Graph analysis of the human connectome: Promise, progress, and pitfalls
Open accessAbstract
The human brain is a complex, interconnected network par excellence. Accurate and informative mapping of this human connectome has become a central goal of neuroscience. At the heart of this endeavor is the notion that brain connectivity can be abstracted to a graph of nodes, representing neural elements (e.g., neurons, brain regions), linked by edges, representing some measure of structural, functional or causal interaction between nodes. Such a representation brings connectomic data into the realm of graph theory, affording a rich repertoire of mathematical tools and concepts that can be used to characterize diverse anatomical and dynamical properties of brain networks. Although this approach has tremendous potential - and has seen rapid uptake in the neuroimaging community - it also has a number of pitfalls and unresolved challenges which can, if not approached with due caution, undermine the explanatory potential of the endeavor. We review these pitfalls, the prevailing solutions to overcome them, and the challenges at the forefront of the field.
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Methods of constructing graphs from diverse neuroimaging modalities share many similar concepts, but with subtle differences that depend on the nature of the data.
“Methods of constructing graphs from diverse neuroimaging modalities share many similar concepts (Fig. 4), but with subtle differences that depend on the nature of the data.19,37”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [37]
Thresholding connectomes is necessary for removing false-positive edges and facilitating between-group comparisons but can remove potentially valid information regarding differences in network topology.
“Thresholding connectomes is necessary for removing false-positive edges and facilitating between-group comparisons but can remove potentially valid information regarding differences in network topology (Fornito, Zalesky, & Breakspear, 2013; van Wijk, Stam, & Daffertshofer, 2010).”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Fornito, Zalesky, & Breakspear, 2013
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135 · see citing works
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324 · see citing works
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783 · see citing works
The structure of the nervous system of the nematode <i>Caenorhabditis elegans</i>
White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1986.0056 · Cited by 5,589 · cited 2× · see citing works
“Corresponding author: A/Prof. Alex Fornito Monash Biomedical Imaging 770 Blackburn Rd Clayton, Victoria, Australia 3168 Tel: +61 3 8344 1876 Fax: +61 3 9348 0469 Email: alex.fornito@monash.edu AC C EP TE D M AN U SC R IP T”
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173 · see citing works
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124 · see citing works
Network-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750 · see citing works
Self-citationWhite matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
Jones, D. K., Knösche, T. R., & Turner, R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.081 · Cited by 2,479 · see citing works
Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.036 · Cited by 2,387 · see citing works
Competition between functional brain networks mediates behavioral variability
Kelly, A. M. C., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.08.008 · Cited by 1,287 · see citing works
Efficiency of Functional Brain Networks and Intellectual Performance
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1443-09.2009 · Cited by 1,078 · see citing works
The anatomical basis of functional localization in the cortex
Passingham, R. E., Stephan, K. E., & Kötter, R. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn893 · Cited by 1,024 · see citing works
Sonuga-Barke, E. J. S., & Castellanos, F. X. (2007) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2007.02.005 · Cited by 890 · see citing works
Wiring specificity in the direction-selectivity circuit of the retina
Briggman, K. L., Helmstaedter, M., & Denk, W. (2011) · Nature · doi:10.1038/nature09818 · Cited by 883 · see citing works
Adaptive reconfiguration of fractal small-world human brain functional networks
Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T., & Bullmore, E. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606005103 · Cited by 814 · see citing works
Cortical folding patterns and predicting cytoarchitecture
Fischl, B., Rajendran, N., Busa, E., Augustinack, J., Hinds, O., Yeo, B. T. T., Mohlberg, H., Amunts, K., & Zilles, K. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhm225 · Cited by 737 · see citing works
Identifying neural drivers with functional MRI: an electrophysiological validation
David, O., Guillemain, I., Saillet, S., Reyt, S., Deransart, C., Segebarth, C., & Depaulis, A. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060315 · Cited by 533 · see citing works
Broadband criticality of human brain network synchronization
Kitzbichler, M. G., Smith, M. L., Christensen, S. R., & Bullmore, E. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000314 · Cited by 527 · see citing works
Functional connectivity magnetic resonance imaging classification of autism
Anderson, J. S., Nielsen, J. A., Froehlich, A. L., DuBray, M. B., Druzgal, T. J., Cariello, A. N., Cooperrider, J. R., Zielinski, B. A., Ravichandran, C., Fletcher, P. T., et al. (2011) · Brain · doi:10.1093/brain/awr263 · Cited by 405 · see citing works
Dynamical consequences of lesions in cortical networks
Honey, C. J., & Sporns, O. (2008) · Human Brain Mapping · doi:10.1002/hbm.20579 · Cited by 353 · see citing works
Connectivity differences in brain networks
Zalesky, A., Cocchi, L., Fornito, A., Murray, M. M., & Bullmore, E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.068 · Cited by 260 · see citing works
Self-citationZilles, K. (2002) · European Neuropsychopharmacology · doi:10.1016/s0924-977x(02)00108-6 · Cited by 244 · see citing works
Exploring the Morphospace of Communication Efficiency in Complex Networks
Goñi, J., Avena-Koenigsberger, A., Velez de Mendizabal, N., van den Heuvel, M. P., Betzel, R. F., & Sporns, O. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058070 · Cited by 158 · see citing works
A DTI-Derived Measure of Cortico-Cortical Connectivity
Zalesky, A., & Fornito, A. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2012113 · Cited by 141 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx179 · Cited by 3,657 · see citing works
Network hubs in the human brain
van den Heuvel, M. P., & Sporns, O. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.012 · Cited by 2,123 · see citing works
The connectomics of brain disorders
Fornito, A., Zalesky, A., & Breakspear, M. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3901 · Cited by 1,734 · see citing works
Self-citationFunctional connectomics from resting-state fMRI
Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., et al. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.016 · Cited by 952 · see citing works
Rethinking segregation and integration: contributions of whole-brain modelling
Deco, G., Tononi, G., Boly, M., & Kringelbach, M. L. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3963 · Cited by 658 · see citing works
Brain networks and cognitive architectures
Petersen, S. E., & Sporns, O. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.027 · Cited by 553 · see citing works
Dynamic cooperation and competition between brain systems during cognitive control
Cocchi, L., Zalesky, A., Fornito, A., & Mattingley, J. B. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.08.006 · Cited by 429 · see citing works
Self-citationvan den Heuvel, M. P., Bullmore, E. T., & Sporns, O. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.001 · Cited by 328 · see citing works
Mathematical framework for large-scale brain network modeling in The Virtual Brain
Sanz-Leon, P., Knock, S. A., Spiegler, A., & Jirsa, V. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.002 · Cited by 356 · see citing works
Towards quantitative connectivity analysis: reducing tractography biases
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.074 · Cited by 324 · see citing works
Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302 · see citing works
“giving rise to the new fields of connectomics and network neuroscience [149, 150, 32, 148].”
BackgroundStructural network efficiency is associated with cognitive impairment in small-vessel disease
Lawrence, A. J., Chung, A. W., Morris, R. G., Markus, H. S., & Barrick, T. R. (2014) · Neurology · doi:10.1212/wnl.0000000000000612 · Cited by 285 · see citing works
Connectome sensitivity or specificity: which is more important?
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., van den Heuvel, M. P., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.035 · Cited by 275 · see citing works
Self-citationThe human connectome in Alzheimer disease — relationship to biomarkers and genetics
Yu, M., Sporns, O., & Saykin, A. J. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00529-1 · Cited by 265 · see citing works
The contribution of geometry to the human connectome
Roberts, J. A., Perry, A., Lord, A. R., Roberts, G., Mitchell, P. B., Smith, R. E., Calamante, F., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.009 · Cited by 213 · see citing works
Self-citationDevelopmental changes in brain network hub connectivity in late adolescence
Baker, S. T. E., Lubman, D. I., Yucel, M., Allen, N. B., Whittle, S., Fulcher, B. D., Zalesky, A., & Fornito, A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5043-14.2015 · Cited by 168 · see citing works
Self-citationStructural network alterations and neurological dysfunction in cerebral amyloid angiopathy
Reijmer, Y. D., Fotiadis, P., Martinez-Ramirez, S., Salat, D. H., Schultz, A., Shoamanesh, A., Ayres, A. M., Vashkevich, A., Rosas, D., Schwab, K., et al. (2015) · Brain · doi:10.1093/brain/awu316 · Cited by 181 · see citing works
Mapping Structural Connectivity Using Diffusion <scp>MRI</scp> : Challenges and Opportunities
Yeh, C., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27188 · Cited by 187 · see citing works
Drakesmith, M., Caeyenberghs, K., Dutt, A., Lewis, G., David, A. S., & Jones, D. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.011 · Cited by 157 · see citing works
Prefrontal connectomics: From anatomy to human imaging
Haber, S. N., Liu, H., Seidlitz, J., & Bullmore, E. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01156-6 · Cited by 129 · see citing works
Changing Brain Networks Through Non-invasive Neuromodulation
To, W. T., De Ridder, D., Hart Jr., J., & Vanneste, S. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00128 · Cited by 135 · see citing works
“Effective connectivity refers to the directional flow of information or the causal relationships between (Friston, 2011; Shafi et al., 2012; Fornito et al., 2013; Luft et al., 2014) nodes, whereas functional connectivity simply computes measures of statistical independence (correlation) between…”
BackgroundInfluentialDynamics of the human structural connectome underlying working memory training
Caeyenberghs, K., Metzler-Baddeley, C., Foley, S., & Jones, D. K. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.1973-15.2016 · Cited by 91 · see citing works
Myung, W., Han, C. E., Fava, M., Mischoulon, D., Papakostas, G. I., Heo, J.-Y., Kim, K. W., Kim, S. T., Kim, D. J. H., Kim, D. K., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.110 · Cited by 101 · see citing works
Panigrahy, A., Schmithorst, V. J., Wisnowski, J. L., Watson, C. G., Bellinger, D. C., Newburger, J. W., & Rivkin, M. J. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.01.013 · Cited by 86 · see citing works
Richmond, S., Johnson, K. A., Seal, M. L., Allen, N. B., & Whittle, S. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.08.024 · Cited by 70 · see citing works
Hart, M. G., Ypma, R. J. F., Romero-Garcia, R., Price, S. J., & Suckling, J. (2016) · Journal of Neurosurgery · doi:10.3171/2015.4.jns142683 · Cited by 74 · see citing works
Lohse, C., Bassett, D. S., Lim, K. O., & Carlson, J. M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003712 · Cited by 62 · see citing works
Sinke, M. R. T., Otte, W. M., Christiaens, D., Schmitt, O., Leemans, A., van der Toorn, A., Sarabdjitsingh, R. A., Joëls, M., & Dijkhuizen, R. M. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1628-y · Cited by 70 · see citing works
Functional connectivity and the sleep-deprived brain
Chee, M. W. L., & Zhou, J. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2019.02.009 · Cited by 68 · see citing works
Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease
Mårtensson, G., Pereira, J. B., Mecocci, P., Vellas, B., Tsolaki, M., Kłoszewska, I., Soininen, H., Lovestone, S., Simmons, A., Volpe, G., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-29927-0 · Cited by 60 · see citing works
Sedeño, L., Piguet, O., Abrevaya, S., Desmaras, H., García‐Cordero, I., Baez, S., Alethia de la Fuente, L., Reyes, P., Tu, S., Moguilner, S., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23627 · Cited by 55 · see citing works
Global Network Organization of the Fetal Functional Connectome
De Asis-Cruz, J., Andersen, N., Kapse, K., Khrisnamurthy, D., Quistorff, J., Lopez, C., Vezina, G., & Limperopoulos, C. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhaa410 · Cited by 40 · see citing works
Kesler, S. R., Acton, P., Rao, V., & Ray, W. J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00048 · Cited by 34 · see citing works
Dynamic coupling of complex brain networks and dual-task behavior
Alavash, M., Thiel, C. M., & Gießing, C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.028 · Cited by 25 · see citing works
- Authors
- Fornito, A., Zalesky, A., & Breakspear, M.
- Affiliations
- The University of Melbourne (AU)
- Black Dog Institute (AU)
- Corresponding author
- Fornito, Alex
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 80
- Pages
- 426-444
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2146406922
- Cited by
- 743
- References
- 26
- Citations by year
- 2026: 21 · 2025: 39 · 2024: 36 · 2023: 55 · 2022: 55 · 2021: 68 · 2020: 80 · 2019: 57 · 2018: 70 · 2017: 77 · 2016: 72 · 2015: 49 · 2014: 49 · 2013: 14
- FWCI
- 5.21
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Funders
- National Health and Medical Research Council (JSMF22002082, 1050504)
- Australian Research Council
- Monash University
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- human connectome, brain networks, graph theory, graph analysis, structural connectivity, functional connectivity, causal connectivity, network topology, connectomics, neuroimaging
- MeSH terms
- Algorithms, Brain, Humans, Models, Anatomic, Models, Neurological, Nerve Net, Connectome
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