, 3270–3283 (2009).
Murphy, K., Birn, R.M., Handwerker, D.a., Jones, T.B. & Bandettini, P.a. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44, 893–905 (2009).
Saad, Z. et al. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2, 25–32 (2012).
Schölvinck, M.L., Maier, A., Ye, F.Q., Duyn, J.H. & Leopold, D.A. Neural basis of global resting-state fMRI activity. Proc. Natl. Acad. Sci. USA 107, 10238–10243 (2010).
Cordes, D. et al. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am. J. Neuroradiol. 22, 1326–1333 (2001).
Niazy, R.K., Xie, J., Miller, K., Beckmann, C.F. & Smith, S.M. Spectral characteristics of resting state networks. Prog. Brain Res. 193, 259–276 (2011).
Worsley, K.J., Marrett, S., Neelin, P. & Evans, A.C. Searching scale space for activation in PET images. Hum. Brain Mapp. 4, 74–90 (1996).
Marrelec, G. et al. Regions, systems, and the brain: hierarchical measures of functional integration in fMRI. Med. Image Anal. 12, 484–496 (2008).
Friston, K.J. & Frith, C.D. Time dependent changes in effective connectivity measured with PET. Hum. Brain Mapp. 1, 69–79 (1993).
Smith, S.M. et al. Network modelling methods for fMRI. Neuroimage 54, 875–891 (2011).
Varoquaux, G., Gramfort, A. & Poline, J.B. Advances in Neural Information Processing Systems. (Vancouver, Canada, 2010).
Marrelec, G. et al. Partial correlation for functional brain interactivity investigation in functional MRI. Neuroimage 32, 228–237 (2006).
Varoquaux, G. et al. A group model for stable multi-subject ICA on fMRI datasets. Neuroimage 51, 288–299 (2010).
Lowe, M.J., Dzemidzic, M., Lurito, J.T., Mathews, V.P. & Phillips, M.D. Correlations in low-frequency BOLD fluctuations reflect cortico-cortical connections. Neuroimage 12, 582–587 (2000).
Sun, F.T., Miller, L.M. & D'Esposito, M. Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. Neuroimage 21, 647–658 (2004).
Rissman, J., Gazzaley, A. & D'Esposito, M. Measuring functional connectivity during distinct stages of a cognitive task. Neuroimage 23, 752–763 (2004).
Patel, R.S., Bowman, F.D. & Rilling, J.K. A Bayesian approach to determining connectivity of the human brain. Hum. Brain Mapp. 27, 267–276 (2006).
Smith, S.M. et al. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. USA 106, 13040–13045 (2009).
Peltier, S.J., Polk, T.A. & Noll, D.C. Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm. Hum. Brain Mapp. 20, 220–226 (2003).
van den Heuvel, M., Mandl, R. & Hulshoff Pol, H. Normalized cut group clustering of resting-state fMRI data. PLoS ONE 3, e2001 (2008).
Beckmann, C.F. Modelling with independent components. Neuroimage 62, 891–901 (2012).
Breakspear, M., Brammer, M.J., Bullmore, E.T., Das, P. & Williams, L.M. Spatiotemporal wavelet resampling for functional neuroimaging data. Hum. Brain Mapp. 23, 1–25 (2004).
Bellec, P., Marrelec, G. & Benali, H. A bootstrap test to investigate changes in brain connectivity for functional MRI. Stat. Sin. 18, 1253–1268 (2008).
Smith, S.M. et al. Temporally-independent functional modes of spontaneous brain activity. Proc. Natl. Acad. Sci. USA 109, 3131–3136 (2012).
Waites, A.B., Stanislavsky, A., Abbott, D.F. & Jackson, G.D. Effect of prior cognitive state on resting state networks measured with functional connectivity. Hum. Brain Mapp. 24, 59–68 (2005).
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, 1997–2006 (2010).
Klingner, C.M., Hasler, C., Brodoehl, S., Axer, H. & Witte, O.W. Perceptual plasticity is mediated by connectivity changes of the medial thalamic nucleus. Hum. Brain Mapp. advance online publication, doi:10.1002/hbm.22074 (25 March 2012).
Riedl, V. et al. Repeated pain induces adaptations of intrinsic brain activity to reflect past and predict future pain. Neuroimage 57, 206–213 (2011).
Vercammen, A., Knegtering, H., Liemburg, E.J., den Boer, J.A. & Aleman, A. Functional connectivity of the temporo-parietal region in schizophrenia: effects of rTMS treatment of auditory hallucinations. J. Psychiatr. Res. 44, 725–731 (2010).
Keeser, D. et al. Prefrontal transcranial direct current stimulation changes connectivity of resting-state networks during fMRI. J. Neurosci. 31, 15284–15293 (2011).
Fox, M.D., Buckner, R.L., White, M.P., Greicius, M.D. & Pascual-Leone, A. Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol. Psychiatry 72, 595–603 (2012).
Tambini, A., Ketz, N. & Davachi, L. Enhanced brain correlations during rest are related to memory for recent experiences. Neuron 65, 280–290 (2010).
Lewis, C.M., Baldassarre, A., Committeri, G., Romani, G.L. & Corbetta, M. Learning sculpts the spontaneous activity of the resting human brain. Proc. Natl. Acad. Sci. USA 106, 17558–17563 (2009).
Koyama, M.S. et al. Cortical signatures of dyslexia and remediation: an intrinsic functional connectivity approach. PLoS ONE 8, e55454 (2013).
Varoquaux, G. & Craddock, R.C. Learning and comparing functional connectomes across subjects. Neuroimage advance online publication, doi:10.1016/j.neuroimage.2013.04.007 (11 April 2013).
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).
Genovese, C.R., Lazar, N.A. & Nichols, T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870–878 (2002).
Efron, B. When should hypothesis testing problems be combined? Ann. Appl. Stat. 2, 197–223 (2008).
Zalesky, A., Fornito, A. & Bullmore, E.T. Network-based statistic: identifying differences in brain networks. Neuroimage 53, 1197–1207 (2010).
Hu, J.X., Zhao, H. & Zhou, H.H. False Discovery Rate Control With Groups. J. Am. Stat. Assoc. 105, 1215–1227 (2010).
Craddock, R.C., Holtzheimer, P.E., Hu, X.P. & Mayberg, H.S. Disease state prediction from resting state functional connectivity. Magn. Reson. Med. 62, 1619–1628 (2009).
Dosenbach, N.U.F. et al. Prediction of individual brain maturity using fMRI. Science 329, 1358–1361 (2010).
Bunke, H. A graph distance metric based on the maximal common subgraph. Pattern Recognit. Lett. 19, 255–259 (1998).
Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).
Bullmore, E.T. & Bassett, D.S. Brain graphs: graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 7, 113–140 (2011).
Zuo, X.N. et al. Network centrality in the human functional connectome. Cereb. Cortex 22, 1862–1875 (2012).
Bassett, D.S., Meyer-Lindenberg, A., Achard, S., Duke, T. & Bullmore, E. Adaptive reconfiguration of fractal small-world human brain functional networks. Proc. Natl. Acad. Sci. USA 103, 19518–19523 (2006).
Richiardi, J., Eryilmaz, H., Schwartz, S., Vuilleumier, P. & Van De Ville, D. Decoding brain states from fMRI connectivity graphs. Neuroimage 56, 616–626 (2011).
Hansen, L.K. Multivariate strategies in functional magnetic resonance imaging. Brain Lang. 102, 186–191 (2007).
Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning (Springer, 2001).
Zhu, C.Z. et al. Discriminative analysis of brain function at resting-state for attention-deficit/hyperactivity disorder. Med. Image Comput. Comput. Assist. Interv. 8, 468–475 (2005).
Vincent, J.L. et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447, 83–86 (2007).
Pawela, C.P. et al. Resting-state functional connectivity of the rat brain. Magn. Reson. Med. 59, 1021–1029 (2008).
Becerra, L., Pendse, G., Chang, P.-C., Bishop, J. & Borsook, D. Robust reproducible resting state networks in the awake rodent brain. PLoS ONE 6, e25701 (2011).
Wang, K. et al. Temporal scaling properties and spatial synchronization of spontaneous blood oxygenation level-dependent (BOLD) signal fluctuations in rat sensorimotor network at different levels of isoflurane anesthesia. NMR Biomed. 24, 61–67 (2011).
Pawela, C.P. et al. A protocol for use of medetomidine anesthesia in rats for extended studies using task-induced BOLD contrast and resting-state functional connectivity. Neuroimage 46, 1137–1147 (2009).
Sörös, P. & Stanton, S.G. On variability and genes: inter-individual differences in auditory brain function. Front. Hum. Neurosci. 6, 150 (2012).
Kapur, S., Phillips, A.G. & Insel, T.R. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Mol. Psychiatry 17, 1174–1179 (2012).
Mennes, M., Biswal, B.B., Castellanos, F.X. & Milham, M.P. Making data sharing work: the FCP/INDI experience. Neuroimage advance online publication, doi:10.1016/j.neuroimage.2012.10.064 (30 October 2012).
Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002).
Desikan, R.S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).
Eickhoff, S.B. et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 25, 1325–1335 (2005).
Lancaster, J.L. et al. Automated Talairach atlas labels for functional brain mapping. Hum. Brain Mapp. 10, 120–131 (2000).
Margulies, D.S. et al. Precuneus shares intrinsic functional architecture in humans and monkeys. Proc. Natl. Acad. Sci. USA 106, 20069–20074 (2009).
Knock, S.A. et al. The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models. J. Neurosci. Methods 183, 86–94 (2009).
Van Horn, J.D. et al. Mapping connectivity damage in the case of Phineas Gage. PLoS ONE 7, e37454 (2012).
Jiang, T. Brainnetome: a new -ome to understand the brain and its disorders. Neuroimage advance online publication, doi:10.1016/j.neuroimage.2013.04.002 (6 April 2013).
Haacke, E.M., Bornw, R.W., Thompson, M.R. & Venkatesan, R. Magnetic Resonance Imaging: Physical Principles and Sequence Design (Wiley-Liss, 1999).
Bernstein, M.A., King, K.F. & Zhou, X.J. Handbook of MRI Pulse Sequences (Academic Press, 2004).
Blaimer, M. et al. SMASH, SENSE, PILS, GRAPPA: how to choose the optimal method. Top. Magn. Reson. Imaging 15, 223–236 (2004).
Yacoub, E. et al. Spin-echo fMRI in humans using high spatial resolutions and high magnetic fields. Magn. Reson. Med. 49, 655–664 (2003).
Weiskopf, N., Hutton, C., Josephs, O. & Deichmann, R. Optimal EPI parameters for reduction of susceptibility-induced BOLD sensitivity losses: a whole-brain analysis at 3 T and 1.5 T. Neuroimage 33, 493–504 (2006).
Glover, G.H. & Law, C.S. Spiral-in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts. Magn. Reson. Med. 46, 515–522 (2001).
Heberlein, K.A. & Hu, X. Simultaneous acquisition of gradient-echo and asymmetric spin-echo for single-shot z-shim: Z-SAGA. Magn. Reson. Med. 51, 212–216 (2004).
Gonzalez-Castillo, J., Roopchansingh, V., Bandettini, P.A. & Bodurka, J. Physiological noise effects on the flip angle selection in BOLD fMRI. Neuroimage 54, 2764–2778 (2011).
Grootoonk, S. et al. Characterization and correction of interpolation effects in the realignment of fMRI time series. Neuroimage 11, 49–57 (2000).
Noll, D.C., Cohen, J.D., Meyer, C.H. & Schneider, W. Spiral K-space MR imaging of cortical activation. J. Magn. Reson. Imaging 5, 49–56 (1995).
Rabrait, C. et al. High temporal resolution functional MRI using parallel echo volumar imaging. J. Magn. Reson. Imaging 27, 744–753 (2008).
Moeller, S. et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn. Reson. Med. 63, 1144–1153 (2010).
Setsompop, K. et al. Improving diffusion MRI using simultaneous multi-slice echo planar imaging. Neuroimage 63, 569–580 (2012).
Feinberg, D.A. et al. Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS ONE 5, e15710 (2010).
Bright, M.G. & Murphy, K. Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data. Neuroimage 64, 526–537 (2013).
Heine, L. et al. Resting state networks and consciousness: alterations of multiple resting state network connectivity in physiological, pharmacological, and pathological consciousness States. Front. Psychol. 3, 295 (2012).
Boly, M. et al. Brain connectivity in disorders of consciousness. Brain Connect. 2, 1–10 (2012).
Horovitz, S.G. et al. Decoupling of the brain's default mode network during deep sleep. Proc. Natl. Acad. Sci. USA 106, 11376–11381 (2009).
Spoormaker, V.I. et al. Development of a large-scale functional brain network during human non-rapid eye movement sleep. J. Neurosci. 30, 11379–11387 (2010).
Friston, K. Dynamic causal modeling and Granger causality Comments on: the identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. Neuroimage 58, 303–305 (2011).
David, O. et al. Identifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biol. 6, 2683–2697 (2008).
Scannell, J.W., Burns, G.A., Hilgetag, C.C., O'Neil, M.A. & Young, M.P. The connectional organization of the cortico-thalamic system of the cat. Cereb. Cortex 9, 277–299 (1999).
Mclntosh, A.R. & Gonzalez-Lima, F. Structural equation modeling and its application to network analysis in functional brain imaging. Hum. Brain Mapp. 2, 2–22 (1994).
Lohmann, G., Erfurth, K., Müller, K. & Turner, R. Critical comments on dynamic causal modelling. Neuroimage 59, 2322–2329 (2012).
Zhuang, J., LaConte, S., Peltier, S., Zhang, K. & Hu, X. Connectivity exploration with structural equation modeling: an fMRI study of bimanual motor coordination. Neuroimage 25, 462–470 (2005).
Goebel, R. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magn. Reson. Imaging 21, 1251–1261 (2003).
Granger, C.W. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969).
Sridharan, D., Levitin, D.J. & Menon, V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Natl. Acad. Sci. USA 105, 12569–12574 (2008).
Deshpande, G. & Hu, X. Investigating effective brain connectivity from fMRI data: past findings and current issues with reference to Granger causality analysis. Brain Connect. 2, 235–245 (2012).
Johnston, J.M. et al. Loss of resting interhemispheric functional connectivity after complete section of the corpus callosum. J. Neurosci. 28, 6453–6458 (2008).
Matsumoto, R. et al. Functional connectivity in the human language system: a cortico-cortical evoked potential study. Brain 127, 2316–2330 (2004).
Bohning, D.E. et al. Echoplanar BOLD fMRI of brain activation induced by concurrent transcranial magnetic stimulation. Invest. Radiol. 33, 336–340 (1998).
Ruff, C.C. et al. Distinct causal influences of parietal versus frontal areas on human visual cortex: evidence from concurrent TMS-fMRI. Cereb. Cortex 18, 817–827 (2008).
Datta, A. et al. Gyri-precise head model of transcranial direct current stimulation: improved spatial focality using a ring electrode versus conventional rectangular pad. Brain Stimulat. 2, 201–207 (2009).
Acknowledgements
This work was supported by grants from US National Institute of Mental Health (BRAINS R01MH094639 to M.P.M. and K23MH087770 to A.D.M.), the Stavros Niarchos Foundation (M.P.M.), the Brain and Behavior Research Foundation (R.C.C.) and the Leon Levy Foundation (C.K. and A.D.M.). J.T.V. receives funding from the London Institute for Mathematical Sciences HDTRA1-11-1-0048 and US National Institutes of Health R01ES017436. Additional support was provided by a gift from Joseph P. Healey to the Child Mind Institute (M.P.M.). We thank D. Lurie for his assistance in the preparation of the manuscript and references as well as Z. Shehzad, Z. Yang and S. Urchs for their helpful comments. We acknowledge our colleagues who allowed us to reproduce their figures.
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K.H. is a full time employee of Siemens Medical Solutions USA, and owns shares in Siemens, AG.
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Craddock, R., Jbabdi, S., Yan, CG. et al. Imaging human connectomes at the macroscale. Nat Methods 10, 524–539 (2013). https://doi.org/10.1038/nmeth.2482
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DOI: https://doi.org/10.1038/nmeth.2482
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