Skip to main content

Advertisement

SpringerLink
Log in

Human Cortical Anatomical Networks Assessed by Structural MRI

  • Published:
Brain Imaging and Behavior Aims and scope Submit manuscript

Abstract

Mapping the structure and function of the brain with non-invasive brain imaging techniques has become a world-wide enterpise in the last 20 years. The core concept that drives this rapid growth has been the use of a standardized 3D coordinate space for combining data from many subjects and/or time-points. This has allowed geographically-separated laboratories to reproduce experiments in precise detail, to share data or to perform meta-analysis in ways that go far beyond the traditional reviewing of summary results in journal publications. A further corollary of the brain mapping approach is the natural fostering of multi-center collaboration among distant sites. This article describes recent progress in trans-Pacific collaboration between Canadian and Asian laboratories in the study of neuroanatomical networks obtained from MRI data, both in the normal brain and in neurodegenerative disorders.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3, 1–10. doi:10.1371/journal.pcbi.0030017.

    Article  CAS  Google Scholar 

  • 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, 63–72. doi:10.1523/JNEUROSCI.3874-05.2006.

    Article  PubMed  CAS  Google Scholar 

  • Albert, R., Jeong, H., & Barabasi, A. L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382. doi:10.1038/35019019.

    Article  PubMed  CAS  Google Scholar 

  • Andrews, T. J., Halpern, S. D., & Purves, D. (1997). Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract. The Journal of Neuroscience, 17, 2859–2868.

    PubMed  CAS  Google Scholar 

  • Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks. The Neuroscientist, 12, 512–523. doi:10.1177/1073858406293182.

    Article  PubMed  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, 19518–19523. doi:10.1073/pnas.0606005103.

    Article  PubMed  CAS  Google Scholar 

  • Buckner, R. L. (2004). Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron, 44(1), 195–208. doi:10.1016/j.neuron.2004.09.006.

    Article  PubMed  CAS  Google Scholar 

  • Buckner, R. L., Snyder, A. Z., Shannon, B. J., LaRossa, G., Sachs, R., Fotenos, A. F., et al. (2005). Molecular, structural, and functional characterization of Alzheimer’s Disease: evidence for a relationship between default activity, amyloid, and memory. The Journal of Neuroscience, 25, 7709–7717. doi:10.1523/JNEUROSCI.2177-05.2005.

    Article  PubMed  CAS  Google Scholar 

  • Bullmore, E. T., Woodruff, P. W., Wright, I. C., Rabe-Hesketh, S., Howard, R. J., Shuriquie, N., et al. (1998). Does dysplasia cause anatomical dysconnectivity in schizophrenia? Schizophrenia Research, 30, 127–135. doi:10.1016/S0920-9964(97)00141-2.

    Article  PubMed  CAS  Google Scholar 

  • Charil, A., Dagher, A., Lerch, J. P., Zijdenbos, A. P., Worsley, K. W., & Evans, A. C. (2007). Focal cortical atrophy in multiple sclerosis: relation to lesion load and disability. NeuroImage, 34(2), 509–517. doi:10.1016/j.neuroimage.2006.10.006.

    Article  PubMed  Google Scholar 

  • Charil, A., Zijdenbos, A. P., Taylor, J., Boelman, C., Worsley, K., Evans, A. C., et al. (2003). Statistical mapping analysis of lesion location and neurological disability in multiple sclerosis: application to 452 patient data sets. NeuroImage, 19(3), 532–544. doi:10.1016/S1053-8119(03)00117-4.

    Article  PubMed  Google Scholar 

  • Chen, Z. J., He, Y., Rosa-Neto, P., Germann, J., & Evans, A. C. (2008). Uncovering modular architecture in human cortical networks. Cerebral Cortex, 18(10), 2374–2381.

    Article  PubMed  Google Scholar 

  • Danon, L., Diaz-Guilera, A., & Arenas, A. (2006). The effect of size heterogeneity on community identification in complex networks. J Stat Mech: Theory and Experiment. P11010.

  • Delbeuck, X., Van der Linden, M., & Collette, F. (2003). Alzheimer’s disease as a disconnection syndrome? Neuropsychology Review, 13, 79–92. doi:10.1023/A:1023832305702.

    Article  PubMed  CAS  Google Scholar 

  • Dougherty, R. F., Ben-Shachar, M., Bammer, R., Brewer, A. A., & Wandell, B. A. (2005). Functional organization of human occipital–callosal fiber tracts. Proceedings of the National Academy of Sciences of the United States of America, 102, 7350. doi:10.1073/pnas.0500003102.

    Article  PubMed  CAS  Google Scholar 

  • Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Neuroplasticity: changes in gray matter induced by training. Nature, 427, 311–312. doi:10.1038/427311a.

    Article  PubMed  CAS  Google Scholar 

  • Duncan, J., & Owen, A. M. (2000). Trends in Neurosciences, 23, 475–483. doi:10.1016/S0166-2236(00)01633-7.

    Article  PubMed  CAS  Google Scholar 

  • Eguiluz, V. M., Chialvo, D. R., Cecchi, G. A., Baliki, M., & Apkarian, A. V. (2005). Scale-free brain functional networks. Physical Review Letters, 94, 018102. doi:10.1103/PhysRevLett.94.018102.

    Article  PubMed  CAS  Google Scholar 

  • Ferrer, I., Blanco, R., Carulla, M., Condom, M., Alcantara, S., Olive, M., et al. (1995). Transforming growth factor–alpha immunoreactivity in the developing adult brain. Neuroscience, 66, 189–199. doi:10.1016/0306-4522(94)00584-R.

    Article  PubMed  CAS  Google Scholar 

  • Genovese, C. R., Lazar, N. A., & Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage, 15, 870–878. doi:10.1006/nimg.2001.1037.

    Article  PubMed  Google Scholar 

  • Graham, D. I., Nicoll, J. A. R., Bone I, Eds. (2006). Adams & Graham’s Introduction to Neuropathology Hodder Arnold, London.

  • Hachinski, V. C., Potter, P. & Merskey, H. (1987). Leukoaraiosis. Archives of Neurology, 44(1), 21–23.

    PubMed  CAS  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 

  • He, Y., Chen, Z. J., & Evans, A. C. (2008). Altered Small-World architectures in structural brain networks in Alzheimer’s Disease revealed by cortical thickness from MRI. The Journal of Neuroscience, 24(18), 4756–4766.

    Article  PubMed  CAS  Google Scholar 

  • Hilgetag, C. C., Burns, G. A., O’Neill, M. A., Scannell, J. W., & Young, M. P. (2000). Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 355, 91–110. doi:10.1098/rstb.2000.0551.

    Article  PubMed  CAS  Google Scholar 

  • Hillis, A. E., Wityk, R. J., Barker, P. B., Beauchamp N.J., Gailloud P., Murphy K., et al. (2002). Subcortical aphasia and neglect in acute stroke: the role of cortical hypoperfusion. Brain, 125(5), 1094–1104.

    Article  PubMed  CAS  Google Scholar 

  • Hofer, S., & Frahm, J. (2006). Topography of the human corpus callosum revisited—comprehensive fiber tractography using diffusion tensor magnetic resonance imaging. NeuroImage, 32, 989–994. doi:10.1016/j.neuroimage.2006.05.044.

    Article  PubMed  Google Scholar 

  • Huang, H., Zhang, J., Jiang, H., Wakana, S., Poetscher, L., Miller, M. I., et al. (2005). DTI tractography based parcellation of white matter: application to the mid-sagittal morphology of corpus callosum. NeuroImage, 26, 195–205. doi:10.1016/j.neuroimage.2005.01.019.

    Article  PubMed  Google Scholar 

  • Humphries, M. D., Gurney, K., & Prescott, T. J. (2005). The brainstem reticular formation is a small-world, not scale-free, network. Proc R Soc Lond B Biol Sci, 273, 503–511. doi:10.1098/rspb.2005.3354.

    Article  Google Scholar 

  • Kabani, N., Le Goualher, G., MacDonald, D., & Evans, A. C. (2001). Measurement of cortical thickness using an automated 3-D algorithm: a validation study. NeuroImage, 13, 375–380. doi:10.1006/nimg.2000.0652.

    Article  PubMed  CAS  Google Scholar 

  • Kaiser, M., & Hilgetag, C. C. (2004). Modelling the development of cortical networks. Neurocomputing, 58–60, 297–302.

    Article  Google Scholar 

  • Kaiser, M., & Hilgetag, C. C. (2006). Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems. PLoS Computational Biology, 2, e95. doi:10.1371/journal.pcbi.0020095.

    Article  PubMed  CAS  Google Scholar 

  • Kier, E. L., Staib L. H., Davis L. M., & Bronen R. A. (2004). MR imaging of the temporal stem: anatomic dissection tractography of the uncinate fasciculus, inferior occipitofrontal fasciculus, inferior occipitofrontal fasciculus, and Meyer’s loop of the optic radiation. American Journal of Neuroradiology, 25, 677–691.

    PubMed  Google Scholar 

  • Kim, J. S., Singh, V., Lee, J. K., Lerch, J., Ad-Dabbagh, Y., MacDonald, D., et al. (2005). Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage, 27, 210–221. doi:10.1016/j.neuroimage.2005.03.036.

    Article  PubMed  Google Scholar 

  • Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87, 198701. doi:10.1103/PhysRevLett.87.198701.

    Article  PubMed  CAS  Google Scholar 

  • Lee, J. K., Lee, J. M., Kim, J. S., Kim, I. Y., Evans, A. C., & Kim, S. I. (2006). A novel quantitative cross-validation of different cortical surface reconstruction algorithms using MRI phantom. NeuroImage, 31(2), 572–584. doi:10.1016/j.neuroimage.2005.12.044.

    Article  PubMed  CAS  Google Scholar 

  • Lerch, J. P., & Evans, A. C. (2005). Cortical thickness analysis examined through power analysis and a population simulation. NeuroImage, 24(1), 163–173. doi:10.1016/j.neuroimage.2004.07.045.

    Article  PubMed  Google Scholar 

  • Lerch, J. P., Pruessner, J. C., Zijdenbos, A. P., Burger, K., Hampel, H., Teipel, S. J., et al. (2005). Focal decline of cortical thickness in Alzheimer’s Disease identified by computational neuroanatomy. Cerebral Cortex (New York, N.Y.), 15(7), 995–1001. doi:10.1093/cercor/bhh200.

    Article  Google Scholar 

  • Lerch, J. P., Worsley, K., Shaw, W. P., Greenstein, D. K., Lenroot, R. K., Giedd, J., et al. (2006). Mapping Anatomical Correlations Across Cerebral Cortex (MACACC) using Cortical Thickness from MRI. Neuroimage, 31(3):993–1003.

    Article  PubMed  Google Scholar 

  • Lerch, J. P., Pruessner, J., Zijdenbos, A. P., Collins, D. L., Teipel, S. J., Hampel, H., et al. (2008). Automated cortical thickness measurements from MRI can accurately separate Alzheimer’s patients from normal elderly controls. Neurobiology of Aging, 29(1), 23–30. doi:10.1016/j.neurobiolaging.2006.09.013.

    Article  PubMed  Google Scholar 

  • Lyttelton, O. C., Boucher, M., Robbins, S., & Evans, A. C. (2007). An unbiased iterative group registration template for cortical surface analysis. NeuroImage, 34, 1535–1544. doi:10.1016/j.neuroimage.2006.10.041.

    Article  PubMed  Google Scholar 

  • MacDonald, D., Kabani, N., Avis, D., & Evans, A. C. (2000). Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage, 12, 340–356. doi:10.1006/nimg.1999.0534.

    Article  PubMed  CAS  Google Scholar 

  • Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S., et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the United States of America, 97, 4398–4403. doi:10.1073/pnas.070039597.

    Article  PubMed  CAS  Google Scholar 

  • Makris, N., Kennedy, D. N., McInerney, S., Sorensen, A. G., Wang, R., Caviness Jr., V. S., et al. (2005). Segmentation of subcomponents within the superior longitudinal fascicle in humans: a quantitative, in vivo, DTMRI study. Cerebral Cortex (New York, N.Y.), 15, 854–869. doi:10.1093/cercor/bhh186.

    Article  Google Scholar 

  • Maslov, S., & Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science, 296, 910–913. doi:10.1126/science.1065103.

    Article  PubMed  CAS  Google Scholar 

  • Mazziotta, J. C., Toga, A. W., Evans, A. C., Fox, P., & Lancaster, J. (1995). A probabilistic atlas of the human brain: theory and rationale for its development. NeuroImage, 2, 89–101.

    Article  PubMed  CAS  Google Scholar 

  • Mazziotta, J. C., Toga, A. W., Evans, A. C., Fox, P. T., Lancaster, J., Zilles, K., et al. (2001). A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 356, 1293–1322.

    Article  PubMed  CAS  Google Scholar 

  • Mechelli, A., Crinion, J. T., Noppeney, U., O’Doherty, J., Ashburner, J., Frackowiak, R. S., et al. (2004). Neurolinguistics: structural plasticity in the bilingual brain. Nature, 431, 757. doi:10.1038/431757a.

    Article  PubMed  CAS  Google Scholar 

  • Mechelli, A., Friston, K. J., Frackowiak, R. S., & Price, C. J. (2005). Structural covariance in the human cortex. The Journal of Neuroscience, 25, 8303–8310. doi:10.1523/JNEUROSCI.0357-05.2005.

    Article  PubMed  CAS  Google Scholar 

  • Medina, D., DeToledo-Morrell, L., Urresta, F., Gabrieli, J. D., Moseley, M., Fleischman, D., et al. (2006). White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiology of Aging, 27, 663–672. doi:10.1016/j.neurobiolaging.2005.03.026.

    Article  PubMed  Google Scholar 

  • Mesulam, M. M. (1990). Large-scale neurocognitive networks and distributed processing for attention, language and memory. Annals of Neurology, 28, 597–613. doi:10.1002/ana.410280502.

    Article  CAS  Google Scholar 

  • Micheloyannis, S., Pachou, E., Stam, C. J., Vourkas, M., Erimaki, S., & Tsirka, V. (2006). Using graph theoretical analysis of multi channel EEG toevaluate the neural efficiency hypothesis. Neuroscience Letters, 402, 273–277. doi:10.1016/j.neulet.2006.04.006.

    Article  PubMed  CAS  Google Scholar 

  • Mitelman, S. A., Buchsbaum, M. S., Brickman, A. M., & Shihabuddin, L. (2005). Cortical intercorrelations of frontal area volumes in schizophrenia. NeuroImage, 27, 753–770. doi:10.1016/j.neuroimage.2005.05.024.

    Article  PubMed  Google Scholar 

  • Mok, K., He, Y., Kinomura, S., Goto, R., Taki, Y., Sato, K., et al. (2008). Basis of anatomical disconnectivity on leukoaraiosis-associated cortical changes (submitted).

  • Narr, K. L., Bilder, R. M., Toga, A. W., Woods, R. P., Rex, D. E., Szeszko, P. R., et al. (2005). Mapping cortical thickness and gray matter concentration in first episode schizophrenia. Cerebral Cortex (New York, N.Y.), 15, 708–719. doi:10.1093/cercor/bhh172.

    Article  Google Scholar 

  • Newman, M. E. J., & Girvan, M. (2004). Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 69, 026133.

    Google Scholar 

  • Parent, A., & Carpenter, M. B. (1995). Human neuroanatomy. Baltimore, MD: Williams & Wilkins.

    Google Scholar 

  • Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004). Comparing dynamic causal models. NeuroImage, 22, 1157–1172. doi:10.1016/j.neuroimage.2004.03.026.

    Article  PubMed  CAS  Google Scholar 

  • Rose, S. E., Chen, F., Chalk, J. B., Zelaya, F. O., Strugnell, W. E., Benson, M., et al. (2000). Loss of connectivity in Alzheimer’s Disease: an evaluation of white matter tract integrity with colour coded MR diffusion tensor imaging. Journal of Neurology, Neurosurgery, and Psychiatry, 69, 528–530. doi:10.1136/jnnp.69.4.528.

    Article  PubMed  CAS  Google Scholar 

  • Salvador, R., Suckling, J., Coleman, M. R., Pickard, J. D., Menon, D., & Bullmore, E. (2005a). Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex (New York, N.Y.), 15, 1332–1342. doi:10.1093/cercor/bhi016.

    Article  Google Scholar 

  • Salvador, R., Suckling, J., Schwarzbauer, C., & Bullmore, E. (2005b). Undirected graphs of frequency-dependent functional connectivity in whole brain network. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360, 937–946. doi:10.1098/rstb.2005.1645.

    Article  PubMed  Google Scholar 

  • Singh, V., Chertkow, H., Lerch, J. P., Evans, A. C., Dorr, A. E., & Kabani, N. J. (2006). Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer’s Disease. Brain, 129(11), 2885–2893. doi:10.1093/brain/awl256.

    Article  PubMed  Google Scholar 

  • Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8, 418–425. doi:10.1016/j.tics.2004.07.008.

    Article  PubMed  Google Scholar 

  • Sporns, O., & Tononi, G. (2002). Classes of network connectivity and dynamics. Complexity, 7, 28–38. doi:10.1002/cplx.10015.

    Article  Google Scholar 

  • Sporns, O., Tononi, G., & Edelman, G. M. (2000). Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex (New York, N.Y.), 10, 127–141. doi:10.1093/cercor/10.2.127.

    Article  CAS  Google Scholar 

  • Sporns, O., Tononi, G., & Kotter, R. (2005). The human connectome: a structural description of the human brain. PLoS Computational Biology, 1, 245–251. doi:10.1371/journal.pcbi.0010042.

    Article  CAS  Google Scholar 

  • Sporns, O., & Zwi, J. D. (2004). The small world of the cerebral cortex. Neuroinformatics, 2, 145–162. doi:10.1385/NI:2:2:145.

    Article  PubMed  Google Scholar 

  • Stam, C. J. (2004). Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network? Neuroscience Letters, 355, 25–28. doi:10.1016/j.neulet.2003.10.063.

    Article  PubMed  CAS  Google Scholar 

  • Stam, C. J., Jones, B. F., Manshanden, I., van Cappellen, A. M., Montez, T., Verbunt, J. P., et al. (2006). MEG evaluation of resting-state functional connectivity in Alzheimer’s disease. NeuroImage, 32, 1335–1344. doi:10.1016/j.neuroimage.2006.05.033.

    Article  PubMed  CAS  Google Scholar 

  • Stam, C., Jones, B., Nolte, G., Breakspear, M., & Scheltens, P. (2007). Small-world networks and functional connectivity in Alzheimer’s Disease. Cerebral Cortex (New York, N.Y.), 17, 92–99. doi:10.1093/cercor/bhj127.

    Article  CAS  Google Scholar 

  • Steinmetz, H., Herzog, A., Huang, Y., & Hacklander, T. (1994). Discordant brainsurface anatomy in monozygotic twins. The New England Journal of Medicine, 331, 951–952. doi:10.1056/NEJM199410063311419.

    Article  PubMed  CAS  Google Scholar 

  • Stephan, K. E., Hilgetag, C. C., Burns, G. A., O’Neill, M. A., Young, M. P., & Kotter, R. (2000). Computational analysis of functional connectivity between areas of primate cerebral cortex. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 355, 111–126. doi:10.1098/rstb.2000.0552.

    Article  PubMed  CAS  Google Scholar 

  • Stephan, K. E., Kamper, L., Bozkurt, A., Burns, G. A., Young, M. P., & Kotter, R. (2001). Advanced database methodology for the collation of connectivity data on the Macaque brain (CoCoMac). Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 356, 1159–1186. doi:10.1098/rstb.2001.0908.

    Article  PubMed  CAS  Google Scholar 

  • Strogatz, S. H. (2001). Exploring complex networks. Nature, 410, 268–276. doi:10.1038/35065725.

    Article  PubMed  CAS  Google Scholar 

  • Suddath, R. L., Christison, G. W., Torrey, E. F., Casanova, M. F., & Weinberger, D. R. (1990). Anatomical abnormalities in the brains of monozygotic twins discordant for schizophrenia. The New England Journal of Medicine, 322, 789–794.

    Article  PubMed  CAS  Google Scholar 

  • Taki, Y., Goto, R., Evans, A. C., Zijdenbos, A. P., Neelin, P., Lerch, J., et al. (2004). Voxel-based morphometry of human brain with age and cerebrovascular risk factors. Neurobiology of Aging, 25, 455–463. doi:10.1016/j.neurobiolaging.2003.09.002.

    Article  PubMed  Google Scholar 

  • Teipel, S. J., Stahl, R., Dietrich, O., Schoenberg, S. O., Perneczky, R., Bokde, A. L., et al. (2007). Multivariate network analysis of fiber tract integrity in Alzheimer’s Disease. NeuroImage, 34, 985–995. doi:10.1016/j.neuroimage.2006.07.047.

    Article  PubMed  Google Scholar 

  • Thompson, P. M., Cannon, T. D., Narr, K. L., van Erp, T., Poutanen, V. P., Huttunen, M., et al. (2001). Genetic influences on brain structure. Nature Neuroscience, 4, 1253–1258.

    Article  PubMed  CAS  Google Scholar 

  • Tomimoto, H., Akiguchi, I., Snenaga T., Nishimura, M., Wakita, H., Nakamura S., et al. (1996). Alterations of the blood brain barrier and glial cells in white matter lesions in cerebrovascular and Alzheimer’s Disease patients. Stroke, 27(11), 2069–2074.

    PubMed  CAS  Google Scholar 

  • Tootell, R. B., Tsao, D., & Vanduffel, W. (2003). Neuroimaging weighs in: humans meet macaques in “primate” visual cortex. The Journal of Neuroscience, 23, 3981–3989.

    PubMed  CAS  Google Scholar 

  • Tuch, D. S., Wisco, J. J., Khachaturian, M. H., Ekstrom, L. B., Kotter, R., & Vanduffel, W. (2005). Q-ball imaging of macaque white matter architecture. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360, 869–879. doi:10.1098/rstb.2005.1651.

    Article  PubMed  Google Scholar 

  • Wakana, S., Jiang, H., Nagae-Poetscher, L. M., van Zijl, P. C., & Mori, S. (2004). Fiber tract-based atlas of human white matter anatomy. Radiology, 2003, 77–87. doi:10.1148/radiol.2301021640.

    Article  Google Scholar 

  • Watkins, K. E., Paus, T., Lerch, J. P., Zijdenbos, A., Collins, D. L., Neelin, P., et al. (2001). Structural asymmetries in the human brain: a voxel-based statistical analysis of 142 MRI scans. Cerebral Cortex (New York, N.Y.), 11, 868–877. doi:10.1093/cercor/11.9.868.

    Article  CAS  Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442. doi:10.1038/30918.

    Article  PubMed  CAS  Google Scholar 

  • Woodruff, P. W., Wright, I. C., Shuriquie, N., Russouw, H., Rushe, T., Howard, R. J., et al. (1997). Structural brain abnormalities in male schizophrenics reflect fronto–temporal dissociation. Psychological Medicine, 27, 1257–1266. doi:10.1017/S0033291797005229.

    Article  PubMed  CAS  Google Scholar 

  • Wright, I. C., Sharma, T., Ellison, Z. R., McGuire, P. K., Friston, K. J., Brammer, M. J., et al. (1999). Supra-regional brain systems and the neuropathology of schizophrenia. Cerebral Cortex (New York, N.Y.), 9, 366–378. doi:10.1093/cercor/9.4.366.

    Article  CAS  Google Scholar 

  • Zarei, M., Johansen-Berg, H., Smith, S., Ciccarelli, O., Thompson, A. J., & Matthews, P. M. (2006). Functional anatomy of interhemispheric cortical connections in the human brain. Journal of Anatomy, 209, 311–320. doi:10.1111/j.1469-7580.2006.00615.x.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada, H3A 2B4

    A. C. Evans, Y. He, J. S. Kim, Z. Chen, O. Lyttelton, J. Lerch, V. Singh, R. Goto, S. Kinomura, K. Mok, J. K. Lee & U. Yoon

  2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, People’s Republic of China

    Y. He & T. Jiang

  3. Department of Nuclear Medicine and Radiology, Institute of Development, Aging, and Cancer, Tohoku University Hospital, Sendai, Japan

    H. Fukuda, R. Kawashima, K. Sato, Y. Taki, R. Goto, S. Kinomura & K. Mok

  4. Department of Biomedical Engineering, Hanyang University, 17 Haengdang-dong, Sungdong-gu, P.O. Box 55, Seoul, 133-791, Republic of Korea

    J. M. Lee, S. I. Kim, J. S. Kim, K. Im, V. Singh, J. K. Lee & U. Yoon

Authors
  1. A. C. Evans
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. M. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. I. Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. H. Fukuda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. R. Kawashima
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Y. He
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. T. Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. J. S. Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Z. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. K. Im
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. O. Lyttelton
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. J. Lerch
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. V. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. K. Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Y. Taki
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. R. Goto
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. S. Kinomura
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. K. Mok
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. J. K. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. U. Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. C. Evans.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Evans, A.C., Lee, J.M., Kim, S.I. et al. Human Cortical Anatomical Networks Assessed by Structural MRI. Brain Imaging and Behavior 2, 289–299 (2008). https://doi.org/10.1007/s11682-008-9034-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-008-9034-3

Keywords

Search

Navigation