Towards a new approach to reveal dynamical organization of the brain using topological data analysis
Open accessAbstract
Little is known about how our brains dynamically adapt for efficient functioning. Most previous work has focused on analyzing changes in co-fluctuations between a set of brain regions over several temporal segments of the data. We argue that by collapsing data in space or time, we stand to lose useful information about the brain’s dynamical organization. Here we use Topological Data Analysis to reveal the overall organization of whole-brain activity maps at a single-participant level—as an interactive representation—without arbitrarily collapsing data in space or time. Using existing multitask fMRI datasets, with the known ground truth about the timing of transitions from one task-block to next, our approach tracks both within- and between-task transitions at a much faster time scale (~4–9 s) than before. The individual differences in the revealed dynamical organization predict task performance. In summary, our approach distills complex brain dynamics into interactive and behaviorally relevant representations.
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?
Differences between individuals in task performance are related to patterns of dynamic brain organization.
“Several recent studies have further shown how individual differences in task performance are related to patterns of dynamic brain organization54,55.”
From Cognitive and behavioural flexibility: Neural mechanisms and clinical considerations (2021) · cites this paper as [55]
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
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585 · 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
Self-citationFortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293 · see citing works
Fast algorithm for detecting community structure in networks
Newman, M. E. J. (2004) · Physical Review E · doi:10.1103/physreve.69.066133 · Cited by 5,415 · 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
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150 · see citing works
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.042 · Cited by 4,502 · see citing works
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326 · see citing works
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124 · see citing works
Large-scale automated synthesis of human functional neuroimaging data.
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011) · Nature Methods · doi:10.1038/nmeth.1635 · Cited by 3,686 · cited 3× · see citing works
“The group-maps (in red-yellow scale) for positive relation with CS are overlaid on the task-associated meta-analysis maps from NeuroSynth 44 .”
MethodResultInfluentialTracking whole-brain connectivity dynamics in the resting state
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs352 · Cited by 2,992 · see citing works
Dynamic functional connectivity: Promise, issues, and interpretations
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962 · see citing works
Self-citationOptimal Detection of Changepoints With a Linear Computational Cost
Killick, R., Fearnhead, P., & Eckley, I. A. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2012.737745 · Cited by 2,586 · see citing works
Carlsson, G. (2009) · Bulletin of the American Mathematical Society · doi:10.1090/s0273-0979-09-01249-x · Cited by 2,302 · see citing works
Self-citationModels of core/periphery structures
Borgatti, S. P., & Everett, M. G. (2000) · Social Networks · doi:10.1016/s0378-8733(99)00019-2 · Cited by 2,248 · see citing works
Time–frequency dynamics of resting-state brain connectivity measured with fMRI
Chang, C., & Glover, G. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.011 · Cited by 1,808 · see citing works
Self-citation(2002) · Cited by 1,635 · see citing works
Function in the human connectome: task-fMRI and individual differences in behavior
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., Feldt, C., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.033 · Cited by 1,529 · see citing works
Resting-state fMRI in the human connectome project
Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D. A., Griffanti, L., Harms, M. P., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.039 · Cited by 1,502 · see citing works
The dynamic functional connectome: State-of-the-art and perspectives
Preti, M. G., Bolton, T. A. W., & Ville, D. V. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.061 · Cited by 1,407 · see citing works
The Human Connectome Project's neuroimaging approach
Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., Coalson, T. S., Harms, M. P., Jenkinson, M., Moeller, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4361 · Cited by 1,090 · see citing works
Network attributes for segregation and integration in the human brain
Sporns, O. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.11.015 · Cited by 1,067 · see citing works
Self-citationDamaraju, E., Allen, E. A., Belger, A., Ford, J. M., McEwen, S. C., Mathalon, D. H., Mueller, B. A., Pearlson, G. D., Potkin, S. G., Preda, A., et al. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.07.003 · Cited by 1,057 · see citing works
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.018 · Cited by 886 · see citing works
Time-resolved resting-state brain networks
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400181111 · Cited by 753 · see citing works
Hutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S., & Menon, R. S. (2012) · Human Brain Mapping · doi:10.1002/hbm.22058 · Cited by 715 · see citing works
Mobile computation: Spatiotemporal integration of the properties of objects in motion
Cavanagh, P., Holcombe, A. O., & Chou, W. (2008) · Journal of Vision · doi:10.1167/8.12.1 · Cited by 715 · see citing works
Temporally-independent functional modes of spontaneous brain activity
Smith, S. M., Miller, K. L., Moeller, S., Xu, J., Auerbach, E. J., Woolrich, M. W., Beckmann, C. F., Jenkinson, M., Andersson, J., Glasser, M. F., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121329109 · Cited by 702 · see citing works
Liu, X., & Duyn, J. H. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216856110 · Cited by 647 · see citing works
Generating surrogate data for time series with several simultaneously measured variables
Prichard, D., & Theiler, J. P. (1994) · Physical Review Letters · doi:10.1103/physrevlett.73.951 · Cited by 639 · see citing works
Depression, rumination and the default network
Berman, M. G., Peltier, S., Nee, D. E., Kross, E., Deldin, P. J., & Jonides, J. (2011) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsq080 · Cited by 568 · see citing works
Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adults
Grabner, G., Janke, A. L., Budge, M. M., Smith, D. B., Pruessner, J. C., & Collins, D. L. (2006) · Lecture notes in computer science · doi:10.1007/11866763_8 · Cited by 556 · see citing works
Extracting insights from the shape of complex data using topology
Lum, P. Y., Singh, G., Lehman, A., Ishkanov, T., Vejdemo‐Johansson, M., Alagappan, M., Carlsson, J. G., & Carlsson, G. (2013) · Scientific Reports · doi:10.1038/srep01236 · Cited by 530 · see citing works
Self-citationNicolau, M. M., Levine, A. J., & Carlsson, G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1102826108 · Cited by 516 · see citing works
Self-citationTopological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition
Singh, G., Memoli, F., & Carlsson, G. (2007) · The Eurographics Association · doi:10.2312/spbg/spbg07/091-100 · Cited by 514 · see citing works
Self-citationRashid, B., Damaraju, E., Pearlson, G. D., & Calhoun, V. D. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00897 · Cited by 399 · see citing works
Core-Periphery Structure in NetworksPreprint
Puck, M., Mason, R., Porter, A., Fowler, J. H., & Mucha, P. J. (2013) · Cited by 385 · see citing works
Lindquist, M. A., Xu, Y., Nebel, M. B., & Caffo, B. S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.052 · Cited by 369 · see citing works
Periodic changes in fMRI connectivity
Handwerker, D. A., Roopchansingh, V., González-Castillo, J., & Bandettini, P. A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.078 · Cited by 360 · see citing works
Self-citationTask-Based Core-Periphery Organization of Human Brain Dynamics
Bassett, D. S., Wymbs, N. F., Rombach, M. P., Porter, M. A., Mucha, P. J., & Grafton, S. T. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003171 · Cited by 356 · see citing works
The future of fMRI connectivity
Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.022 · Cited by 355 · see citing works
Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns
González-Castillo, J., Hoy, C. W., Handwerker, D. A., Robinson, M. E., Buchanan, L. C., Saad, Z. S., & Bandettini, P. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1501242112 · Cited by 344 · see citing works
Self-citationEEG correlates of time-varying BOLD functional connectivity
Chang, C. E., Liu, Z., Chen, M. C., Liu, X., & Duyn, J. H. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.01.049 · Cited by 344 · see citing works
Task-based dynamic functional connectivity: Recent findings and open questions
González-Castillo, J., & Bandettini, P. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.006 · Cited by 293 · see citing works
Self-citationThe behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity
Cohen, J. R. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.036 · Cited by 291 · see citing works
Dynamic BOLD functional connectivity in humans and its electrophysiological correlates
Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., & Laufs, H. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00339 · Cited by 285 · see citing works
Evaluation of volume-based and surface-based brain image registration methods
Klein, A., Ghosh, S. S., Avants, B., Yeo, B. T. T., Fischl, B., Ardekani, B., Gee, J. C., Mann, J. J., & Parsey, R. V. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.091 · Cited by 255 · see citing works
Demirtaş, M., Tornador, C., Falcón, C., López‐Solà, M., Hernández‐Ribas, R., Pujol, J., Menchón, J. M., Ritter, P., Cardoner, N., Soriano‐Mas, C., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23215 · Cited by 236 · see citing works
Topological pattern recognition for point cloud data
Carlsson, G. (2014) · Acta Numerica · doi:10.1017/s0962492914000051 · Cited by 230 · see citing works
Self-citationTopological analysis of population activity in visual cortex
Singh, G., Memoli, F., Ishkhanov, T., Sapiro, G., Carlsson, G., & Ringach, D. L. (2008) · Journal of Vision · doi:10.1167/8.8.11 · Cited by 213 · see citing works
Self-citationShine, J. M., Koyejo, S., & Poldrack, R. A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604898113 · Cited by 203 · see citing works
Woolrich, M. W., Behrens, T. E. J., Beckmann, C. F., & Smith, S. M. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.836545 · Cited by 168 · see citing works
Estimation of dynamic functional connectivity using multiplication of temporal derivatives
Shine, J. M., Koyejo, O., Bell, P. T., Gorgolewski, K. J., Gilat, M., & Poldrack, R. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.064 · Cited by 166 · see citing works
Prefrontal Dynamics Underlying Rapid Instructed Task Learning Reverse with Practice
Cole, M. W., Bagic, A. I., Kass, R. E., & Schneider, W. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.1662-10.2010 · Cited by 139 · see citing works
Dynamic Properties of Functional Connectivity in the Rodent
Keilholz, S. D., Magnuson, M. E., Pan, W., Willis, M., & Thompson, G. J. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0115 · Cited by 136 · see citing works
Topological methods for exploring low-density states in biomolecular folding pathways
Yao, Y., Sun, J. Z., Huang, X., Bowman, G. R., Singh, G., Lesnick, M., Guibas, L. J., Pande, V. S., & Carlsson, G. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3103496 · Cited by 129 · see citing works
Self-citationFrom connectome to cognition: The search for mechanism in human functional brain networks
Mill, R. D., Ito, T., & Cole, M. W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.060 · Cited by 118 · see citing works
Making sense of brain network data
Sporns, O. (2013) · Nature Methods · doi:10.1038/nmeth.2485 · Cited by 109 · see citing works
Self-citationPetridou, N., Caballero‐Gaudes, C., Dryden, I. L., Francis, S., & Gowland, P. A. (2012) · Human Brain Mapping · doi:10.1002/hbm.21513 · Cited by 106 · see citing works
Advances in Neural Information Processing Systems 4 (NIPS 1991)
Hinton, G. E., Williams, C. K. I., & Revow, M. (1991) · Cited by 104 · see citing works
neuRosim: AnRPackage for Generating fMRI Data
Welvaert, M., Durnez, J., Moerkerke, B., Verdoolaege, G., & Rosseel, Y. (2011) · Journal of Statistical Software · doi:10.18637/jss.v044.i10 · Cited by 97 · see citing works
Ponce‐Alvarez, A., He, B. J., Hagmann, P., & Deco, G. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004445 · Cited by 86 · see citing works
Xu, Y., & Lindquist, M. A. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00285 · Cited by 83 · see citing works
Sourty, M., Thoraval, L., Roquet, D., Armspach, J., Foucher, J., & Blanc, F. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00060 · Cited by 60 · see citing works
Neural correlates of task switching in paternal 15q11–q13 deletion Prader–Willi syndrome
Woodcock, K. A., Humphreys, G. W., Oliver, C., & Hansen, P. (2010) · Brain Research · doi:10.1016/j.brainres.2010.09.093 · Cited by 53 · see citing works
Topological methods reveal high and low functioning neuro‐phenotypes within fragile X syndrome
Romano, D., Nicolau, M., Quintin, E., Mazaika, P. K., Lightbody, A. A., Hazlett, H. C., Piven, J., Carlsson, G., & Reiss, A. L. (2014) · Human Brain Mapping · doi:10.1002/hbm.22521 · Cited by 50 · see citing works
Self-citationThe impact of context processing deficits on task-switching performance in schizophrenia
Ravizza, S. M., Moua, K. C. K., Long, D. L., & Carter, C. S. (2009) · Schizophrenia Research · doi:10.1016/j.schres.2009.08.010 · Cited by 25 · see citing works
Core-Periphery Structure in Networks
Rombach, M. P., Porter, M. A., Fowler, J. H., & Mucha, P. J. (2014) · SIAM Journal on Applied Mathematics · doi:10.1137/120881683 · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Networks beyond pairwise interactions: Structure and dynamics
Battiston, F., Cencetti, G., Iacopini, I., Latora, V., Lucas, M., Patania, A., Young, J.-G., & Petri, G. (2020) · Physics Reports · doi:10.1016/j.physrep.2020.05.004 · Cited by 1,739 · see citing works
Networks beyond pairwise interactions: Structure and dynamics
Battiston, F., Cencetti, G., Iacopini, I., Latora, V., Lucas, M., Patania, A., Young, J., & Petri, G. (2020) · UCL Discovery (University College London) · Cited by 1,179 · see citing works
Cognitive and behavioural flexibility: Neural mechanisms and clinical considerations
Uddin, L. Q. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00428-w · Cited by 698 · see citing works
Graph theory methods: applications in brain networks
Sporns, O. (2018) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2018.20.2/osporns · Cited by 643 · see citing works
Self-citationIdiosynchrony: From shared responses to individual differences during naturalistic neuroimaging
Finn, E. S., Glerean, E., Khojandi, A., Nielson, D. M., Molfese, P. J., Handwerker, D. A., & Bandettini, P. A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116828 · Cited by 277 · see citing works
Self-citationThe importance of the whole: Topological data analysis for the network neuroscientist
Sizemore, A. E., Phillips‐Cremins, J. E., Ghrist, R., & Bassett, D. S. (2018) · Network Neuroscience · doi:10.1162/netn_a_00073 · Cited by 207 · see citing works
Ginestra Bianconi, A. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108770996 · Cited by 207 · see citing works
Metrics for graph comparison: A practitioner’s guide
Wills, P., & Meyer, F. G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228728 · Cited by 199 · see citing works
Simplicial complexes and complex systems
Salnikov, V., Cassese, D., & Lambiotte, R. (2018) · European Journal of Physics · doi:10.1088/1361-6404/aae790 · Cited by 164 · see citing works
Cornblath, E., Ashourvan, A., Kim, J. Z., Betzel, R. F., Ciric, R., Adebimpe, A., Baum, G. L., He, X.-S., Ruparel, K., Moore, T. M., et al. (2018) · Communications Biology · doi:10.1038/s42003-020-0961-x · Cited by 160 · see citing works
Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features
Côté-Allard, U., Campbell, E., Phinyomark, A., Laviolette, F., Gosselin, B. J., & Scheme, E. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00158 · Cited by 132 · see citing works
Higher-order simplicial synchronization of coupled topological signals
Ghorbanchian, R., Restrepo, J. G., Torres, J. J., & Bianconi, G. (2021) · Communications Physics · doi:10.1038/s42005-021-00605-4 · Cited by 121 · see citing works
Topological Data Analysis with Applications
Carlsson, G., & Vejdemo‐Johansson, M. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108975704 · Cited by 112 · see citing works
Self-citationMultivariate information theory uncovers synergistic subsystems of the human cerebral cortex
Varley, T. F., Pope, M., Faskowitz, J., & Sporns, O. (2023) · Communications Biology · doi:10.1038/s42003-023-04843-w · Cited by 107 · see citing works
Self-citationHigher-order organization of multivariate time series
Santoro, A., Battiston, F., Petri, G., & Amico, E. (2023) · Nature Physics · doi:10.1038/s41567-022-01852-0 · Cited by 106 · see citing works
Network Tomography for Understanding Phenotypic Presentations in Aortic Stenosis
Casaclang‐Verzosa, G., Shrestha, S., Khalil, M. J., Cho, J. S., Tokodi, M., Balla, S., Alkhouli, M. A., Badhwar, V., Narula, J., Miller, J. D., et al. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.11.025 · Cited by 90 · see citing works
Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics
Bolton, T. A. W., Morgenroth, E., Preti, M. G., & Ville, D. V. D. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.06.005 · Cited by 89 · see citing works
Topological phase transitions in functional brain networks
Santos, F. A. N., Raposo, E. P., Coutinho-Filho, M. D., Copelli, M., Stam, C. J., & Douw, L. (2019) · Physical review. E · doi:10.1103/physreve.100.032414 · Cited by 83 · see citing works
Beyond the clustering coefficient: A topological analysis of node neighbourhoods in complex networks
Kartun-Giles, A. P., & Ginestra Bianconi, A. (2019) · Chaos Solitons & Fractals X · doi:10.1016/j.csfx.2019.100004 · Cited by 81 · see citing works
Yan, L., Masood, T. B., Sridharamurthy, R., Rasheed, F., Natarajan, V., Hotz, I., & Wang, B. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14331 · Cited by 81 · see citing works
Topological methods for data modelling
Carlsson, G. (2020) · Nature Reviews Physics · doi:10.1038/s42254-020-00249-3 · Cited by 79 · see citing works
Self-citationSimplicial complexes and complex systems
Salnikov, V., Cassese, D., & Lambiotte, R. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.56775 · Cited by 63 · see citing works
Gao, S., Mishne, G., & Scheinost, D. (2021) · Human Brain Mapping · doi:10.1002/hbm.25561 · Cited by 58 · see citing works
Neurogenetic profiles delineate large-scale connectivity dynamics of the human brain
Diez, I. P., & Sepulcre, J. (2018) · Nature Communications · doi:10.1038/s41467-018-06346-3 · Cited by 58 · see citing works
The hidden repertoire of brain dynamics and dysfunction
McIntosh, A. R., & Jirsa, V. (2019) · Network Neuroscience · doi:10.1162/netn_a_00107 · Cited by 61 · see citing works
SFCM: A Fuzzy Clustering Algorithm of Extracting the Shape Information of Data
Bui, Q., Vo, B., Snåšel, V., Pedrycz, W., Hong, T., Nguyên, N. T., & Chen, M. (2020) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.3014662 · Cited by 60 · see citing works
Brain mitochondrial diversity and network organization predict anxiety-like behavior in male mice
Rosenberg, A. M., Saggar, M., Monzel, A. S., Devine, J., Rogu, P. J., Limoges, A., Junker, A., Sandi, C., Mosharov, E. V., Dumitriu, D., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39941-0 · Cited by 60 · see citing works
Self-citationSaggar, M., Shine, J. M., Liégeois, R., Dosenbach, N. U. F., & Fair, D. A. (2022) · Nature Communications · doi:10.1038/s41467-022-32381-2 · Cited by 60 · see citing works
Self-citationGeniesse, C., Sporns, O., Petri, G., & Saggar, M. (2019) · Network Neuroscience · doi:10.1162/netn_a_00093 · Cited by 57 · see citing works
Self-citationFeature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals
Campbell, E., Phinyomark, A., & Scheme, E. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00437 · Cited by 60 · see citing works
González-Castillo, J., Caballero‐Gaudes, C., Topolski, N., Handwerker, D. A., Pereira, F., & Bandettini, P. A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116129 · Cited by 54 · see citing works
Self-citationA hands-on tutorial on network and topological neuroscience
Centeno, E. G. Z., Moreni, G., Vriend, C., Douw, L., & Santos, F. A. N. (2022) · Brain Structure and Function · doi:10.1007/s00429-021-02435-0 · Cited by 51 · see citing works
Topological portraits of multiscale coordination dynamics
Zhang, M., Kalies, W. D., Kelso, J. A. S., & Tognoli, E. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108672 · Cited by 49 · see citing works
Organizing principles of whole-brain functional connectivity in zebrafish larvae
Betzel, R. F. (2020) · Network Neuroscience · doi:10.1162/netn_a_00121 · Cited by 51 · see citing works
Xie, H., Zheng, C. Y., Handwerker, D. A., Bandettini, P. A., Calhoun, V. D., Mitra, S. D., & González-Castillo, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.037 · Cited by 44 · see citing works
Self-citationF-Mapper: A Fuzzy Mapper clustering algorithm
Bui, Q., Vo, B., Nguyen, H.-A. T., Nguyen, Q. V. H., & Snåšel, V. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105107 · Cited by 44 · see citing works
Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology
Saggar, M., & Uddin, L. Q. (2019) · eneuro · doi:10.1523/eneuro.0384-19.2019 · Cited by 38 · see citing works
Self-citationThe topology of data: opportunities for cancer research
Loughrey, C. F., Fitzpatrick, P., Orr, N., & Jurek, A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab553 · Cited by 43 · see citing works
Tang, E., Mattar, M. G., Giusti, C., Lydon‐Staley, D. M., Thompson‐Schill, S. L., & Bassett, D. S. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0400-9 · Cited by 43 · see citing works
Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains
Anderson, K. L., Anderson, J. S., Palande, S., & Wang, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00755-3_8 · Cited by 40 · see citing works
Topological gene expression networks recapitulate brain anatomy and function
Patania, A., Selvaggi, P., Veronese, M., Dipasquale, O., Expert, P., & Petri, G. (2019) · Network Neuroscience · doi:10.1162/netn_a_00094 · Cited by 38 · see citing works
Simplicial and topological descriptions of human brain dynamics
Billings, J., Saggar, M., Hlinka, J., Keilholz, S. D., & Petri, G. (2021) · Network Neuroscience · doi:10.1162/netn_a_00190 · Cited by 38 · see citing works
Self-citationA dynamic gradient architecture generates brain activity states
Brown, J. A., Lee, A. J., Pasquini, L., & Seeley, W. W. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119526 · Cited by 36 · see citing works
The expanding horizons of network neuroscience: From description to prediction and control
Srivastava, P., Fotiadis, P., Parkes, L., & Bassett, D. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119250 · Cited by 36 · see citing works
Network Analysis of Time Series: Novel Approaches to Network Neuroscience
Varley, T. F., & Sporns, O. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.787068 · Cited by 34 · see citing works
Self-citationXie, H., Beaty, R. E., Jahanikia, S., Geniesse, C., Sonalkar, N., & Saggar, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118531 · Cited by 31 · see citing works
Self-citationComplex networks of marine heatwaves reveal abrupt transitions in the global ocean
Benedetti‐Cecchi, L. (2021) · Scientific Reports · doi:10.1038/s41598-021-81369-3 · Cited by 31 · see citing works
Persistent homology and applied homotopy theory
Carlsson, G. (2020) · Chapman & Hall/CRC eBooks · doi:10.1201/9781351251624-8 · Cited by 29 · see citing works
Self-citationImaging the temporal dynamics of brain states with highly sampled fMRI
Yang, Z., & Lewis, L. D. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.005 · Cited by 28 · see citing works
Editorial: Topological Neuroscience
Expert, P., Lord, L.-D., Kringelbach, M. L., & Petri, G. (2019) · Network Neuroscience · doi:10.1162/netn_e_00096 · Cited by 28 · see citing works
Why topological data analysis detects financial bubbles?
Akingbade, S. W., Gidea, M., Manzi, M., & Nateghi, V. (2023) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2023.107665 · Cited by 27 · see citing works
TopoAct: Visually Exploring the Shape of Activations in Deep Learning
Rathore, A., Chalapathi, N., Palande, S., & Wang, B. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14195 · Cited by 27 · see citing works
Parameter investigation of topological data analysis for EEG signals
Altindış, F., Yılmaz, B., Borisenok, S., & İçöz, K. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102196 · Cited by 25 · see citing works
Atypical core-periphery brain dynamics in autism
Roy, D., & Uddin, L. Q. (2021) · Network Neuroscience · doi:10.1162/netn_a_00181 · Cited by 22 · see citing works
Salch, A., Regalski, A., Abdallah, H., Suryadevara, R., Catanzaro, M. J., & Diwadkar, V. A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0255859 · Cited by 24 · see citing works
Yang, W., & Wang, Y. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00453-6 · Cited by 25 · see citing works
Temporal Mapper: Transition networks in simulated and real neural dynamics
Zhang, M., Chowdhury, S., & Saggar, M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00301 · Cited by 22 · see citing works
Self-citationWhy Topology for Machine Learning and Knowledge Extraction?
Ferri, M. (2018) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010006 · Cited by 23 · see citing works
Feasibility of topological data analysis for event-related fMRI
Ellis, C. T., Lesnick, M., Henselman‐Petrusek, G., Keller, B., & Cohen, J. D. (2019) · Network Neuroscience · doi:10.1162/netn_a_00095 · Cited by 23 · see citing works
The connectome spectrum as a canonical basis for a sparse representation of fast brain activity
Rué‐Queralt, J., Glomb, K., Pascucci, D., Tourbier, S., Carboni, M., Vulliémoz, S., Plomp, G., & Hagmann, P. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118611 · Cited by 24 · see citing works
Singh, Y., Jons, W. A., Eaton, J. E., Vesterhus, M. N., Karlsen, T. H., Bjoerk, I., Abildgaard, A., Jørgensen, K. K., Folseraas, T., Little, D. H. W., et al. (2022) · European Radiology Experimental · doi:10.1186/s41747-022-00312-x · Cited by 22 · see citing works
Raman spectroscopy and topological machine learning for cancer grading
Conti, F., D’Acunto, M., Caudai, C., Colantonio, S., Gaeta, R., Moroni, D., & Pascali, M. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-34457-5 · Cited by 22 · see citing works
The complexity of the stream of consciousness
Coppola, P., Allanson, J. E., Naçi, L., Adapa, R., Finoia, P., Williams, G. B., Pickard, J. D., Owen, A. M., Menon, D. K., & Stamatakis, E. A. (2022) · Communications Biology · doi:10.1038/s42003-022-04109-x · Cited by 22 · see citing works
State and trait characteristics of anterior insula time-varying functional connectivity
Pasquini, L., Toller, G., Staffaroni, A. M., Brown, J. A., Deng, J., Lee, A. J., Kurcyus, K., Shdo, S. M., Allen, I. E., Sturm, V. E., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116425 · Cited by 20 · see citing works
Model-based plant phenomics on morphological traits using morphometric descriptors
Noshita, K., Murata, H., & Kirié, S. (2022) · Ikushugaku zasshi · doi:10.1270/jsbbs.21078 · Cited by 20 · see citing works
Characterizing EEG signals of meditative states using persistent homology and Hodge spectral entropy
Gupta, K. V., Beuria, J., & Behera, L. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105779 · Cited by 20 · see citing works
Geniesse, C., Chowdhury, S., & Saggar, M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00229 · Cited by 17 · see citing works
Self-citationEspinoza, F. A., Vergara, V. M., Damaraju, E., Henke, K., Faghiri, A., Turner, J. A., Belger, A., Ford, J. M., McEwen, S. C., Mathalon, D. H., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00634 · Cited by 19 · see citing works
Mapping firms’ locations in technological space: A topological analysis of patent statistics
Escolar, E. G., Hiraoka, Y., Igami, M., & Ozcan, Y. (2023) · Research Policy · doi:10.1016/j.respol.2023.104821 · Cited by 18 · see citing works
Computing Wasserstein Distance for Persistence Diagrams on a Quantum ComputerPreprint
Berwald, J., Gottlieb, J. M., & Munch, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06433 · Cited by 28 · see citing works
Allocentric representation in the human amygdala and ventral visual stream
Evensmoen, H. R., Rimol, L. M., Winkler, A. M., Betzel, R. F., Hansen, T. I., Nili, H., & Haberg, A. K. (2021) · Cell Reports · doi:10.1016/j.celrep.2020.108658 · Cited by 17 · see citing works
STruD: Truss Decomposition of Simplicial Complexes
Preti, G., Morales, G. D. F., & Bonchi, F. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450073 · Cited by 17 · see citing works
Dizaji, A. S., Vieira, B. H., Khodaei, M. R., Ashrafi, M., Parham, E., Hossein‐Zadeh, G., Salmón, C. E. G., & Hamid Soltanian-zadeh, A. (2021) · Basic and Clinical Neuroscience Journal · doi:10.32598/bcn.12.1.574.1 · Cited by 16 · see citing works
A topological approach for cancer subtyping from gene expression data
Rafique, O., & Mir, A. H. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103357 · Cited by 16 · see citing works
Functional network collapse in neurodegenerative disease
Brown, J. A., Lee, A. J., Fernhoff, K., Pistone, T., Pasquini, L., Wise, A. B., Staffaroni, A. M., Mandelli, M. L., Lee, S. E., Boxer, A. L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-65156-6 · Cited by 17 · see citing works
Singer, B., Meling, D., Hirsch‐Hoffmann, M., Michels, L., Kometer, M., Smigielski, L., Dornbierer, D., Seifritz, E., Vollenweider, F. X., & Scheidegger, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-55726-x · Cited by 15 · see citing works
Wang, T., Johnson, T. S., Zhang, J., & Huang, K. (2018) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813279827_0032 · Cited by 15 · see citing works
Predictive modeling of neurobehavioral state and trait variation across development
Sanchez–Alonso, S., & Aslin, R. Ν. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100855 · Cited by 14 · see citing works
Ensemble forecasting of the Zika space‐time spread with topological data analysis
Soliman, M., Lyubchich, V., & Gel, Y. R. (2020) · Environmetrics · doi:10.1002/env.2629 · Cited by 14 · see citing works
Using discrete Ricci curvatures to infer COVID-19 epidemic network fragility and systemic risk
de Souza, D. B., da Cunha, J. T. S., dos Santos, E. F., Correia, J. B., da Silva, H. P., Filho, J. L. D. L., Albuquerque, J. D. O., & Santos, F. A. N. (2021) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/abed4e · Cited by 14 · see citing works
Liu, S., Yang, Y., Yang, Q., Chang, G., Zhao, X., Tang, Q., Hu, A., & Chen, X. (2022) · ChemElectroChem · doi:10.1002/celc.202101561 · Cited by 13 · see citing works
Manifold learning for fMRI time-varying functional connectivity
González-Castillo, J., Fernandez, I. S., Lam, K. C., Handwerker, D. A., Pereira, F., & Bandettini, P. A. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1134012 · Cited by 11 · see citing works
Self-citationGaurav, K., Landge, J., & Reddy, T. K. (2024) · IEEE Sensors Letters · doi:10.1109/lsens.2024.3488331 · Cited by 12 · see citing works
Duman, A. N., Tatar, A. E., & Pirim, H. (2019) · Brain Sciences · doi:10.3390/brainsci9060144 · Cited by 12 · see citing works
Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques
Casanova, R., Lyday, R. G., Bahrami, M., Burdette, J. H., Simpson, S. L., & Laurienti, P. J. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.740143 · Cited by 12 · see citing works
From calcium imaging to graph topology
Blevins, A. S., Bassett, D. S., Scott, E. K., & Vanwalleghem, G. C. (2022) · Network Neuroscience · doi:10.1162/netn_a_00262 · Cited by 11 · see citing works
Ciocanel, M.-V., Juenemann, R., Dawes, A. T., & McKinley, S. A. (2021) · Bulletin of Mathematical Biology · doi:10.1007/s11538-020-00847-3 · Cited by 12 · see citing works
Topological Data Analysis of Vascular Disease: A Theoretical Framework
Nicponski, J., & Jung, J.-H. (2020) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2020.00034 · Cited by 12 · see citing works
Haşegan, D., Geniesse, C., Chowdhury, S., & Saggar, M. (2024) · Network Neuroscience · doi:10.1162/netn_a_00403 · Cited by 11 · see citing works
Self-citationData analysis of intellectual property policy system based on Internet of Things
Wang, W. (2020) · Enterprise Information Systems · doi:10.1080/17517575.2020.1712744 · Cited by 11 · see citing works
Chung, M. K., Garcia‐Ramos, C., de Paiva, F. B., Mathis, J. R., Prabhakaran, V., Nair, V. A., Meyerand, M. E., Hermann, B. P., Binder, J. R., & Struck, A. F. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120436 · Cited by 11 · see citing works
Nakajima, R., Laskaris, N., Rhee, J. K., Baker, B. J., & Kosmidis, E. K. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15177 · Cited by 11 · see citing works
Saggar, M., Bruno, J. L., Gaillard, C., Claudino, L., & Ernst, M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119686 · Cited by 10 · see citing works
Self-citation31st Annual Computational Neuroscience Meeting: CNS*2022
Corsi, M., Sorrentino, P., Schwartz, D., George, N., Hugueville, L., Kahn, A. E., Dupont, S., Bassett, D. S., Jirsa, V., & Fallani, F. D. V. (2023) · Journal of Computational Neuroscience · doi:10.1007/s10827-022-00841-9 · Cited by 10 · see citing works
Ryu, H. M., Habeck, C. G., Stern, Y., & Lee, S. (2023) · Human Brain Mapping · doi:10.1002/hbm.26304 · Cited by 10 · see citing works
TDAview: an online visualization tool for topological data analysis
Walsh, K., Voineagu, M., Vafaee, F., & Voineagu, I. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa600 · Cited by 10 · see citing works
Persistent homology of fractional Gaussian noise
Masoomy, H., Askari, B., Najafi, M. N., & Movahed, M. S. (2021) · Physical review. E · doi:10.1103/physreve.104.034116 · Cited by 10 · see citing works
Madukpe, V. N., Ugoala, B. C., & Zulkepli, N. F. S. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00971-0 · Cited by 9 · see citing works
Lee, Y., Yoo, K., Roh, J. H., Moon, W., & Jeong, Y. (2019) · Brain Topography · doi:10.1007/s10548-019-00719-7 · Cited by 9 · see citing works
Jiang, S., Bao, N., Smith, A. D. S., Byndoor, S., Lehn, R. C. V., Mavrikakis, M., Abbott, N. L., & Zavala, V. M. (2023) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.3c03076 · Cited by 8 · see citing works
- Authors
- Saggar, M., Sporns, O., Gonzalez-Castillo, J., Bandettini, P. A., Carlsson, G., Glover, G., & Reiss, A. L.
- Affiliations
- Stanford Medicine (US)
- Indiana University Bloomington (US)
- National Institutes of Health (US)
- Ayasdi (United States) (US)
- Stanford University (US)
- Corresponding author
- Saggar, Manish
- Venue
- Nature Communications
- Published
- 2018-04-11
- Volume
- 9
- Issue
- 1
- Pages
- 1399-1399
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC5895632
- OpenAlex
- W2797507693
- Cited by
- 253
- References
- 69
- Citations by year
- 2026: 29 · 2025: 32 · 2024: 19 · 2023: 40 · 2022: 24 · 2021: 40 · 2020: 27 · 2019: 32 · 2018: 10
- FWCI
- 16.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- IUScholarWorks Open (Indiana University) (submittedVersion)
- Funders
- National Alliance for Research on Schizophrenia and Depression
- National Institutes of Health (93-M-0170, NCT00001360, 5u54mh091657-05, 1U54‐MH‐091657)
- McDonnell Center for Systems Neuroscience (ZIAMH002783, 1U54MH091657)
- National Institute of Mental Health (K99MH104605, 1U54 MH091657, U54-MH091657, 93- M-0170, NCT00001360, R00MH104605, ZIAMH002783)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- brain dynamics, topological data analysis, task transitions, interactive visualization, individual differences, task performance prediction
- MeSH terms
- Adult, Brain, Female, Humans, Magnetic Resonance Imaging, Male, Memory, Short-Term, Nerve Net, Rest, Task Performance and Analysis, Connectome, Datasets as Topic
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
