Mapping Structural Connectivity Using Diffusion MRI : Challenges and Opportunities

Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single subject brain

Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319 · see citing works

Advances in functional and structural MR image analysis and implementation as FSL

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630 · see citing works

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

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

Complex brain networks: graph theoretical analysis of structural and functional systems

Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820 · see citing works

Complex network measures of brain connectivity: Uses and interpretations

Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522 · see citing works

Complex networks: structure and dynamics

BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010 · see citing works

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182 · see citing works

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628 · see citing works

The organization of the human cerebellum estimated by intrinsic functional connectivity

Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304 · see citing works

The WU-Minn Human Connectome Project: An overview

Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651 · see citing works

The minimal preprocessing pipelines for the Human Connectome Project

Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253 · see citing works

Efficient behavior of small-world networks

Latora, V., & Marchiori, M. (2001) · Physical Review Letters · doi:10.1103/physrevlett.87.198701 · Cited by 5,119 · see citing works

A multi-modal parcellation of human cerebral cortex

Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., et al. (2016) · Nature · doi:10.1038/nature18933 · Cited by 5,111 · see citing works

Automatically Parcellating the Human Cerebral Cortex

Fischl, B. R. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg087 · Cited by 4,208 · see citing works

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

A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data

Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., & Zilles, K. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.034 · Cited by 4,033 · see citing works

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

Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo

Basser, P. J., Mattiello, J., & Bihan, D. L. (1994) · Journal of Magnetic Resonance Series B · doi:10.1006/jmrb.1994.1037 · Cited by 3,656 · see citing works

Functional and Effective Connectivity: A Review

Friston, K. J. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0008 · Cited by 3,415 · see citing works

NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain

Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.072 · Cited by 3,288 · see citing works

The Human Connectome: A Structural Description of the Human Brain

Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260 · see citing works

MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation

Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116137 · Cited by 2,974 · see citing works

Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature

Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.010 · Cited by 2,956 · see citing works

Small-World Brain Networks

Bassett, D. S., & Bullmore, E. (2006) · The Neuroscientist · doi:10.1177/1073858406293182 · Cited by 2,600 · see citing works

White 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

Rich-club organization of the human connectome

van den Heuvel, M. P., & Sporns, O. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3539-11.2011 · Cited by 2,354 · see citing works

Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution

Tournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191 · 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

Structural and Functional Brain Networks: From Connections to Cognition

Park, H.-J., & Friston, K. (2013) · Science · doi:10.1126/science.1238411 · Cited by 2,106 · see citing works

Organization, development and function of complex brain networks

SPORNS, O., CHIALVO, D., KAISER, M., & HILGETAG, C. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.07.008 · Cited by 2,055 · see citing works

Fiber tracking: principles and strategies – a technical review

Mori, S., & van Zijl, P. C. M. (2002) · NMR in Biomedicine · doi:10.1002/nbm.781 · Cited by 2,019 · see citing works

Tracking neuronal fiber pathways in the living human brain

Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., McKinstry, R. C., Burton, H., & Raichle, M. E. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.10422 · Cited by 1,774 · 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

Virtual in vivo interactive dissection of white matter fasciculi in the human brain

Catani, M., Howard, R. J., Pajevic, S., & Jones, D. K. (2002) · NeuroImage · doi:10.1006/nimg.2002.1136 · Cited by 1,637 · see citing works

The human connectome: a complex network

Sporns, O. (2011) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2010.05888.x · Cited by 1,399 · see citing works

Tractography-based connectomes are dominated by false-positive connections

Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,361 · see citing works

Modular brain networks

Sporns, O., & Betzel, R. F. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033634 · Cited by 1,334 · see citing works

Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information

Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.005 · Cited by 1,216 · see citing works

Whole-brain anatomical networks: does the choice of nodes matter?

Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.027 · Cited by 1,169 · see citing works

The multiple traveling salesman problem: an overview of formulations and solution procedures

Bektaş, T. (2005) · Omega · doi:10.1016/j.omega.2004.10.004 · Cited by 1,157 · see citing works

Brain Graphs: Graphical Models of the Human Brain Connectome

Bullmore, E. T., & Bassett, D. S. (2011) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-040510-143934 · Cited by 1,130 · see citing works

Graph theoretical analysis of complex networks in the brain

Stam, C. J., & Reijneveld, J. C. (2007) · Nonlinear Biomedical Physics · doi:10.1186/1753-4631-1-3 · Cited by 1,036 · see citing works

Diffusion tensor imaging and beyond

Tournier, J., Mori, S., & Leemans, A. (2011) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22924 · Cited by 921 · see citing works

Human brain networks in health and disease

Bassett, D. S., & Bullmore, E. T. (2009) · Current Opinion in Neurology · doi:10.1097/wco.0b013e32832d93dd · Cited by 894 · see citing works

Advances in diffusion MRI acquisition and processing in the Human Connectome Project

Sotiropoulos, S. N., Jbabdi, S., Xu, J., Andersson, J., Moeller, S., Auerbach, E. J., Glasser, M. F., Hernandez, M., Sapiro, G., Jenkinson, M., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.057 · Cited by 872 · see citing works

A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex

Markov, N. T., Ercsey-Ravasz, M. M., Ribeiro Gomes, A. R., Lamy, C., Magrou, L., Vezoli, J., Misery, P., Falchier, A., Quilodran, R., Gariel, M. A., et al. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs270 · Cited by 857 · see citing works

Small-World Brain Networks Revisited

Bassett, D. S., & Bullmore, E. T. (2016) · The Neuroscientist · doi:10.1177/1073858416667720 · Cited by 847 · see citing works

Mapping Human Whole-Brain Structural Networks with Diffusion MRI

Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V. J., Meuli, R., & Thiran, J.-P. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000597 · Cited by 788 · see citing works

SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography

Smith, R. E., Tournier, J., Calamante, F., & Connelly, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.092 · Cited by 758 · see citing works

SIFT: Spherical-deconvolution informed filtering of tractograms

Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.049 · Cited by 752 · see citing works

Graph analysis of the human connectome: Promise, progress, and pitfalls

Fornito, A., Zalesky, A., & Breakspear, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.087 · Cited by 743 · see citing works

Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited

Thomas, C., Ye, F. Q., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405672111 · Cited by 725 · see citing works

Graph theoretical modeling of brain connectivity

Yong He, A., & Evans, A. C. (2010) · Current Opinion in Neurology · doi:10.1097/wco.0b013e32833aa567 · Cited by 704 · see citing works

Cortical high-density counterstream architectures

Markov, N. T., Ercsey-Ravasz, M., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013) · Science · doi:10.1126/science.1238406 · Cited by 616 · see citing works

Tractography: Where Do We Go from Here?

Jbabdi, S., & Johansen-Berg, H. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0033 · Cited by 614 · see citing works

Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data

Tournier, J., Yeh, C., Calamante, F., Cho, K.-H., Connelly, A., & Lin, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.002 · Cited by 591 · see citing works

Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images

Raffelt, D. A., Tournier, J., Rose, S., Ridgway, G. R., Henderson, R. D., Crozier‬, ‪., Salvado, O., & Connelly, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.045 · Cited by 579 · see citing works

Diffusion MRI fiber tractography of the brain

Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3785 · Cited by 574 · cited 1× · see citing works

Imaging cortical association tracts in the human brain using diffusion‐tensor‐based axonal tracking

Mori, S., Kaufmann, W. E., Davatzikos, C. A., Stieltjes, B., Amodei, L., Fredericksen, K., Pearlson, G. D., Melhem, E. R., Solaiyappan, M., Raymond, G. V., et al. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10074 · Cited by 558 · see citing works

The human connectome: Origins and challenges

Sporns, O. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.023 · Cited by 434 · see citing works

Imaging human connectomes at the macroscale

Craddock, R. C., Jbabdi, S., Yan, C.-G., Vogelstein, J. T., Castellanos, F. X., Di Martino, A., Kelly, C., Heberlein, K., Colcombe, S., & Milham, M. P. (2013) · Nature Methods · doi:10.1038/nmeth.2482 · Cited by 419 · see citing works

Weight consistency specifies regularities of macaque cortical networks

Markov, N. T., Misery, P., Falchier, A., Lamy, C., Vezoli, J., Quilodran, R., Gariel, M. A., Giroud, P., Ercsey-Ravasz, M., Pilaz, L. J., et al. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq201 · Cited by 386 · see citing works

Conserved and variable architecture of human white matter connectivity

Bassett, D. S., Brown, J. A., Deshpande, V. S., Carlson, J. M., & Grafton, S. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.006 · 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

Challenges and limitations of quantifying brain connectivity <i>in vivo</i> with diffusion MRI

Jones, D. K. (2010) · Imaging in Medicine · doi:10.2217/iim.10.21 · Cited by 321 · see citing works

Global fiber reconstruction becomes practical

Reisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., & Kiselev, V. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.016 · Cited by 312 · see citing works

Harnessing reliability for neuroscience research

Zuo, X.-N., Xu, T., & Milham, M. P. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0655-x · Cited by 310 · see citing works

Fibre-specific white matter reductions in Alzheimer’s disease and mild cognitive impairment

Mito, R., Raffelt, D., Dhollander, T., Vaughan, D. N., Tournier, J.-D., Salvado, O., Brodtmann, A., Rowe, C. C., Villemagne, V. L., & Connelly, A. (2018) · Brain · doi:10.1093/brain/awx355 · Cited by 309 · see citing works

Structural connectomics in brain diseases

Griffa, A., Baumann, P., Thiran, J., & Hagmann, P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.056 · Cited by 306 · see citing works

Building connectomes using diffusion MRI: why, how and but

Sotiropoulos, S. N., & Zalesky, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3752 · Cited by 290 · see citing works

MR connectomics: Principles and challenges

Hagmann, P., Cammoun, L., Gigandet, X., Gerhard, S., Grant, P. E., Wedeen, V. J., Meuli, R. A., Thiran, J., Honey, C. J., & Sporns, O. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.01.014 · Cited by 271 · see citing works

The effects of SIFT on the reproducibility and biological accuracy of the structural connectome

Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.004 · Cited by 266 · see citing works

Evaluation and statistical inference for human connectomes

Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N., & Wandell, B. A. (2014) · Nature Methods · doi:10.1038/nmeth.3098 · Cited by 259 · see citing works

COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography

Daducci, A., Dal Palu, A., Lemkaddem, A., & Thiran, J.-P. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2352414 · Cited by 255 · see citing works

The role of long-range connections on the specificity of the macaque interareal cortical network

Markov, N. T., Ercsey-Ravasz, M., Lamy, C., Gomes, A. R. R., Magrou, L., Misery, P., Giroud, P., Barone, P., Dehay, C., Toroczkai, Z., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218972110 · Cited by 217 · see citing works

Specificity and robustness of long-distance connections in weighted, interareal connectomes

Betzel, R. F., & Bassett, D. S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720186115 · Cited by 214 · see citing works

Discrete neuroanatomical networks are associated with specific cognitive abilities in old age

Wen, W., Zhu, W., He, Y., Kochan, N. A., Reppermund, S., Slavin, M. J., Brodaty, H., Crawford, J., Xia, A., & Sachdev, P. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.4085-10.2011 · Cited by 204 · see citing works

The Mouse Cortical Connectome, Characterized by an Ultra-Dense Cortical Graph, Maintains Specificity by Distinct Connectivity Profiles

Gămănuț, R., Kennedy, H., Toroczkai, Z., Ercsey-Ravasz, M., Essen, D. C. V., Knoblauch, K., & Burkhalter, A. (2018) · Neuron · doi:10.1016/j.neuron.2017.12.037 · Cited by 202 · see citing works

Human cortical connectome reconstruction from diffusion weighted MRI: The effect of tractography algorithm

Bastiani, M., Shah, N. J., Goebel, R., & Roebroeck, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.002 · Cited by 192 · see citing works

Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data

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

Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model

Christiaens, D., Reisert, M., Dhollander, T., Sunaert, S., Suetens, P., & Maes, F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.008 · Cited by 152 · see citing works

Asymmetry of subinsular anisotropy by in vivo diffusion tensor imaging

Cao, Y., Whalen, S., Huang, J., Berger, K. L., & DeLano, M. C. (2003) · Human Brain Mapping · doi:10.1002/hbm.10130 · Cited by 142 · see citing works

Test–retest reliability of structural brain networks from diffusion MRI

Buchanan, C. R., Pernet, C. R., Gorgolewski, K. J., Storkey, A., & Bastin, M. E. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.09.054 · Cited by 140 · see citing works

Microstructure informed tractography: pitfalls and open challenges

Daducci, A., Dal Palú, A., Descoteaux, M., & Thiran, J.-P. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00247 · Cited by 138 · see citing works

Characteristics and variability of structural networks derived from diffusion tensor imaging

Cheng, H., Wang, Y., Sheng, J., Kronenberger, W. G., Mathews, V. P., Hummer, T. A., & Saykin, A. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.036 · Cited by 122 · see citing works

Surface-enhanced tractography (SET)

St‐Onge, E., Daducci, A., Girard, G., & Descoteaux, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.036 · Cited by 106 · see citing works

Common misconceptions, hidden biases and modern challenges of dMRI tractography

Rheault, F., Poulin, P., Caron, A. V., St‐Onge, E., & Descoteaux, M. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab6aad · Cited by 105 · see citing works

Micro-, Meso- and Macro-Connectomics of the Brain

Kennedy, H., Essen, D. C. V., & Christen, Y. (2016) · Research and perspectives in neurosciences · doi:10.1007/978-3-319-27777-6 · Cited by 98 · see citing works

The effects of connection reconstruction method on the interregional connectivity of brain networks via diffusion tractography

Li, L., Rilling, J. K., Preuss, T. M., Glasser, M. F., & Hu, X. P. (2011) · Human Brain Mapping · doi:10.1002/hbm.21332 · Cited by 98 · see citing works

Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI?

Civier, O., Smith, R. E., Yeh, C.-H., Connelly, A., & Calamante, F. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.039 · Cited by 95 · see citing works

The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking

Calamante, F. (2019) · Diagnostics · doi:10.3390/diagnostics9030115 · Cited by 92 · see citing works

Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics

Yeh, C., Smith, R. E., Liang, X., Calamante, F., & Connelly, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.047 · Cited by 80 · see citing works

MicroTrack: An Algorithm for Concurrent Projectome and Microstructure Estimation

Sherbondy, A. J., Rowe, M. C., & Alexander, D. C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15705-9_23 · Cited by 75 · see citing works

The structural connectome in traumatic brain injury: A meta-analysis of graph metrics

Imms, P. E., Clemente, A., Cook, M. J., D'Souza, W. J., Wilson, P. H., Jones, D. K., & Caeyenberghs, K. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.01.002 · Cited by 74 · see citing works

A framework based on spin glass models for the inference of anatomical connectivity from diffusion‐weighted MR data – a technical review

Mangin, J., Poupon, C., Cointepas, Y., Rivière, D., Orfanos, D. P., Clark, C., Régis, J. M., & Bihan, D. L. (2002) · NMR in Biomedicine · doi:10.1002/nbm.780 · Cited by 73 · see citing works

Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks

Roine, T., Jeurissen, B., Perrone, D., Aelterman, J., Philips, W. R., Sijbers, J., & Leemans, A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.10.009 · Cited by 63 · see citing works

Network over‐connectivity differentiates autism spectrum disorder from other developmental disorders in toddlers: A diffusion MRI study

Conti, E., Mitra, J., Calderoni, S., Pannek, K., Shen, K., Pagnozzi, A. M., Rose, S., Mazzotti, S., Scelfo, D., Tosetti, M., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23520 · Cited by 62 · see citing works

Ax<scp>T</scp>ract: Toward microstructure informed tractography

Girard, G., Daducci, A., Petit, L., Thiran, J., Whittingstall, K., Deriche, R., Wassermann, D., & Descoteaux, M. (2017) · Human Brain Mapping · doi:10.1002/hbm.23741 · Cited by 62 · see citing works

The anatomy of reliability: a must read for future human brain mapping

Xing, X.-X., & Zuo, X. (2018) · 中国科学通报：英文版 · doi:10.1016/j.scib.2018.12.010 · Cited by 57 · see citing works

Test-Retest Reliability of Graph Theory Measures of Structural Brain Connectivity

Dennis, E. L., Jahanshad, N., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Wright, M. J., & Thompson, P. M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33454-2_38 · Cited by 49 · see citing works

Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease

Oxtoby, N. P., Garbarino, S., Firth, N. C., Warren, J. D., Schott, J. M., Alexander, D. C., & Initiative, A. D. N. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00580 · Cited by 46 · see citing works

Connectomes from streamlines tractography: Assigning streamlines to brain parcellations is not trivial but highly consequential

Yeh, C., Smith, R. E., Dhollander, T., Calamante, F., & Connelly, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.005 · Cited by 46 · see citing works

Why data coherence and quality is critical for understanding interareal cortical networks

Kennedy, H., Kenneth Knoblauch, B., & Toroczkai, Z. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.031 · Cited by 43 · see citing works

Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph

Dimitriadis, S. I., Drakesmith, M., Bells, S., Parker, G. D., Linden, D. E. J., & Jones, D. K. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00694 · Cited by 43 · see citing works

Optimization of graph construction can significantly increase the power of structural brain network studies

Messaritaki, E., Dimitriadis, S. I., & Jones, D. K. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.052 · Cited by 39 · see citing works

Tract‐specific atrophy in focal epilepsy: Disease, genetics, or seizures?

Vaughan, D. N., Raffelt, D. A., Curwood, E. K., Tsai, M., Tournier, J., Connelly, A., & Jackson, G. D. (2016) · Annals of Neurology · doi:10.1002/ana.24848 · Cited by 39 · see citing works

Reproducibility of graph metrics of human brain structural networks

Duda, J., Cook, P. A., & Gee, J. C. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00046 · Cited by 38 · see citing works

MR g-ratio-weighted connectome analysis in patients with multiple sclerosis

Kamagata, K., Zalesky, A., Yokoyama, K., Andica, C., Hagiwara, A., Shimoji, K., Kumamaru, K. K., Takemura, M. Y., Hoshino, Y., Kamiya, K., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-50025-2 · Cited by 35 · see citing works

Addressing the Path-Length-Dependency Confound in White Matter Tract Segmentation

Liptrot, M. G., Sidaros, K., & Dyrby, T. B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096247 · Cited by 31 · see citing works

Repeatability Analysis of Global and Local Metrics of Brain Structural Networks

Andreotti, J., Jann, K., Melie‐García, L., Giezendanner, S., Dierks, T., & Federspiel, A. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0202 · Cited by 20 · see citing works

Reducing false positives in tractography with microstructural and anatomical priorsPreprint

Schiavi, S., Barakovic, M., Ocampo-Pineda, M., Descoteaux, M., Thiran, J.-P., & Daducci, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/608349 · Cited by 18 · see citing works

Robust Identification of Rich-Club Organization in Weighted and Dense Structural Connectomes

Liang, X., Yeh, C., Connelly, A., & Calamante, F. (2018) · Brain Topography · doi:10.1007/s10548-018-0661-8 · Cited by 9 · see citing works

The Brain in Space -- Micro-, Meso- and Macro-Connectomics of the Brain

Kennedy, H., Essen, D. C. V., & Christen, Y. (2016) · Cited by 2 · see citing works

Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review

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

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works

Sadeghi, D., Shoeibi, A., Ghassemi, N., Moridian, P., Khadem, A. A., Alizadehsani, R., Teshnehlab, M., Górriz, J. M., Khozeimeh, F., Zhang, Y., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105554 · Cited by 171 · see citing works

Gut bless you: The microbiota-gut-brain axis in irritable bowel syndrome

Hillestad, E. M. R., van der Meeren, A., Nagaraja, B. H., Bjørsvik, B. R., Haleem, N., Benítez‐Páez, A., Sanz, Y., Hausken, T., Lied, G. A., Lundervold, A., et al. (2022) · World Journal of Gastroenterology · doi:10.3748/wjg.v28.i4.412 · Cited by 145 · 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

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Moridian, P., Ghassemi, N., Jafari, M., Salloum‐Asfar, S., Sadeghi, D., Khodatars, M., Shoeibi, A., Khosravi, A., Ling, S. H., Subaşı, A., et al. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.999605 · Cited by 121 · see citing works

Brain simulation as a cloud service: The Virtual Brain on EBRAINS

Schirner, M., Domide, L., Perdikis, D., Triebkorn, P., Stefanovski, L., Pai, R. K., Prodan, P., Valean, B., Palmer, J., Langford, C., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118973 · Cited by 113 · see citing works

Edges in brain networks: Contributions to models of structure and function

Faskowitz, J., Betzel, R. F., & Sporns, O. (2021) · Network Neuroscience · doi:10.1162/netn_a_00204 · Cited by 102 · see citing works

Diffusion MRI tractography for neurosurgery: The basics, current state, technical reliability and challenges

Yang, J. Y.-M., Yeh, C.-H., Poupon, C., & Calamante, F. (2021) · Physics in Medicine &amp; Biology · doi:10.1088/1361-6560/ac0d90 · Cited by 80 · see citing works

Enhancing precision in human neuroscience

Nebe, S., Reutter, M., Baker, D. H., Bölte, J., Domes, G., Gamer, M., Gärtner, A., Gießing, C., Gurr, C., Hilger, K., et al. (2023) · eLife · doi:10.7554/elife.85980 · Cited by 75 · see citing works

A spatiotemporal complexity architecture of human brain activity

Krohn, S., von Schwanenflug, N., Waschke, L., Romanello, A., Gell, M., Garrett, D. D., & Finke, C. (2023) · Science Advances · doi:10.1126/sciadv.abq3851 · Cited by 65 · see citing works

Personalizing Repetitive Transcranial Magnetic Stimulation Parameters for Depression Treatment Using Multimodal Neuroimaging

Klooster, D., Ferguson, M. A., Boon, P., & Baeken, C. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.11.004 · Cited by 58 · see citing works

Brain atrophy in prodromal synucleinopathy is shaped by structural connectivity and gene expression

Rahayel, S., Tremblay, C., Vo, A., Zheng, Y. Q., Lehéricy, S., Arnulf, I., Vidailhet, M., Corvol, J., ICEBERG Study Group, Vidailhet, M., et al. (2022) · Brain · doi:10.1093/brain/awac187 · Cited by 55 · see citing works

The whole-brain connectome landscape in patients with schizophrenia: A systematic review and meta-analysis of graph theoretical characteristics

Gao, Z., Xiao, Y., Zhu, F., Tao, B., Yu, W., & Lui, S. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105144 · Cited by 47 · see citing works

Imaging Alzheimer's genetic risk using diffusion MRI: A systematic review

Harrison, J. R., Bhatia, S., Tan, Z. X., Mirza-Davies, A., Benkert, H., Tax, C. M. W., & Jones, D. K. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102359 · Cited by 48 · see citing works

Filtering in tractography using autoencoders (FINTA)

Legarreta, J. H., Petit, L., Rheault, F., Theaud, G., Lemaire, C., Descoteaux, M., & Jodoin, P. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102126 · Cited by 49 · see citing works

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence

Liang, X., Sun, L., Liao, X., Lei, T., Xia, M., Duan, D., Zeng, Z., Li, Q., Xu, Z., Men, W., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-44863-6 · Cited by 42 · see citing works

Statistical Learning Methods for Neuroimaging Data Analysis with Applications

Hongtu Zhu, B., Li, T., & Zhao, B. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-100353 · Cited by 41 · see citing works

Dynamic primitives of brain network interaction

Schirner, M., Kong, X., Yeo, B. T. T., Deco, G., & Ritter, P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118928 · Cited by 42 · see citing works

The Multilayer Network Approach in the Study of Personality Neuroscience

Brooks, D., Hulst, H. E., de Bruin, L., Glas, G. G., Geurts, J. J. G., & Douw, L. (2020) · Brain Sciences · doi:10.3390/brainsci10120915 · Cited by 40 · see citing works

Navigating the link between processing speed and network communication in the human brain

Imms, P., Domínguez D, J. F., Burmester, A., Seguin, C., Clemente, A., Dhollander, T., Wilson, P. H., Poudel, G., & Caeyenberghs, K. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02241-8 · Cited by 39 · see citing works

Displaying the autonomic processing network in humans – a global tractography approach

Reisert, M., Weiller, C., & Hosp, J. A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117852 · Cited by 37 · see citing works

Contribution of animal models toward understanding resting state functional connectivity

Pais‐Roldán, P., Matéo, C., Pan, W., Acland, B. T., Kleinfeld, D., Snyder, L. H., Yu, X., & Keilholz, S. D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118630 · Cited by 37 · see citing works

Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels

Domhof, J. W. M., Jung, K., Eickhoff, S. B., & Popovych, O. V. (2021) · Network Neuroscience · doi:10.1162/netn_a_00202 · Cited by 36 · see citing works

Signed graph representation learning for functional-to-structural brain network mapping

Tang, H., Guo, L., Fu, X., Wang, Y., Mackin, S. R., Ajilore, O., Leow, A., Thompson, P. M., Huang, H., & Zhan, L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102674 · Cited by 36 · see citing works

Insomnia Subtypes Have Differentiating Deviations in Brain Structural Connectivity

Bresser, T., Blanken, T. F., de Lange, S. C., Leerssen, J., Foster‐Dingley, J. C., Lakbila‐Kamal, O., Wassing, R., Ramautar, J. R., Stoffers, D., van den Heuvel, M. P., et al. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.06.014 · Cited by 32 · see citing works

Structural white matter properties and cognitive resilience to tau pathology

Qiu, T., Liu, Z., Rheault, F., Legarreta, J. H., Caron, A. V., St‐Onge, F., Strikwerda‐Brown, C., Metz, A., Dadar, M., Soucy, J., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13776 · Cited by 30 · see citing works

Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition

Kurokawa, R., Kamiya, K., Koike, S., Nakaya, M., Uematsu, A., Tanaka, S., Kamagata, K., Okada, N., Morita, K., Kasai, K., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118675 · Cited by 30 · see citing works

Cognitive resilience depends on white matter connectivity: The Maastricht Study

DeJong, N. R., Jansen, J., van Boxtel, M. V., Schram, M., Stehouwer, C., Dagnelie, P., van der Kallen, C. V. D., Kroon, A., Wesselius, A., Koster, A., et al. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12758 · Cited by 26 · see citing works

Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome

Feng, G., Wang, Y., Huang, W., Chen, H., Dai, Z., Ma, G., Li, X., Zhang, Z., & Shu, N. (2022) · Human Brain Mapping · doi:10.1002/hbm.25883 · Cited by 28 · see citing works

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Bröhl, T., Rings, T., Pukropski, J., von Wrede, R. D., & Lehnertz, K. (2024) · Frontiers in Network Physiology · doi:10.3389/fnetp.2023.1338864 · Cited by 28 · see citing works

Graph Theoretical Analysis of Brain Network Characteristics in Brain Tumor Patients: A Systematic Review

Semmel, E. S., Quadri, T. R., & King, T. Z. (2021) · Neuropsychology Review · doi:10.1007/s11065-021-09512-5 · Cited by 26 · see citing works

Bundle myelin fraction (BMF) mapping of different white matter connections using microstructure informed tractography

Schiavi, S., Lu, P., Weigel, M., Lutti, A., Jones, D. K., Kappos, L., Granziera, C., & Daducci, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118922 · Cited by 24 · see citing works

Imaging versus electrographic connectivity in human mood-related fronto-temporal networks

Adkinson, J. A., Tsolaki, E. C., Sheth, S. A., Metzger, B. A., Robinson, M. E., Oswalt, D. N., McIntyre, C. C., Mathura, R. K., Waters, A. C., Allawala, A. B., et al. (2022) · Brain stimulation · doi:10.1016/j.brs.2022.03.002 · Cited by 24 · see citing works

Cerebral chemoarchitecture shares organizational traits with brain structure and function

Hänisch, B., Hansen, J. Y., Bernhardt, B. C., Eickhoff, S. B., Dukart, J., Mišić, B., & Valk, S. L. (2023) · eLife · doi:10.7554/elife.83843 · Cited by 23 · see citing works

An analysis-ready and quality controlled resource for pediatric brain white-matter research

Richie-Halford, A., Cieslak, M., Ai, L., Caffarra, S., Covitz, S. C., Franco, A. R., Karipidis, I. I., Kruper, J., Milham, M. P., Avelar‐Pereira, B., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01695-7 · Cited by 31 · see citing works

On time and space in the brain: A relativistic pseudo-diffusion framework

Le Bihan, D. (2020) · Brain Multiphysics · doi:10.1016/j.brain.2020.100016 · Cited by 30 · see citing works

Mapping effective connectivity by virtually perturbing a surrogate brain

Luo, Z., Peng, K., Liang, Z., Cai, S., Xu, C., Li, D., Hu, Y., Zhou, C., & Liu, Q. (2025) · Nature Methods · doi:10.1038/s41592-025-02654-x · Cited by 24 · see citing works

Diffusion MRI with machine learning

Karimi, D., & Warfield, S. K. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00353 · Cited by 23 · see citing works

Structural connectome construction using constrained spherical deconvolution in multi-shell diffusion-weighted magnetic resonance imaging

Tahedl, M., Tournier, J., & Smith, R. E. (2025) · Nature Protocols · doi:10.1038/s41596-024-01129-1 · Cited by 26 · see citing works

The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

Boshkovski, T., Kocarev, L., Cohen‐Adad, J., Mišić, B., Lehéricy, S., Stikov, N., & Mancini, M. (2021) · Network Neuroscience · doi:10.1162/netn_a_00179 · Cited by 23 · see citing works

Reliability and subject specificity of personalized whole-brain dynamical models

Domhof, J. W. M., Eickhoff, S. B., & Popovych, O. V. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119321 · Cited by 22 · see citing works

From Tumor to Network: Functional Connectome Heterogeneity and Alterations in Brain Tumors—A Multimodal Neuroimaging Narrative Review

Lozada, P. S. M., Pozo-Neira, J., & León-Rojas, J. E. (2025) · Cancers · doi:10.3390/cancers17132174 · Cited by 19 · see citing works

Advances in the fMRI analysis of the default mode network: a review

Sanz‐Morales, E., & Melero, H. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02888-z · Cited by 20 · see citing works

Bidirectional Mapping with Contrastive Learning on Multimodal Neuroimaging Data

Ye, K., Tang, H., Dai, S., Guo, L., Liu, J. Y., Wang, Y., Leow, A., Thompson, P. M., Huang, H., & Zhan, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43898-1_14 · Cited by 20 · see citing works

Analysis of Brain Structural Connectivity Networks and White Matter Integrity in Patients With Mild Cognitive Impairment

Bergamino, M., Schiavi, S., Daducci, A., Walsh, R. R., & Stokes, A. M. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.793991 · Cited by 19 · see citing works

Hierarchical microstructure informed tractography

Ocampo-Pineda, M., Schiavi, S., Rheault, F., Girard, G., Petit, L., Descoteaux, M., & Daducci, A. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0907 · Cited by 19 · see citing works

A simulated annealing algorithm for randomizing weighted networks

Milisav, F., Bazinet, V., Betzel, R. F., & Mišić, B. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00735-z · Cited by 17 · see citing works

Prenatal depressive symptoms are associated with altered structural brain networks in infants and moderated by infant sleep

Donnici, C., Tomfohr‐Madsen, L. M., Long, X., Manning, K. Y., Giesbrecht, G. F., & Lebel, C. A. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.06.054 · Cited by 17 · see citing works

Tutorial: a guide to diffusion MRI and structural connectomics

Shamir, I., & Assaf, Y. (2024) · Nature Protocols · doi:10.1038/s41596-024-01052-5 · Cited by 17 · see citing works

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Girard, G., Rafael‐Patiño, J., Truffet, R., Aydogan, D. B., Adluru, N., Nair, V. A., Prabhakaran, V., Bendlin, B. B., Alexander, A. L., Bosticardo, S., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120231 · Cited by 16 · see citing works

A critical review of connectome validation studies

Sarwar, T., Ramamohanarao, K., & Zalesky, A. (2021) · NMR in Biomedicine · doi:10.1002/nbm.4605 · Cited by 16 · see citing works

Assessment of structural disconnections in gliomas: comparison of indirect and direct approaches

Silvestri, E., Villani, U., Moretto, M., Colpo, M., Salvalaggio, A., Anglani, M., Castellaro, M., Facchini, S., Monai, E., D’Avella, D., et al. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02494-x · Cited by 15 · see citing works

Sex differences in cognition and structural covariance-based morphometric connectivity: evidence from 28,000+ UK Biobank participants

Yang, C. C., Totzek, J. F., Lepage, M., & Lavigne, K. M. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad286 · Cited by 15 · see citing works

Involvement of the cerebellum in structural connectivity enhancement in episodic migraine

Matoso, A., Fouto, A. R., Esteves, I., Ruiz‐Tagle, A., Caetano, G., da Silva, N. A., Vilela, P. F., Gil‐Gouveia, R., Nunes, R. G., & Figueiredo, P. (2024) · The Journal of Headache and Pain · doi:10.1186/s10194-024-01854-8 · Cited by 16 · see citing works

Multimodal fusion analysis of structural connectivity and gray matter morphology in migraine

Planchuelo‐Gómez, Á., García‐Azorín, D., Peral, Á. L. G., Aja‐Fernández, S., Rodríguez, M., & de Luis-Garcı́a, R. (2020) · Human Brain Mapping · doi:10.1002/hbm.25267 · Cited by 14 · see citing works

Network Analysis in Multiple Sclerosis and Related Disorders

Pontillo, G., Barrantes-Cepas, M., Broeders, T. A. A., Koubiyr, I., & Schoonheim, M. M. (2024) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2024.03.008 · Cited by 13 · see citing works

The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI

Moody, J. F., Adluru, N., Alexander, A. L., & Field, A. S. (2021) · Seminars in Ultrasound CT and MRI · doi:10.1053/j.sult.2021.07.007 · Cited by 12 · see citing works

Evaluation of tractogram filtering methods using human-like connectome phantoms

Sarwar, T., Ramamohanarao, K., Daducci, A., Schiavi, S., Smith, R. E., & Zalesky, A. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120376 · Cited by 13 · see citing works

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

Chen, Y., Zekelman, L., Zhang, C., Xue, T., Song, Y., Makris, N., Yogesh Rathi, A., Golby, A. J., Cai, W., Zhang, F., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103120 · Cited by 13 · see citing works

What Models and Tools can Contribute to a Better Understanding of Brain Activity?

Goodfellow, M., Andrzejak, R. G., Masoller, C., & Lehnertz, K. (2022) · Frontiers in Network Physiology · doi:10.3389/fnetp.2022.907995 · Cited by 13 · see citing works

Altered structural connectivity in olfactory disfunction after mild COVID-19 using probabilistic tractography

Bispo, D. D. D. C., Brandão, P. R. D. P., Pereira, D. A., Maluf, F. B., Dias, B. A., Paranhos, H. R., von Glehn, F., de Oliveira, A. C. P., Soares, A., Descoteaux, M., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-40115-7 · Cited by 13 · see citing works

Anatomically constrained tractography of the fetal brain

Calixto, C. A., Jaimes, C. E., Soldatelli, M. D., Warfield, S. K., Gholipour, A., & Karimi, D. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120723 · Cited by 12 · see citing works

Microstructure-Weighted Connectomics in Multiple Sclerosis

Bosticardo, S., Schiavi, S., Schaedelin, S. A., Lu, P., Baraković, M., Weigel, M., Kappos, L., Kühle, J., Daducci, A., & Granziera, C. (2021) · Brain Connectivity · doi:10.1089/brain.2021.0047 · Cited by 11 · see citing works

Eigenmodes of the brain: revisiting connectomics and geometryPreprint

Sina Mansour, L., Behjat, H., Ville, D. V. D., Smith, R. E., Yeo, B. T. T., & Zalesky, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.16.589843 · Cited by 11 · see citing works

From connectome to silicon: a biologically-inspired complex network of CMOS chaotic oscillators for analog brain emulation

Kuate, P. D. K., Ito, H., Fossi, J. T., Zhao, M., Fozin, T. F., Fotsin, H. B., Valdes-Sosa, P. A., Boccaletti, S., & Minati, L. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-11198-w · Cited by 9 · see citing works

Improved neuropathological identification of traumatic brain injury through quantitative neuroimaging and neural network analyses: Some practical approaches for the neurorehabilitation clinician

Bigler, E. D., & Allder, S. J. (2021) · Neurorehabilitation · doi:10.3233/nre-218023 · Cited by 10 · see citing works

Intra- and inter-hemispheric structural connectome in agenesis of the corpus callosum

Shi, M., Freitas, L. G. A., Spencer‐Smith, M. M., Kebets, V., Anderson, V. A., McIlroy, A., WOOD, A. G., Leventer, R. J., Ville, D. V. D., & Siffredi, V. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102709 · Cited by 10 · see citing works

Collegiate athlete brain data for white matter mapping and network neuroscience

Caron, B. A., Stuck, R., McPherson, B. C., Bullock, D. N., Kitchell, L. M., Faskowitz, J., Kellar, D., Cheng, H., Newman, S. D., Port, N., et al. (2021) · Scientific Data · doi:10.1038/s41597-021-00823-z · Cited by 9 · see citing works

Multiscale Brain Network Models and Their Applications in Neuropsychiatric Diseases

Lu, M., Guo, Z., Gao, Z., Cao, Y., & Fu, J. (2022) · Electronics · doi:10.3390/electronics11213468 · Cited by 9 · see citing works

Evaluation of human epileptic brain networks by constructing simplicial complexes

Li, Z., Li, X., Li, M., Zhang, K., Zhang, X., & Zhou, X. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.115699 · Cited by 9 · see citing works

Integrated diffusion image operator ( iDIO ): A pipeline for automated configuration and processing of diffusion MRI data

Hsu, C. H., Chong, S. T., Kung, Y., Kuo, K., Huang, C., & Lin, C. (2023) · Human Brain Mapping · doi:10.1002/hbm.26239 · Cited by 8 · see citing works

Expanding connectomics to the laminar level: A perspective

Shamir, I., & Assaf, Y. (2023) · Network Neuroscience · doi:10.1162/netn_a_00304 · Cited by 8 · see citing works

The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections

Trinkle, S., Foxley, S., Wildenberg, G., Kasthuri, N., & La Rivière, P. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118576 · Cited by 8 · see citing works

Structural MRI–based Computer-aided Diagnosis Models for Alzheimer Disease: Insights into Misclassifications and Diagnostic Limitations

Kang, X., Lin, J., Zhao, K., Yan, S., Chen, P., Wang, D., Yao, H., Zhou, B., Yu, C., Wang, P., et al. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.240508 · Cited by 8 · see citing works

A Virtual Clinical Trial of Psychedelics to Treat Patients With Disorders of Consciousness

Alnagger, N., Cardone, P., Martial, C., Perl, Y. S., Mindlin, I., Sitt, J. D., Roseman, L., Carhart-Harris, R. L., Nutt, D., Mallaroni, P., et al. (2025) · Advanced Science · doi:10.1002/advs.202511780 · Cited by 8 · see citing works

Brain connectomes in youth at risk for serious mental illness: an exploratory analysis

Metzak, P. D., Shakeel, M. K., Long, X., Lasby, M., de Souza, R. M., Bray, S., Goldstein, B. I., MacQUEEN, G. M., Wang, J., Kennedy, S. H., et al. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-04118-4 · Cited by 8 · see citing works

Investigating the impact of standard brain atlases and connectivity measures on the accuracy of ADHD detection from fMRI data using deep learning

Agarwal, S., Raj, A., Chowdhury, A., Aich, G., Chatterjee, R., & Ghosh, K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17962-7 · Cited by 9 · see citing works

Blurred streamlines: A novel representation to reduce redundancy in tractography

Gabusi, I., Battocchio, M., Bosticardo, S., Schiavi, S., & Daducci, A. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103101 · Cited by 8 · see citing works

Free Water-Corrected Fractional Anisotropy of the Fornix and Parahippocampal Cingulum Predicts Longitudinal Memory Change in Cognitively Healthy Older Adults

Hou, M., Bergamino, M., de Chastelaine, M., Sambamoorthy, S., & Rugg, M. D. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2024.04.005 · Cited by 8 · see citing works

Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study

Liang, X., Koh, C., Yeh, C., Goodin, P., Lamp, G., Connelly, A., & Carey, L. M. (2021) · Brain Sciences · doi:10.3390/brainsci11111388 · Cited by 8 · see citing works

A groupwise registration and tractography framework for cardiac myofiber architecture description by diffusion MRI: An application to the ventricular junctions

Magat, J., Yon, M., Bihan-Poudec, Y., & Ozenne, V. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271279 · Cited by 8 · see citing works

An open, analysis-ready, and quality controlled resource for pediatric brain white-matter researchPreprint

Richie-Halford, A., Cieslak, M., Ai, L., Caffarra, S., Covitz, S. C., Franco, A. R., Karipidis, I. I., Kruper, J., Milham, M. P., Avelar‐Pereira, B., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.24.481303 · Cited by 8 · see citing works

Connectome-based schizophrenia prediction using structural connectivity - Deep Graph Neural Network(sc-DGNN)

Udayakumar, P., & Subhashini, R. (2024) · Journal of X-Ray Science and Technology · doi:10.3233/xst-230426 · Cited by 7 · see citing works

Structural brain network organization in children with prenatal alcohol exposure

Liang, X., Kelly, C. E., Yeh, C., Dhollander, T., Hearps, S. J. C., Anderson, P. J., & Thompson, D. K. (2024) · NeuroImage Clinical · doi:10.1016/j.nicl.2024.103690 · Cited by 8 · see citing works

Spherical-deconvolution informed filtering of tractograms changes laterality of structural connectome

He, Y., Hong, Y., & Wu, Y. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120904 · Cited by 7 · see citing works

Resolving heterogeneity of early-onset major depressive disorder through individual differential structural covariance network analysis

Luo, Z., Hu, Z., Qiu, X., Li, W., Wang, C., Lan, X., Mai, S., Chen, Y., Liu, G., Zhang, F., et al. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.01.025 · Cited by 7 · see citing works

Precision neurodiversity: personalized brain network architecture as a window into cognitive variability

Mohammad, S. I., Azzam, E. R., Vasudevan, A., Ismail, S. M., Ayaz, H., & Prasad, K. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1669431 · Cited by 8 · see citing works

Gyral and sulcal connectivity in the human cerebral cortex

Kruggel, F., & Solodkin, A. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac338 · Cited by 7 · see citing works

Can structure predict function at individual level in the human connectome?

Smolders, L., Baene, W. D., Rutten, G.-J. M., van der Hofstad, R., & Florack, L. M. J. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02796-2 · Cited by 7 · see citing works

High‐Order line graphs of fMRI data in major depressive disorder

Guo, H., Huang, X., Wang, C., Wang, H., Bai, X., & Li, Y. (2024) · Medical Physics · doi:10.1002/mp.17119 · Cited by 7 · see citing works

A diffusion MRI study of brain white matter microstructure in adolescents and adults with a Fontan circulation: Investigating associations with resting and peak exercise oxygen saturations and cognition

Verrall, C. E., Chen, J., Yeh, C., Mackay, M. T., d’Udekem, Y., Winlaw, D. S., Iyengar, A. J., Ayer, J. G., Gentles, T. L., Cordina, R., et al. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103151 · Cited by 7 · see citing works

Future projections for mammalian whole-brain simulations based on technological trends in related fields

Igarashi, J. (2024) · Neuroscience Research · doi:10.1016/j.neures.2024.11.005 · Cited by 6 · see citing works

Graph analysis of guilt processing network highlights links with subclinical anxiety and self-blame

Zareba, M. R., Bielski, K., Costumero, V., & Visser, M. (2024) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsae092 · Cited by 6 · see citing works

Clinical connectomics: Mapping structural brain networks in disease

Zalesky, A., & Biase, M. A. D. (2026) · Neuron · doi:10.1016/j.neuron.2026.02.007 · Cited by 7 · see citing works

Application of omics-based biomarkers in substance use disorders

Yang, L., Zhang, L., Zhang, H., & Liu, J. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100008 · Cited by 6 · see citing works

Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses

Sairanen, V., Ocampo‐Pineda, M., Granziera, C., Schiavi, S., & Daducci, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118802 · Cited by 6 · see citing works

Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis

Ma, K., Wen, X., Zhu, Q., & Zhang, D. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3342047 · Cited by 6 · see citing works

Dual-Viral Transduction Utilizing Highly Efficient Retrograde Lentivirus Improves Labeling of Long Propriospinal Neurons

Brown, B. L., Zalla, R. M., Shepard, C. T., Howard, R. M., Kopechek, J. A., Magnuson, D. S. K., & Whittemore, S. R. (2021) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2021.635921 · Cited by 6 · see citing works

A spatiotemporal complexity architecture of human brain activityPreprint

Krohn, S., von Schwanenflug, N., Waschke, L., Romanello, A., Gell, M., Garrett, D. D., & Finke, C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.04.446948 · Cited by 6 · see citing works

Atlas-based templates vs. subject-specific tractography: resolving the debate

Schilling, K. G., Zhang, F., Tournier, J., Vergani, F., Sotiropoulos, S. N., Rokem, A., & O’Donnell, L. J. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02974-w · Cited by 6 · see citing works

MR connectomics: Principles and challenges

Hagmann, P., Cammoun, L., Gigandet, X., Gerhard, S., Grant, P. E., Wedeen, V. J., Meuli, R. A., Thiran, J., Honey, C. J., & Sporns, O. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.01.014 · Cited by 271 · see citing works

Current challenges and opportunities for tractography

Rheault, F., Poulin, P., Caron, A. V., St‐Onge, E., Schilling, K. G., Petit, L., Dell’Acqua, F., Leemans, A., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00037-9 · Cited by 2 · see citing works

The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI

Moody, J. F., Adluru, N., Alexander, A. L., & Field, A. S. (2021) · Seminars in Ultrasound CT and MRI · doi:10.1053/j.sult.2021.07.007 · Cited by 12 · see citing works

Global brain connectivity analysis by diffusion MR tractography

Gigandet, X. (2009) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-4511 · Cited by 6 · see citing works

Building connectomes using diffusion MRI: why, how and but

Sotiropoulos, S. N., & Zalesky, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3752 · Cited by 290 · see citing works