Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data
Open accessAbstract
Graph theory (GT) is a powerful framework for quantifying topological features of neuroimaging-derived functional and structural networks. However, false positive (FP) connections arise frequently and influence the inferred topology of networks. Thresholding is often used to overcome this problem, but an appropriate threshold often relies on a priori assumptions, which will alter inferred network topologies. Four common network metrics (global efficiency, mean clustering coefficient, mean betweenness and smallworldness) were tested using a model tractography dataset. It was found that all four network metrics were significantly affected even by just one FP. Results also show that thresholding effectively dampens the impact of FPs, but at the expense of adding significant bias to network metrics. In a larger number (n=248) of tractography datasets, statistics were computed across random group permutations for a range of thresholds, revealing that statistics for network metrics varied significantly more than for non-network metrics (i.e., number of streamlines and number of edges). Varying degrees of network atrophy were introduced artificially to half the datasets, to test sensitivity to genuine group differences. For some network metrics, this atrophy was detected as significant (p<0.05, determined using permutation testing) only across a limited range of thresholds. We propose a multi-threshold permutation correction (MTPC) method, based on the cluster-enhanced permutation correction approach, to identify sustained significant effects across clusters of thresholds. This approach minimises requirements to determine a single threshold a priori. We demonstrate improved sensitivity of MTPC-corrected metrics to genuine group effects compared to an existing approach and demonstrate the use of MTPC on a previously published network analysis of tractography data derived from a clinical population. In conclusion, we show that there are large biases and instability induced by thresholding, making statistical comparisons of network metrics difficult. However, by testing for effects across multiple thresholds using MTPC, true group differences can be robustly identified.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
There is ongoing disagreement about whether filtering methods are beneficial or detrimental to the structural connectome.
“Research is still underway to determine the best method(s) for filtering, with some disagreement on the beneficial versus detrimental effect of these methods on the structural connectome (Buchanan et al., 2020; Civier et al., 2019; Drakesmith et al., 2015; Frigo et al., 2020; Schiavi et al., 2020a; Yeh et al., 2016a).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Drakesmith et al., 2015
The Introduction’s “Thresholding” section says removing weak connections can suppress false positives but can also remove genuine connections and bias network metrics; it calls the alternative of leaving graphs unthresholded contentious. This supports a beneficial-versus-detrimental trade-off for network thresholding, but the paper does not establish disagreement about filtering methods for the structural connectome generally. Page numbers were not provided in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFalse positive tract reconstructions may lead to biased characterization of connectome topology.
“This may lead to biased characterization of connectome topology, particularly in the presence of false positive tract reconstructions (Drakesmith et al. 2015; Zalesky et al. 2016).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Drakesmith et al. 2015
False-positive connections can severely bias estimation of connectivity.
“and that these erroneous connections can severely bias the estimation of connectivity (Drakesmith et al., 2015; Maier-Hein et al., 2017; Zalesky et al., 2016b).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Drakesmith et al., 2015
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060 · see citing works
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989 · see citing works
Centrality in social networks conceptual clarification
Freeman, L. C. (1978) · Social Networks · doi:10.1016/0378-8733(78)90021-7 · Cited by 17,062 · see citing works
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
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
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812 · see citing works
Nonparametric statistical testing of EEG- and MEG-data
Maris, E., & Oostenveld, R. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.03.024 · Cited by 8,496 · see citing works
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256 · see citing works
MR diffusion tensor spectroscopy and imaging
Basser, P. J., Mattiello, J., & LeBihan, D. (1994) · Biophysical Journal · doi:10.1016/s0006-3495(94)80775-1 · Cited by 5,970 · see citing works
SMITH, S., & NICHOLS, T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.061 · Cited by 5,250 · 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
Measuring Information Transfer
Schreiber, T. (2000) · Physical Review Letters · doi:10.1103/physrevlett.85.461 · Cited by 4,571 · see citing works
Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2035616 · Cited by 4,311 · 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
Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471 · 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
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
Efficiency and Cost of Economical Brain Functional Networks
Achard, S., & Bullmore, E. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030017 · Cited by 2,553 · 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
Łuczak, T., Janson, S., & Ruciński, A. (2000) · Cited by 2,192 · see citing works
Tournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191 · see citing works
Edgington, E. S., Edgington, E., & Onghena, P. (1964) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess2169 · Cited by 2,034 · see citing works
Stam, C. J., Nolte, G., & Daffertshofer, A. (2007) · Human Brain Mapping · doi:10.1002/hbm.20346 · Cited by 2,024 · see citing works
Tuch, D. S. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20279 · Cited by 1,880 · see citing works
Partial directed coherence: a new concept in neural structure determination
Baccalá, L. A., & Sameshima, K. (2001) · Biological Cybernetics · doi:10.1007/pl00007990 · Cited by 1,791 · 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
Identifying true brain interaction from EEG data using the imaginary part of coherency
Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., & Hallett, M. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.04.029 · Cited by 1,756 · see citing works
ALSPAC–The Avon Longitudinal Study of Parents and Children
Golding, Pembrey, Jones, & The Alspac Study Team (2001) · Paediatric and Perinatal Epidemiology · doi:10.1046/j.1365-3016.2001.00325.x · Cited by 1,549 · see citing works
Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence
Humphries, M. D., & Gurney, K. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002051 · Cited by 1,442 · see citing works
Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging
Wedeen, V. J., Hagmann, P., Tseng, W. I., Reese, T. G., & Weisskoff, R. M. (2005) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20642 · Cited by 1,339 · see citing works
White matter in learning, cognition, and psychiatric disorders
Fields, R. D. (2008) · Trends in Neurosciences · doi:10.1016/j.tins.2008.04.001 · Cited by 1,337 · see citing works
The B‐matrix must be rotated when correcting for subject motion in DTI data
Leemans, A., & Jones, D. K. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21890 · Cited by 1,266 · see citing works
Self-citationNunez, P. L., Srinivasan, R., Westdorp, A. F., Wijesinghe, R. S., Tucker, D. M., Silberstein, R. B., & Cadusch, P. J. (1997) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(97)00066-7 · Cited by 1,230 · see citing works
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory
van Wijk, B. C. M., Stam, C. J., & Daffertshofer, A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013701 · Cited by 1,161 · see citing works
Intensity and coherence of motifs in weighted complex networks.
Onnela, J., Saramäki, J., Kertész, J., & Kaski, K. (2004) · Physical review. E, Statistical, nonlinear, and soft matter physics · doi:10.1103/physreve.71.065103 · Cited by 1,087 · 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
Clustering in weighted networks
Opsahl, T., & Panzarasa, P. (2009) · Social Networks · doi:10.1016/j.socnet.2009.02.002 · Cited by 1,037 · 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
Kamiński, M., Ding, M., Truccolo, W., & Bressler, S. L. (2001) · Biological Cybernetics · doi:10.1007/s004220000235 · Cited by 990 · see citing works
Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder
Zhang, J., Wang, J., Wu, Q., Kuang, W., Huang, X., He, Y., & Gong, Q. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.05.018 · Cited by 951 · see citing works
ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data
Leemans, A., Jeurissen, B., Sijbers, J., & Jones, D. K. (2009) · Cited by 874 · see citing works
Self-citationAdaptive 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
The stability of centrality measures when networks are sampled
Costenbader, E., & Valente, T. W. (2003) · Social Networks · doi:10.1016/s0378-8733(03)00012-1 · Cited by 813 · see citing works
Age-related changes in modular organization of human brain functional networks
Meunier, D., Achard, S., Morcom, A., & Bullmore, E. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.062 · Cited by 756 · 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
On the robustness of centrality measures under conditions of imperfect data
Borgatti, S. P., Carley, K. M., & Krackhardt, D. M. (2005) · Social Networks · doi:10.1016/j.socnet.2005.05.001 · Cited by 733 · see citing works
van den Heuvel, M. P., Stam, C. J., Boersma, M., & Pol, H. E. H. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.08.010 · Cited by 720 · see citing works
Deterministic and probabilistic tractography based on complex fibre orientation distributions
Descoteaux, M., Deriche, R., Knosche, T. R., & Anwander, A. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2004424 · Cited by 698 · see citing works
Resting-state fMRI confounds and cleanup
Murphy, K., Birn, R. M., & Bandettini, P. A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.001 · Cited by 666 · see citing works
van den Heuvel, M. P., Mandl, R. C. W., Stam, C. J., Kahn, R. S., & Pol, H. E. H. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2874-10.2010 · Cited by 662 · see citing works
Age- and Gender-Related Differences in the Cortical Anatomical Network
Gong, G., Rosa‐Neto, P., Carbonell, F., Chen, Z. J., Yong He, A., & Evans, A. C. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2308-09.2009 · Cited by 625 · see citing works
Graph-based network analysis of resting-state functional MRI
Wang, J. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00016 · Cited by 614 · see citing works
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
Chen, Z. J., He, Y., Rosa-Neto, P., Germann, J., & Evans, A. C. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhn003 · Cited by 484 · see citing works
Bernhardt, B. C., Chen, Z., Yong He, A., Evans, A. C., & Bernasconi, N. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq291 · Cited by 463 · see citing works
Small‐world properties of nonlinear brain activity in schizophrenia
Rubinov, M., Knock, S. A., Stam, C. J., Micheloyannis, S., Harris, A. W. F., Williams, L. M., & Breakspear, M. J. (2007) · Human Brain Mapping · doi:10.1002/hbm.20517 · Cited by 458 · see citing works
Undirected graphs of frequency-dependent functional connectivity in whole brain networks
Salvador, R., Suckling, J., Schwarzbauer, C., & Bullmore, E. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1645 · Cited by 437 · see citing works
Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom
Fillard, P., Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., Ramirez-Manzanares, A., Reisert, M., Sakaie, K., Tensaouti, F., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.032 · Cited by 418 · see citing works
Alzheimer's disease: connecting findings from graph theoretical studies of brain networks
Tijms, B. M., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.02.020 · Cited by 416 · see citing works
de Haan, W., Pijnenburg, Y. A. L., STRIJERS, R. L. M., van der Made, Y., van der Flier, W. M., Scheltens, P., & Stam, C. J. (2009) · BMC Neuroscience · doi:10.1186/1471-2202-10-101 · Cited by 404 · see citing works
Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution
Jeurissen, B., Leemans, A., Jones, D. K., Tournier, J., & Sijbers, J. (2010) · Human Brain Mapping · doi:10.1002/hbm.21032 · Cited by 369 · see citing works
Self-citationHosseini, S. M. H., Hoeft, F., & Kesler, S. R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040709 · Cited by 361 · 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
Parker, G. J. M., & Alexander, D. C. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1639 · Cited by 339 · 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
Self-citationDell'Acqua, F., Scifo, P., Rizzo, G., Catani, M., Simmons, A., Scotti, G., & Fazio, F. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.09.033 · Cited by 320 · see citing works
Dell'Acqua, F., Simmons, A., Williams, S., & Catani, M. (2013) · Human Brain Mapping · doi:10.1002/hbm.22080 · Cited by 310 · 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
Persistent angular structure: new insights from diffusion magnetic resonance imaging data
Jansons, K. M., & Alexander, D. C. (2003) · Inverse Problems · doi:10.1088/0266-5611/19/5/303 · Cited by 305 · see citing works
Guye, M., Bettus, G., Bartoloméi, F., & Cozzone, P. J. (2010) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-010-0205-z · Cited by 298 · see citing works
Bartolomei, F., Bosma, I., Klein, M., Baayen, J. C., Reijneveld, J. C., Postma, T. J., Heimans, J. J., van Dijk, B. W., de Munck, J. C., de Jongh, A., et al. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.05.018 · Cited by 276 · see citing works
Evolving functional network properties and synchronizability during human epileptic seizures
Schindler, K., Bialonski, S., Horstmann, M., Elger, C., & Lehnertz, K. (2008) · BORIS (University Library Bern) · Cited by 267 · see citing works
Handbook of Brain Connectivity
Jirsa, V., & McIntosh, A. R. (2007) · Understanding complex systems · doi:10.1007/978-3-540-71512-2 · Cited by 264 · see citing works
Evolving functional network properties and synchronizability during human epileptic seizures
Schindler, K. A., Bialonski, S., Horstmann, M.-T., Elger, C. E., & Lehnertz, K. (2008) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.2966112 · Cited by 258 · see citing works
Assessment of system dysfunction in the brain through MRI-based connectomics
Filippi, M., van den Heuvel, M. P., Fornito, A., Yong He, A., Pol, H. E. H., Agosta, F., Comı, G., & Rocca, M. A. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70144-3 · Cited by 215 · see citing works
Bonilha, L., Nesland, T., Martz, G. U., Joseph, J. E., Spampinato, M. V., Edwards, J. C., & Tabesh, A. (2012) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2012-302476 · Cited by 209 · see citing works
Schizophrenia, neurodevelopment and corpus callosum
Innocenti, G. M., Ansermet, F., & Parnas, J. (2003) · Molecular Psychiatry · doi:10.1038/sj.mp.4001205 · Cited by 207 · see citing works
Cao, Q., Shu, N., An, L., Wang, P., Sun, L., Xia, M., Wang, J.-H., Gong, G., Zang, Y., Yf, W., et al. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4793-12.2013 · Cited by 206 · see citing works
Hierarchical functional modularity in the resting‐state human brain
Ferrarini, L., Veer, I. M., Baerends, E., van Tol, M., Renken, R. J., van der Wee, N. J. A., Veltman, D. J., Aleman, A., Zitman, F. G., Penninx, B. W. J. H., et al. (2008) · Human Brain Mapping · doi:10.1002/hbm.20663 · Cited by 200 · see citing works
Comparing functional connectivity via thresholding correlations and singular value decomposition
Worsley, K. J., Chen, J.-I., Lerch, J. P., & Evans, A. C. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1637 · Cited by 195 · see citing works
Defining nodes in complex brain networks
Stanley, M. L., Moussa, M. N., Paolini, B., Lyday, R. G., Burdette, J. H., & Laurienti, P. J. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00169 · Cited by 194 · see citing works
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
Brain Network Analysis: Separating Cost from Topology Using Cost-Integration
Ginestet, C. E., Nichols, T. E., Bullmore, E. T., & Simmons, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021570 · Cited by 184 · see citing works
Altered structural networks and executive deficits in traumatic brain injury patients
Caeyenberghs, K., Leemans, A., Leunissen, I., Gooijers, J., Michiels, K., Sunaert, S. G., & Swinnen, S. P. (2012) · Brain Structure and Function · doi:10.1007/s00429-012-0494-2 · Cited by 172 · see citing works
Self-citationBaumgartner, R., Ryner, L. N., Richter, W., et al. (2000) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(99)00102-2 · Cited by 172 · see citing works
Persistent Angular Structure: New Insights from Diffusion MRI Data. Dummy Version
Jansons, K. M., & Alexander, D. C. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45087-0_56 · Cited by 171 · see citing works
Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy C-means
Chuang, K., Chiu, M., Lin, C.-C., & Chen, J.-H. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.819322 · Cited by 171 · see citing works
Finite Sample Properties and Asymptotic Efficiency of Monte Carlo Tests
Jöckel, K. (1986) · The Annals of Statistics · doi:10.1214/aos/1176349860 · Cited by 161 · see citing works
Jones, D. K. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.922191 · Cited by 161 · see citing works
Self-citationPaola, M. D., Spalletta, G., & Caltagirone, C. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-1370 · Cited by 152 · see citing works
Visualization and Processing of Tensor Fields
Brox, T., van den Boomgaard, R., Lauze, F., et al. (2006) · Mathematics and visualization · doi:10.1007/3-540-31272-2 · Cited by 147 · 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
Comparison of EPI Distortion Correction Methods in Diffusion Tensor MRI Using a Novel Framework
Wu, M., Chang, L., Walker, L., Lemaître, H., Barnett, A. S., Marenco, S., & Pierpaoli, C. M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85990-1_39 · Cited by 138 · see citing works
Markett, S. A., Reuter, M., Montag, C., Voigt, G., Lachmann, B., Rudorf, S., Elger, C. E., & Weber, B. (2013) · Human Brain Mapping · doi:10.1002/hbm.22285 · Cited by 136 · see citing works
Diffusion tensor microscopy of the intervertebral disc anulus fibrosus
Hsu, E. W., & Setton, L. A. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199905)41:5 · Cited by 127 · see citing works
Vaessen, M. J., Hofman, P. A. M., Tijssen, H. N., Aldenkamp, A. P., Jansen, J. F. A., & Backes, W. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.011 · Cited by 123 · see citing works
The Problem of Thresholding in Small-World Network Analysis
Langer, N., Pedroni, A., & Jäncke, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0053199 · Cited by 121 · see citing works
Sporns, O. (2007) · Scholarpedia · doi:10.4249/scholarpedia.4695 · Cited by 116 · see citing works
Ferri, R., Rundo, F., Bruni, O., Terzano, M. G., & Stam, C. J. (2008) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.04.294 · Cited by 105 · see citing works
Xia, M., & Yong He, A. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0062 · Cited by 105 · see citing works
A pitfall in the reconstruction of fibre ODFs using spherical deconvolution of diffusion MRI data
Parker, G. D., Marshall, D., Rosin, P. L., Drage, N. A., Richmond, S., & Jones, D. K. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.022 · Cited by 104 · see citing works
Self-citationAltered Structural Brain Connectivity in Healthy Carriers of the Autism Risk Gene, CNTNAP2
Dennis, E. L., Jahanshad, N., Rudie, J. D., Brown, J. A., Johnson, K., McMahon, K. L., de Zubicaray, G. I., Montgomery, G. W., Martin, N. G., Wright, M. J., et al. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0064 · Cited by 101 · see citing works
Orientational diffusion reflects fiber structure within a voxel
Hagen, E. V. D., & Henkelman, R. M. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10250 · Cited by 88 · see citing works
Identifying true cortical interactions in MEG using the nulling beamformer
Hui, H. B., Pantazis, D., Bressler, S. L., & Leahy, R. M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.078 · Cited by 85 · see citing works
Removal of Spurious Coherence in MEG Source-Space Coherence Analysis
Sekihara, K., Owen, J. P., Trisno, S. L., & Nagarajan, S. S. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2162514 · Cited by 85 · see citing works
Characterizing Dynamic Functional Connectivity Across Sleep Stages from EEG
Dimitriadis, S. I., Laskaris, N., del Río‐Portilla, Y., & Koudounis, G. C. (2008) · Brain Topography · doi:10.1007/s10548-008-0071-4 · Cited by 83 · see citing works
Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI
Maddah, M., Mewes, A. U. J., Haker, S. J., Grimson, W. E. L., & Warfield, S. K. (2005) · Lecture notes in computer science · doi:10.1007/11566465_24 · Cited by 82 · see citing works
A permutation testing framework to compare groups of brain networks
Simpson, S. L., Lyday, R. G., Hayasaka, S., Marsh, A. P., & Laurienti, P. J. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00171 · Cited by 77 · see citing works
Owen, J. P., Ziv, E., Bukshpun, P., Pojman, N. J., Wakahiro, M. L. J., Berman, J., Roberts, T. P. L., Friedman, E. J., Sherr, E. H., & Mukherjee, P. (2013) · Brain Connectivity · doi:10.1089/brain.2012.0121 · Cited by 76 · see citing works
Whitlow, C. T., Casanova, R., & Maldjian, J. A. (2011) · Radiology · doi:10.1148/radiol.11101708 · Cited by 74 · see citing works
An Introduction to Computational Diffusion MRI: the Diffusion Tensor and Beyond
Alexander, D. C. (2006) · Mathematics and Visualization · doi:10.1007/3-540-31272-2_5 · Cited by 70 · see citing works
Connectivity and the corpus callosum in autism spectrum conditions
Booth, R. D. L., Wallace, G. L., & Happe, F. G. E. (2011) · Progress in brain research · doi:10.1016/b978-0-444-53884-0.00031-2 · Cited by 53 · 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
An MRI review of acquired corpus callosum lesions
Renard, D., Castelnovo, G., Campello, C., Bouly, S., Floch, A. L., Thouvenot, É., Waconge, A., & Taïeb, G. (2014) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2013-307072 · Cited by 49 · see citing works
Use of Graph Theory to Evaluate Brain Networks: A Clinical Tool for a Small World?
Petrella, J. R. (2011) · Radiology · doi:10.1148/radiol.11110380 · Cited by 48 · see citing works
An exploration of graph metric reproducibility in complex brain networks
Telesford, Q. K., Burdette, J. H., & Laurienti, P. J. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00067 · Cited by 43 · see citing works
Statistical network analysis for functional MRI: summary networks and group comparisons
Ginestet, C. E., Fournel, A., & Simmons, A. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00051 · Cited by 38 · see citing works
Drakesmith, M., El‐Deredy, W., & Welbourne, S. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081553 · Cited by 33 · see citing works
Self-citationStructural Determinants of Functional Brain Dynamics
Sporns, O., & Tononi, G. (2007) · Understanding complex systems · doi:10.1007/978-3-540-71512-2_4 · Cited by 31 · see citing works
Application of neuroanatomical features to tractography clustering
Wang, Q., Yap, P., Wu, G., et al. (2012) · Human Brain Mapping · doi:10.1002/hbm.22051 · Cited by 28 · see citing works
Reconstructing functional brain networks: have we got the basics right?
Papo, D., Zanin, M., & Buldú, J. M. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00107 · Cited by 27 · see citing works
Coherence as a measure of relationship between EEG records.
Walter, D. O. (1968) · PubMed · Cited by 27 · 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
Wang, J. X., Bartolotti, J., Amaral, L. A. N., & Booth, J. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059204 · Cited by 18 · see citing works
A stable betweenness centrality measure in networks
Segarra, S., & Ribeiro, A. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854324 · Cited by 17 · see citing works
Kida, T., & Kakigi, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079023 · Cited by 9 · see citing works
Wibral, M., Vicente, R., Triesch, J., et al. (2009) · BMC Neuroscience · doi:10.1186/1471-2202-10-s1-p232 · Cited by 2 · see citing works
Jones (1999) · doi:10.1002/(sici)1522-2594(199907)42:1
High-resolution intersubject averaging and a coordinate system for the cortical surface
Fischl, B., Sereno, M. I., Tootell, R. B. H., & Dale, A. M. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4
Flagged first, then by citations
Research works printed but not linked: 1
- Parker G.D. Marshall D. Rosin P.L. Drage N. Richmond S. Jones D.K. Fast and fully automated clustering of whole brain tractography results using shape-space analysis Proceedings of the International Society for Magnetic Resonance in Medicine. Salt Lake City, USA 2013 778
Cited by
Sorted by citations
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
“thus implicitly that strong connections are reliable), but it was demonstrated that this indiscriminate filtering (thresholding) does not differentiate between true and spurious connections, and its effectiveness is still debated [85, 122, 375, 53].”
BackgroundMethodThe 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
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
Self-citationThe impact of in-scanner head motion on structural connectivity derived from diffusion MRI
Baum, G. L., Roalf, D. R., Cook, P. A., Ciric, R., Rosen, A. F. G., Xia, C., Elliott, M. A., Ruparel, K., Verma, R., Tunç, B., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.041 · Cited by 116 · see citing works
Hubs in the human fetal brain network
van den Heuvel, M. I., Turk, E., Manning, J. H., Hect, J., Hernandez-Andrade, E., Hassan, S. S., Romero, R., van den Heuvel, M. P., & Thomason, M. E. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.02.001 · Cited by 108 · 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
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
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
“This approach was based loosely on the clustering method introduced by Drakesmith et al. (2015).”
MethodSaba, V., Premi, E., Cristillo, V., Gazzina, S., Palluzzi, F., Zanetti, O., Gasparotti, R., Padovani, A., Borroni, B., & Grassi, M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00211 · Cited by 33 · 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
- Authors
- Drakesmith, M., Caeyenberghs, K., Dutt, A., Lewis, G., David, A. S., & Jones, D. K.
- Affiliations
- Cardiff University (GB)
- Australian Catholic University (AU)
- King's College London (GB)
- University College London (GB)
- Corresponding author
- Drakesmith, M.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-09
- Volume
- 118
- Pages
- 313-333
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4558463
- OpenAlex
- W2136211807
- Cited by
- 157
- References
- 131
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 13 · 2023: 15 · 2022: 16 · 2021: 15 · 2020: 13 · 2019: 18 · 2018: 17 · 2017: 16 · 2016: 8 · 2015: 2
- FWCI
- 8.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- NeuroImage (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- Research Bank (Australian Catholic University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Medical Research Council, UK (G0901885)
- Wellcome Trust (096646/Z/11/Z)
- NIHR Biomedical Research Centre
- Maudsley NHS Foundation Trust
- Institute of Psychiatry, King's College London
- 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
- graph theory, threshold bias, tractography, network topology, global efficiency, clustering coefficient, betweenness centrality, small-worldness, group differences
- MeSH terms
- Adult, Brain, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Neural Pathways, Statistics as Topic, Reproducibility of Results, Artifacts, Young Adult, Neuroimaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
