Skip to main content Accessibility help
Hostname: page-component-66d7dfc8f5-cdn8t Total loading time: 1.197 Render date: 2023-02-09T03:21:40.069Z Has data issue: true Feature Flags: { "useRatesEcommerce": false } hasContentIssue true

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


Bargmann, CI, Marder, E (2013). From the connectome to brain function. Nature Methods 10, 483490.CrossRefGoogle ScholarPubMed
Barron, DS, Eickhoff, SB, Clos, M, Fox, PT (2015). Human pulvinar functional organization and connectivity. Human Brain Mapping 36, 24172431.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Bullmore, E, Sporns, O (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10, 186198.CrossRefGoogle ScholarPubMed
Bullmore, ET, Frangou, S, Murray, RM (1997). The dysplastic net hypothesis: an integration of developmental and dysconnectivity theories of schizophrenia. Schizophrenia Research 28, 143156.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Crossley, NA, Scott, J, Ellison-Wright, I, Mechelli, A (2015 b). Neuroimaging distinction between neurological and psychiatric disorders. British Journal of Psychiatry 207, 429434.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Egger, M, Smith, GD, Sterne, JA (2001). Uses and abuses of meta-analysis. Clinical Medicine 1, 478484.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Eickhoff, SB, Laird, AR, Fox, PT, Bzdok, D, Hensel, L (2016). Functional segregation of the human dorsomedial prefrontal cortex. Cerebral Cortex 26, 304321.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Felleman, DJ, Van Essen, DC (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1, 147.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Fox, PT, Lancaster, JL (2002). Opinion: mapping context and content: the BrainMap model. Nature Reviews Neuroscience 3, 319321.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Friston, K (1994). Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapping 2, 5678.CrossRefGoogle Scholar
Friston, KJ, Frith, CD (1995). Schizophrenia: a disconnection syndrome? Clinical Neurosciences 3, 8997.Google ScholarPubMed
Gong, Q, He, Y (2015). Depression, neuroimaging and connectomics: a selective overview. Biological Psychiatry 77, 223235.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Greicius, M (2008). Resting-state functional connectivity in neuropsychiatric disorders. Current Opinion in Neurology 21, 424430.CrossRefGoogle ScholarPubMed
Harrison, PJ (1999). The neuropathology of schizophrenia. A critical review of the data and their interpretation. Brain 122, 593624.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Laird, AR, Eickhoff, SB, Rottschy, C, Bzdok, D, Ray, KL, Fox, PT (2013). Networks of task co-activations. NeuroImage 80, 505514.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Laird, AR, Lancaster, JL, Fox, PT (2005). BrainMap: the social evolution of a human brain mapping database. Neuroinformatics 3, 6578.CrossRefGoogle ScholarPubMed
Mennes, M, Biswal, BB, Castellanos, FX, Milham, MP (2013). Making data sharing work: the FCP/INDI experience. NeuroImage 82, 683691.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Raichle, ME, Snyder, AZ (2007). A default mode of brain function: a brief history of an evolving idea. NeuroImage 37, 10831090; discussion 1097–9.CrossRefGoogle ScholarPubMed
Raj, A, Kuceyeski, A, Weiner, M (2012). A network diffusion model of disease progression in dementia. Neuron 73, 12041215.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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 ScholarPubMed
Scannell, JW, Blakemore, C, Young, MP (1995). Analysis of connectivity in the cat cerebral cortex. Journal of Neuroscience 15, 14631483.Google ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Seeley, WW, Crawford, RK, Zhou, J, Miller, BL, Greicius, MD (2009). Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 4252.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
van den Heuvel, MP, Sporns, O (2011). Rich-club organization of the human connectome. Journal of Neuroscience 31, 1577515786.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Van Horn, JD, Gazzaniga, MS (2013). Why share data? Lessons learned from the fMRIDC. NeuroImage 82, 677682.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Wasserman, S, Faust, K (1994). Social Network Analysis: Methods and Applications. Cambridge University Press: USA.CrossRefGoogle 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.CrossRefGoogle Scholar
Zhou, J, Gennatas, ED, Kramer, JH, Miller, BL, Seeley, WW (2012). Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73, 12161227.CrossRefGoogle ScholarPubMed
Zilles, K, Amunts, K (2010). Centenary of Brodmann's map – conception and fate. Nature Reviews. Neuroscience 11, 139145.CrossRefGoogle ScholarPubMed
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the or variations. ‘’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

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

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

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

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *