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Meta-connectomics: human brain network and connectivity meta-analyses

Published online by Cambridge University Press:  26 January 2016

N. A. Crossley*
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK Institute for Biological and Medical Engineering, Schools of Medicine, Biological Sciences and Engineering, P. Catholic University of Chile, Chile Department of Psychiatry, School of Medicine, P. Catholic University of Chile, Chile
P. T. Fox
Research Imaging Institute and Department of Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA South Texas Veterans Health Care System, Research Service, San Antonio, TX, USA
E. T. Bullmore
Behavioural & Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, UK Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK GlaxoSmithKline, ImmunoPsychiatry, Alternative Discovery & Development, Cambridge, UK
*Address for correspondence: Dr N. A. Crossley, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK. (Email:


Abnormal brain connectivity or network dysfunction has been suggested as a paradigm to understand several psychiatric disorders. We here review the use of novel meta-analytic approaches in neuroscience that go beyond a summary description of existing results by applying network analysis methods to previously published studies and/or publicly accessible databases. We define this strategy of combining connectivity with other brain characteristics as ‘meta-connectomics’. For example, we show how network analysis of task-based neuroimaging studies has been used to infer functional co-activation from primary data on regional activations. This approach has been able to relate cognition to functional network topology, demonstrating that the brain is composed of cognitively specialized functional subnetworks or modules, linked by a rich club of cognitively generalized regions that mediate many inter-modular connections. Another major application of meta-connectomics has been efforts to link meta-analytic maps of disorder-related abnormalities or MRI ‘lesions’ to the complex topology of the normative connectome. This work has highlighted the general importance of network hubs as hotspots for concentration of cortical grey-matter deficits in schizophrenia, Alzheimer's disease and other disorders. Finally, we show how by incorporating cellular and transcriptional data on individual nodes with network models of the connectome, studies have begun to elucidate the microscopic mechanisms underpinning the macroscopic organization of whole-brain networks. We argue that meta-connectomics is an exciting field, providing robust and integrative insights into brain organization that will likely play an important future role in consolidating network models of psychiatric disorders.

Review Article
Copyright © Cambridge University Press 2016 

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Bargmann, CI, Marder, E (2013). From the connectome to brain function. Nature Methods 10, 483490.Google Scholar
Barron, DS, Eickhoff, SB, Clos, M, Fox, PT (2015). Human pulvinar functional organization and connectivity. Human Brain Mapping 36, 24172431.Google Scholar
Birn, RM, Molloy, EK, Patriat, R, Parker, T, Meier, TB, Kirk, GR, Nair, VA, Meyerand, ME, Prabhakaran, V (2013). The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage 83, 550558.Google Scholar
Bullmore, E, Sporns, O (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10, 186198.Google Scholar
Bullmore, ET, Frangou, S, Murray, RM (1997). The dysplastic net hypothesis: an integration of developmental and dysconnectivity theories of schizophrenia. Schizophrenia Research 28, 143156.Google Scholar
Caspers, J, Zilles, K, Amunts, K, Laird, AR, Fox, PT, Eickhoff, SB (2014). Functional characterization and differential coactivation patterns of two cytoarchitectonic visual areas on the human posterior fusiform gyrus. Human Brain Mapping 35, 27542767.Google Scholar
Cauda, F, Cavanna, AE, D'Agata, F, Sacco, K, Duca, S, Geminiani, GC (2011). Functional connectivity and coactivation of the nucleus accumbens: a combined functional connectivity and structure-based meta-analysis. Journal of Cognitive Neuroscience 23, 28642877.Google Scholar
Cieslik, EC, Zilles, K, Caspers, S, Roski, C, Kellermann, TS, Jakobs, O, Langner, R, Laird, AR, Fox, PT, Eickhoff, SB (2013). Is there ‘one’ DLPFC in cognitive action control? Evidence for heterogeneity from co-activation-based parcellation. Cerebral Cortex 23, 26772689.Google Scholar
Crossley, N, Mechelli, A, Ginestet, C, Rubinov, M, Bullmore, E, McGuire, P (2015 a). Altered hub functioning and compensatory activations in the connectome: a meta-analysis of functional neuroimaging studies in schizophrenia. Schizophrenia Bulletin. Published online: 15 October 2015. doi:10.1093/schbul/sbv146.Google Scholar
Crossley, NA, Mechelli, A, Scott, J, Carletti, F, Fox, PT, McGuire, P, Bullmore, ET (2014). The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137, 23822395.Google Scholar
Crossley, NA, Mechelli, A, Vertes, PE, Winton-Brown, TT, Patel, AX, Ginestet, CE, McGuire, P, Bullmore, ET (2013). Cognitive relevance of the community structure of the human brain functional coactivation network. Proceedings of the National Academy of Sciences USA 110, 1158311588.Google Scholar
Crossley, NA, Scott, J, Ellison-Wright, I, Mechelli, A (2015 b). Neuroimaging distinction between neurological and psychiatric disorders. British Journal of Psychiatry 207, 429434.Google Scholar
De Luca, M, Beckmann, CF, De Stefano, N, Matthews, PM, Smith, SM (2006). fMRI resting state networks define distinct modes of long-distance interactions in the human brain. NeuroImage 29, 13591367.Google Scholar
Egger, M, Smith, GD, Sterne, JA (2001). Uses and abuses of meta-analysis. Clinical Medicine 1, 478484.Google Scholar
Eickhoff, SB, Bzdok, D, Laird, AR, Roski, C, Caspers, S, Zilles, K, Fox, PT (2011). Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation. NeuroImage 57, 938949.Google Scholar
Eickhoff, SB, Laird, AR, Fox, PT, Bzdok, D, Hensel, L (2016). Functional segregation of the human dorsomedial prefrontal cortex. Cerebral Cortex 26, 304321.Google Scholar
Eickhoff, SB, Laird, AR, Grefkes, C, Wang, LE, Zilles, K, Fox, PT (2009). Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Human Brain Mapping 30, 29072926.Google Scholar
Felleman, DJ, Van Essen, DC (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1, 147.Google Scholar
Fodor, JA (1983). The Modularity of Mind: An Essay on Faculty Psychology. MIT Press: Cambridge, Mass, London.Google Scholar
Fornito, A, Zalesky, A, Bullmore, ET (2016). Fundamentals of Brain Network Analysis. Academic Press, in press.Google Scholar
Fox, PT (1995). Spatial normalization origins: objectives, applications, and alternatives. Human Brain Mapping 3, 161164.Google Scholar
Fox, PT, Lancaster, JL (2002). Opinion: mapping context and content: the BrainMap model. Nature Reviews Neuroscience 3, 319321.Google Scholar
Fox, PT, Lancaster, JL, Laird, AR, Eickhoff, SB (2014). Meta-analysis in human neuroimaging: computational modeling of large-scale databases. Annual Review of Neuroscience 37, 409434.Google Scholar
Friston, K (1994). Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapping 2, 5678.Google Scholar
Friston, KJ, Frith, CD (1995). Schizophrenia: a disconnection syndrome? Clinical Neurosciences 3, 8997.Google Scholar
Gong, Q, He, Y (2015). Depression, neuroimaging and connectomics: a selective overview. Biological Psychiatry 77, 223235.Google Scholar
Goodkind, M, Eickhoff, SB, Oathes, DJ, Jiang, Y, Chang, A, Jones-Hagata, LB, Ortega, BN, Zaiko, YV, Roach, EL, Korgaonkar, MS, Grieve, SM, Galatzer-Levy, I, Fox, PT, Etkin, A (2015). Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305315.Google Scholar
Gratton, C, Nomura, EM, Perez, F, D'Esposito, M (2012). Focal brain lesions to critical locations cause widespread disruption of the modular organization of the brain. Journal of Cognitive Neuroscience 24, 12751285.Google Scholar
Greicius, M (2008). Resting-state functional connectivity in neuropsychiatric disorders. Current Opinion in Neurology 21, 424430.Google Scholar
Harrison, PJ (1999). The neuropathology of schizophrenia. A critical review of the data and their interpretation. Brain 122, 593624.Google Scholar
Hawrylycz, MJ, Lein, ES, Guillozet-Bongaarts, AL, Shen, EH, Ng, L, Miller, JA, van de Lagemaat, LN, Smith, KA, Ebbert, A, Riley, ZL, Abajian, C, Beckmann, CF, Bernard, A, Bertagnolli, D, Boe, AF, Cartagena, PM, Chakravarty, MM, Chapin, M, Chong, J, Dalley, RA, Daly, BD, Dang, C, Datta, S, Dee, N, Dolbeare, TA, Faber, V, Feng, D, Fowler, DR, Goldy, J, Gregor, BW, Haradon, Z, Haynor, DR, Hohmann, JG, Horvath, S, Howard, RE, Jeromin, A, Jochim, JM, Kinnunen, M, Lau, C, Lazarz, ET, Lee, C, Lemon, TA, Li, L, Li, Y, Morris, JA, Overly, CC, Parker, PD, Parry, SE, Reding, M, Royall, JJ, Schulkin, J, Sequeira, PA, Slaughterbeck, CR, Smith, SC, Sodt, AJ, Sunkin, SM, Swanson, BE, Vawter, MP, Williams, D, Wohnoutka, P, Zielke, HR, Geschwind, DH, Hof, PR, Smith, SM, Koch, C, Grant, SG, Jones, AR (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391399.Google Scholar
Kapur, S, Phillips, AG, Insel, TR (2012). Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Molecular Psychiatry 17, 11741179.Google Scholar
Kelly, C, Zuo, XN, Gotimer, K, Cox, CL, Lynch, L, Brock, D, Imperati, D, Garavan, H, Rotrosen, J, Castellanos, FX, Milham, MP (2011). Reduced interhemispheric resting state functional connectivity in cocaine addiction. Biological Psychiatry 69, 684692.Google Scholar
Koski, L, Paus, T (2000). Functional connectivity of the anterior cingulate cortex within the human frontal lobe: a brain-mapping meta-analysis. Experimental Brain Research 133, 5565.Google Scholar
Laird, AR, Eickhoff, SB, Li, K, Robin, DA, Glahn, DC, Fox, PT (2009). Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. Journal of Neuroscience 29, 1449614505.Google Scholar
Laird, AR, Eickhoff, SB, Rottschy, C, Bzdok, D, Ray, KL, Fox, PT (2013). Networks of task co-activations. NeuroImage 80, 505514.Google Scholar
Laird, AR, Fox, PM, Eickhoff, SB, Turner, JA, Ray, KL, McKay, DR, Glahn, DC, Beckmann, CF, Smith, SM, Fox, PT (2011). Behavioral interpretations of intrinsic connectivity networks. Journal of Cognitive Neuroscience 23, 40224037.Google Scholar
Laird, AR, Lancaster, JL, Fox, PT (2005). BrainMap: the social evolution of a human brain mapping database. Neuroinformatics 3, 6578.Google Scholar
Mennes, M, Biswal, BB, Castellanos, FX, Milham, MP (2013). Making data sharing work: the FCP/INDI experience. NeuroImage 82, 683691.Google Scholar
Patel, AX, Kundu, P, Rubinov, M, Jones, PS, Vertes, PE, Ersche, KD, Suckling, J, Bullmore, ET (2014). A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. NeuroImage 95, 287304.Google Scholar
Postuma, RB, Dagher, A (2006). Basal ganglia functional connectivity based on a meta-analysis of 126 positron emission tomography and functional magnetic resonance imaging publications. Cerebral Cortex 16, 15081521.Google Scholar
Power, JD, Barnes, KA, Snyder, AZ, Schlaggar, BL, Petersen, SE (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 21422154.Google Scholar
Radua, J, Mataix-Cols, D, Phillips, ML, El-Hage, W, Kronhaus, DM, Cardoner, N, Surguladze, S (2012). A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. European Psychiatry 27, 605611.Google Scholar
Raichle, ME, Snyder, AZ (2007). A default mode of brain function: a brief history of an evolving idea. NeuroImage 37, 10831090; discussion 1097–9.Google Scholar
Raj, A, Kuceyeski, A, Weiner, M (2012). A network diffusion model of disease progression in dementia. Neuron 73, 12041215.Google Scholar
Rajkowska, G, Selemon, LD, Goldman-Rakic, PS (1998). Neuronal and glial somal size in the prefrontal cortex: a postmortem morphometric study of schizophrenia and Huntington disease. Archives of General Psychiatry 55, 215224.Google Scholar
Richiardi, J, Altmann, A, Milazzo, AC, Chang, C, Chakravarty, MM, Banaschewski, T, Barker, GJ, Bokde, AL, Bromberg, U, Buchel, C, Conrod, P, Fauth-Buhler, M, Flor, H, Frouin, V, Gallinat, J, Garavan, H, Gowland, P, Heinz, A, Lemaitre, H, Mann, KF, Martinot, JL, Nees, F, Paus, T, Pausova, Z, Rietschel, M, Robbins, TW, Smolka, MN, Spanagel, R, Strohle, A, Schumann, G, Hawrylycz, M, Poline, JB, Greicius, MD (2015). Brain networks. Correlated gene expression supports synchronous activity in brain networks. Science 348, 12411244.Google Scholar
Robinson, JL, Laird, AR, Glahn, DC, Blangero, J, Sanghera, MK, Pessoa, L, Fox, PM, Uecker, A, Friehs, G, Young, KA, Griffin, JL, Lovallo, WR, Fox, PT (2012). The functional connectivity of the human caudate: an application of meta-analytic connectivity modeling with behavioral filtering. NeuroImage 60, 117129.Google Scholar
Robinson, JL, Laird, AR, Glahn, DC, Lovallo, WR, Fox, PT (2010). Metaanalytic connectivity modeling: delineating the functional connectivity of the human amygdala. Human Brain Mapping 31, 173184.Google Scholar
Scannell, JW, Blakemore, C, Young, MP (1995). Analysis of connectivity in the cat cerebral cortex. Journal of Neuroscience 15, 14631483.Google Scholar
Scholtens, LH, Schmidt, R, de Reus, MA, van den Heuvel, MP (2014). Linking macroscale graph analytical organization to microscale neuroarchitectonics in the macaque connectome. Journal of Neuroscience 34, 1219212205.Google Scholar
Seeley, WW, Crawford, RK, Zhou, J, Miller, BL, Greicius, MD (2009). Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 4252.Google Scholar
Shulman, GL, Fiez, JA, Corbetta, M, Buckner, RL, Miezin, FM, Raichle, ME, Petersen, SE (1997). Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. Journal of Cognitive Neuroscience 9, 648663.Google Scholar
Smith, SM, Fox, PT, Miller, KL, Glahn, DC, Fox, PM, Mackay, CE, Filippini, N, Watkins, KE, Toro, R, Laird, AR, Beckmann, CF (2009). Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences USA 106, 1304013045.Google Scholar
Spence, SA, Liddle, PF, Stefan, MD, Hellewell, JS, Sharma, T, Friston, KJ, Hirsch, SR, Frith, CD, Murray, RM, Deakin, JF, Grasby, PM (2000). Functional anatomy of verbal fluency in people with schizophrenia and those at genetic risk. Focal dysfunction and distributed disconnectivity reappraised. British Journal of Psychiatry 176, 5260.Google Scholar
Sporns, O (2011). Networks of the Brain. MIT Press.Google Scholar
Stephan, KE, Friston, KJ, Frith, CD (2009). Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophrenia Bulletin 35, 509527.Google Scholar
Stephan, KE, Kamper, L, Bozkurt, A, Burns, GA, Young, MP, 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, 11591186.Google Scholar
Uddin, LQ, Kinnison, J, Pessoa, L, Anderson, ML (2014). Beyond the tripartite cognition-emotion-interoception model of the human insular cortex. Journal of Cognitive Neuroscience 26, 1627.Google Scholar
van den Heuvel, MP, Scholtens, LH, Feldman Barrett, L, Hilgetag, CC, de Reus, MA (2015). Bridging cytoarchitectonics and connectomics in human cerebral cortex. Journal of Neuroscience 35, 1394313948.Google Scholar
van den Heuvel, MP, Sporns, O (2011). Rich-club organization of the human connectome. Journal of Neuroscience 31, 1577515786.Google Scholar
van den Heuvel, MP, Sporns, O, Collin, G, Scheewe, T, Mandl, RC, Cahn, W, Goni, J, Hulshoff Pol, HE, Kahn, RS (2013). Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 70, 783792.Google Scholar
Van Essen, DC, Smith, SM, Barch, DM, Behrens, TE, Yacoub, E, Ugurbil, K (2013). The WU-Minn human connectome project: an overview. NeuroImage 80, 6279.Google Scholar
Van Horn, JD, Gazzaniga, MS (2013). Why share data? Lessons learned from the fMRIDC. NeuroImage 82, 677682.Google Scholar
Vul, E, Harris, C, Winkielamn, P, Pashler, H (2009). Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspectives on Psychological Science 4, 274290.Google Scholar
Warren, DE, Power, JD, Bruss, J, Denburg, NL, Waldron, EJ, Sun, H, Petersen, SE, Tranel, D (2014). Network measures predict neuropsychological outcome after brain injury. Proceedings of the National Academy of Sciences USA 111, 1424714252.Google Scholar
Wasserman, S, Faust, K (1994). Social Network Analysis: Methods and Applications. Cambridge University Press: USA.Google Scholar
Wright, IC, Rabe-Hesketh, S, Woodruff, PW, David, AS, Murray, RM, Bullmore, ET (2000). Meta-analysis of regional brain volumes in schizophrenia. American Journal of Psychiatry. 157, 1625.Google Scholar
Zhou, J, Gennatas, ED, Kramer, JH, Miller, BL, Seeley, WW (2012). Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73, 12161227.Google Scholar
Zilles, K, Amunts, K (2010). Centenary of Brodmann's map – conception and fate. Nature Reviews. Neuroscience 11, 139145.Google Scholar