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Center of Functionally Integrative Neuroscience

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Quinn, A. J., Ede, F. V., Brookes, M. J., Heideman, S. G., Nowak, M., Seedat, Z. A., Vidaurre, D., Zich, C., Nobre, A. C. & Woolrich, M. W. (2019). Unpacking Transient Event Dynamics in Electrophysiological Power Spectra. Brain Topography, 32(6), 1020-1034. https://doi.org/10.1007/s10548-019-00745-5

IC, M., DM, F., WC, K., D, V., L, T., J, H.-L., MA, G., DA, L. & JA, B. (2018). Transient visual pathway critical for normal development of primate grasping behavior. Proceedings of the National Academy of Sciences (PNAS), 115(6), 1364-1369. https://doi.org/10.1073/pnas.1717016115

R, B., D, V., AJ, Q., R, A., O, P. J., S, J. & M, W. (2018). Transient spectral events in resting state MEG predict individual time-frequency task responses. bioRxiv. https://doi.org/10.1101/419374

Higgins, C., van Es, M. W. J., Quinn, A. J., Vidaurre, D. & Woolrich, M. W. (2022). The relationship between frequency content and representational dynamics in the decoding of neurophysiological data. NeuroImage, 260, Article 119462. https://doi.org/10.1016/j.neuroimage.2022.119462

Karapanagiotidis, T., Vidaurre, D., Quinn, A. J., Vatansever, D., Poerio, G. L., Turnbull, A., Ho, N. S. P., Leech, R., Bernhardt, B. C., Jefferies, E., Margulies, D. S., Nichols, T. E., Woolrich, M. W. & Smallwood, J. (2020). The psychological correlates of distinct neural states occurring during wakeful rest. Scientific Reports, 10(1), Article 21121. https://doi.org/10.1038/s41598-020-77336-z

P, S., T, A., D, V., JD, T., S, F., A, C., SJ, C. & AD, E. (2018). The network properties of the brain at the time of normal birth support the acquisition of language processing. bioRxiv. https://doi.org/10.1101/282673

Vidaurre, D., Masaracchia, L., Larsen, N. Y., Ruijters, L. R. P. T., Alonso, S., Ahrends, C. & Woolrich, M. W. (2025). The Gaussian-linear hidden Markov model: A Python package. Imaging Neuroscience, 3, Article imag_a_00460. https://doi.org/10.1162/imag_a_00460

AJ, Q., D, V., R, A., R, B., AC, N. & MW, W. (2018). Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling. Frontiers in Neuroscience, 12(AUG), Article 603. https://doi.org/10.3389/fnins.2018.00603

Alonso, S., Cocchi, L., Hearne, L. J., Shine, J. M. & Vidaurre, D. (2025). Targeted Time-Varying Functional Connectivity. Human Brain Mapping, 46(4), Article e70157. https://doi.org/10.1002/hbm.70157

Vidaurre, D., Woolrich, M. W., Winkler, A. M., Karapanagiotidis, T., Smallwood, J. & Nichols, T. E. (2019). Stable between-subject statistical inference from unstable within-subject functional connectivity estimates. Human Brain Mapping, 40(4), 1234-1243. https://doi.org/10.1002/hbm.24442

D, V., MW, W., AM, W., T, K., J, S. & TE, N. (2018). Stable between-subject statistical inference from unstable within-subject functional connectivity estimates. bioRxiv. https://doi.org/10.1101/268151

Sharma, A., Lange, J., Vidaurre, D. & Florin, E. (2025). Spontaneous network transitions predict somatosensory perception. Cerebral Cortex, 35(11), Article bhaf309. https://doi.org/10.1093/cercor/bhaf309

J, H., S, B., A, S., M, W., D, V. & E, F. (2019). Spontaneous network activity accounts for variability in stimulus-induced gamma responses. bioRxiv. https://doi.org/10.1101/381236

D, V., LT, H., AJ, Q., BAE, H., MJ, B., AC, N. & MW, W. (2018). Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks. Nature Communications, 9(1), Article 2987. https://doi.org/10.1038/s41467-018-05316-z

Vidaurre, D., Hunt, L. T., Quinn, A. J., Hunt, B. A. E., Brookes, M. J., Nobre, A. C. & Woolrich, M. W. (2017). Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks. bioRxiv. https://doi.org/10.1101/150607

Vidaurre, D., Quinn, A. J., Baker, A. P., Dupret, D., Tejero-Cantero, A. & Woolrich, M. W. (2016). Spectrally resolved fast transient brain states in electrophysiological data. NeuroImage, 126, 81-95. https://doi.org/10.1016/j.neuroimage.2015.11.047

Higgins, C., Vidaurre, D., Kolling, N., Liu, Y., Behrens, T. & Woolrich, M. (2022). Spatiotemporally resolved multivariate pattern analysis for M/EEG. Human Brain Mapping, 43(10), 3062-3085. https://doi.org/10.1002/hbm.25835

Deco, G., Vidaurre Henche, D. & Kringelbach, M. L. (2021). Revisiting the global workspace orchestrating the hierarchical organization of the human brain. Nature Human Behaviour, 5(4), 497-511. https://doi.org/10.1038/s41562-020-01003-6

Higgins, C., Liu, Y., Vidaurre, D., Kurth-Nelson, Z., Dolan, R., Behrens, T. & Woolrich, M. (2021). Replay bursts in humans coincide with activation of the default mode and parietal alpha networks. Neuron, 109(5), 882-893.e7. https://doi.org/10.1016/j.neuron.2020.12.007

Jespersen, K. V., Stevner, A., Kringelbach, M., Van Someren, E., Vidaurre, D. & Vuust, P. (2025). Reduced switching between brain states in insomnia: evidence from modeling of fMRI brain dynamics. Cerebral Cortex, 35(11), Article bhaf314. https://doi.org/10.1093/cercor/bhaf314

Ahrends, C., Lumaca, M., Kringelbach, M. L., Vidaurre, D. & Vuust, P. (2025). Prediction of Individual Melodic Contour Processing in Sensory Association Cortices From Resting State Functional Connectivity. Human Brain Mapping, 46(17), Article e70409. https://doi.org/10.1002/hbm.70409

Ahrends, C. & Vidaurre Henche, D. (2023). Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel. bioRxiv. https://doi.org/10.1101/2023.03.02.530638

Ahrends, C., Woolrich, M. W. & Vidaurre, D. (2025). Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel. eLife, 13, Article RP95125. https://doi.org/10.7554/eLife.95125

Gohil, C., Huang, R., Roberts, E., van Es, M. W. J., Quinn, A. J., Vidaurre, D. & Woolrich, M. W. (2024). osl-dynamics, a toolbox for modeling fast dynamic brain activity. eLife, 12, Article RP91949. https://doi.org/10.7554/eLife.91949

Pervaiz, U., Vidaurre, D., Woolrich, M. W. & Smith, S. M. (2020). Optimising network modelling methods for fMRI. NeuroImage, 211, Article 116604. https://doi.org/10.1016/j.neuroimage.2020.116604

Vidaurre, D., Bielza, C. & Larrañaga, P. (2011). On nonlinearity in neural encoding models applied to the primary visual cortex. Network: Computation in Neural Systems, 22(1-4), 97-125. https://doi.org/10.3109/0954898X.2011.637606

Dijkstra, N., Ambrogioni, L., Vidaurre, D. & van Gerven, M. (2020). Neural dynamics of perceptual inference and its reversal during imagery. eLife, 9, 1-19. Article e53588. https://doi.org/10.7554/eLife.53588

Winkler, A. M., Webster, M. A., Vidaurre, D., Nichols, T. E. & Smith, S. M. (2015). Multi-level block permutation. NeuroImage, 123, 253-268. https://doi.org/10.1016/j.neuroimage.2015.05.092

Pervaiz, U., Vidaurre, D., Gohil, C., Smith, S. M. & Woolrich, M. W. (2022). Multi-dynamic modelling reveals strongly time-varying resting fMRI correlations. Medical Image Analysis, 77, Article 102366. https://doi.org/10.1016/j.media.2022.102366

C, Z., MW, W., R, B., D, V., J, S., EL, H., L, J., S, B., AJ, Q. & CJ, S. (2018). Motor learning shapes temporal activity in human sensorimotor cortex. bioRxiv. https://doi.org/10.1101/345421

Vidaurre, D., Bielza, C. & Larrañaga, P. (2010). Learning an L1-regularized Gaussian Bayesian network in the equivalence class space. IEEE Transactions on Cybernetics, 40(5), 1231-1242. Article 5382574. https://doi.org/10.1109/TSMCB.2009.2036593

van Es, M. W. J., Higgins, C., Gohil, C., Quinn, A. J., Vidaurre Henche, D. & Woolrich, M. (2025). Large-scale cortical functional networks are organized in structured cycles. Nature Neuroscience, 28(10), 2118–2128. https://doi.org/10.1038/s41593-025-02052-8

Van Schependom, J., Vidaurre, D., Costers, L., Sjøgård, M., Sima, D. M., Smeets, D., D'hooghe, M. B., D'haeseleer, M., Deco, G., Wens, V., De Tiège, X., Goldman, S., Woolrich, M. & Nagels, G. (2021). Increased brain atrophy and lesion load is associated with stronger lower alpha MEG power in multiple sclerosis patients. NeuroImage: Clinical, 30, Article 102632. https://doi.org/10.1016/j.nicl.2021.102632

F, A.-A., M, J., NK, B., JLR, A., L, G., G, D., S, S., S, J., M, H.-F., E, V., SM, S. & Vidaurre Henche, D. (2017). Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank. bioRxiv. https://doi.org/10.1101/130385

Alfaro-Almagro, F., Jenkinson, M., Bangerter, N. K., Andersson, J. L. R., Griffanti, L., Douaud, G., Sotiropoulos, S. N., Jbabdi, S., Hernandez-Fernandez, M., Vallee, E., Vidaurre, D., Webster, M., McCarthy, P., Rorden, C., Daducci, A., Alexander, D. C., Zhang, H., Dragonu, I., Matthews, P. M. ... Smith, S. M. (2018). Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage, 166, 400-424. https://doi.org/10.1016/j.neuroimage.2017.10.034

Pineda-Pardo, J. A., Bruña, R., Woolrich, M., Marcos, A., Nobre, A. C., Maestú, F. & Vidaurre, D. (2014). Guiding functional connectivity estimation by structural connectivity in MEG: An application to discrimination of conditions of mild cognitive impairment. NeuroImage, 101, 765-777. https://doi.org/10.1016/j.neuroimage.2014.08.002

Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., Barch, D. M., Uǧurbil, K. & Van Essen, D. C. (2013). Functional connectomics from resting-state fMRI. Trends in Cognitive Sciences, 17(12), 666-682. https://doi.org/10.1016/j.tics.2013.09.016

Salvan, P., Lazari, A., Vidaurre, D., Mandino, F., Johansen-Berg, H. & Grandjean, J. (2021). Frequency modulation of entorhinal cortex neuronal activity drives distinct frequency-dependent states of brain-wide dynamics. Cell Reports, 37(5), Article 109954. https://doi.org/10.1016/j.celrep.2021.109954

GC, ON., P, T., D, V., L, L., MW, W. & MJ, B. (2018). Dynamics of large-scale electrophysiological networks: A technical review. NeuroImage, 180(B), 559-576. https://doi.org/10.1016/j.neuroimage.2017.10.003

Ahrends, C. & Vidaurre Henche, D. (2023). Dynamic Functional Connectivity. ArXiv. https://doi.org/10.48550/arXiv.2301.03408

Vidaurre, D., Cichy, R. M. & Woolrich, M. W. (2021). Dissociable Components of Information Encoding in Human Perception. Cerebral cortex (New York, N.Y. : 1991), 31(12), 5664-5675. https://doi.org/10.1093/cercor/bhab189

Masaracchia, L., Fredes, F., Woolrich, M. W. & Vidaurre, D. (2023). Dissecting unsupervised learning through hidden Markov modeling in electrophysiological data. Journal of Neurophysiology, 130(2), 364-379. https://doi.org/10.1152/jn.00054.2023

Rossi, C., Vidaurre, D., Costers, L., D’hooghe, M. B., Akbarian, F., D’haeseleer, M., Woolrich, M., Nagels, G. & Van Schependom, J. (2024). Disrupted working memory event-related network dynamics in multiple sclerosis. Communications Biology, 7(1), Article 1592. https://doi.org/10.1038/s42003-024-07283-2

Stevner, A. B. A., Vidaurre, D., Cabral, J., Rapuano, K., Nielsen, S. F. V., Tagliazucchi, E., Laufs, H., Vuust, P., Deco, G., Woolrich, M. W., Van Someren, E. & Kringelbach, M. L. (2019). Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nature Communications, 10(1), Article 1035. https://doi.org/10.1038/s41467-019-08934-3

D, V., R, A., R, B., AJ, Q., F, A.-A., SM, S. & MW, W. (2018). Discovering dynamic brain networks from big data in rest and task. NeuroImage, 180(B), 646-656. https://doi.org/10.1016/j.neuroimage.2017.06.077

Sharma, A., Vidaurre, D., Vesper, J., Schnitzler, A. & Florin, E. (2021). Differential dopaminergic modulation of spontaneous cortico–subthalamic activity in parkinson’s disease. eLife, 10, Article e66057. https://doi.org/10.7554/eLife.66057

Dirren, E., Klug, J., Jarne, C., Vidaurre, D. & Carrera, E. (2025). Determinants of brain network resilience after stroke. Brain Communications, 7(3), Article fcaf218. https://doi.org/10.1093/braincomms/fcaf218

Rai, L. A., Lee, H., Becke, E., Trenado, C., Abad-Hernando, S., Sperling, M., Vidaurre, D., Wald-Fuhrmann, M., Richardson, D. C., Ward, J. A. & Orgs, G. (2025). Delta-band audience brain synchrony tracks engagement with live and recorded dance. iScience, 28(7), Article 112922. https://doi.org/10.1016/j.isci.2025.112922

Ahrends, C., Stevner, A., Pervaiz, U., Kringelbach, M. L., Vuust, P., Woolrich, M. W. & Vidaurre, D. (2022). Data and model considerations for estimating time-varying functional connectivity in fMRI. NeuroImage, 252, Article 119026. https://doi.org/10.1016/j.neuroimage.2022.119026

Cabral, J., Vidaurre, D., Marques, P., Magalhães, R., Silva Moreira, P., Miguel Soares, J., Deco, G., Sousa, N. & Kringelbach, M. L. (2017). Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Scientific Reports, 7(1), 5135. Article 5135. https://doi.org/10.1038/s41598-017-05425-7

Alonso, S., Llera, A. & Vidaurre, D. (2025). Can fMRI functional connectivity index dynamic neural communication and cognition? Biological Psychology, 199, Article 109074. https://doi.org/10.1016/j.biopsycho.2025.109074

Mosam, F., Vidaurre, D. & De Giuli, E. (2021). Breakdown of random matrix universality in Markov models. Physical Review E, 104(2), Article 024305. https://doi.org/10.1103/PhysRevE.104.024305

D, V., SM, S. & MW, W. (2017). Brain network dynamics are hierarchically organized in time. Proceedings of the National Academy of Sciences (PNAS), 114(48), 12827-12832. https://doi.org/10.1073/pnas.1705120114

Vidaurre, D., Arenas, A. L., Smith, S. M. & Woolrich, M. W. (2019). Behavioural relevance of spontaneous, transient brain network interactions in fMRI. bioRxiv. https://doi.org/10.1101/779736

Vidaurre, D., Llera, A., Smith, S. M. & Woolrich, M. W. (2021). Behavioural relevance of spontaneous, transient brain network interactions in fMRI. NeuroImage, 229, Article 117713. https://doi.org/10.1016/j.neuroimage.2020.117713

Vidaurre, D., Van Gerven, M. A. J., Bielza, C., Larrañaga, P. & Heskes, T. (2013). Bayesian sparse partial least squares. Neural Computation, 25(12), 3318-3339. https://doi.org/10.1162/NECO_a_00524

Khawaldeh, S., Tinkhauser, G., Torrecillos, F., He, S., Foltynie, T., Limousin, P., Zrinzo, L., Oswal, A., Quinn, A. J., Vidaurre, D., Tan, H., Litvak, V., Kühn, A., Woolrich, M. & Brown, P. (2022). Balance between competing spectral states in subthalamic nucleus is linked to motor impairment in Parkinson's disease. Brain, 145(1), 237-250. https://doi.org/10.1093/brain/awab264

Vidaurre, D. & Muruzábal, J. (2007). A quick assessment of topology preservation for SOM structures. IEEE Transactions on Neural Networks, 18(5), 1524-1528. https://doi.org/10.1109/TNN.2007.895820

Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E. J., Glasser, M. F., Ugurbil, K., Barch, D. M., Van Essen, D. C. & Miller, K. L. (2015). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience, 18(11), 1565-1567. https://doi.org/10.1038/nn.4125

Vidaurre, D. (2021). A new model for simultaneous dimensionality reduction and time-varying functional connectivity estimation. PLoS Computational Biology, 17(4), Article e1008580. https://doi.org/10.1371/journal.pcbi.1008580

Vidaurre, D., Rodríguez, E. E., Bielza, C., Larrañaga, P. & Rudomin, P. (2012). A new feature extraction method for signal classification applied to cord dorsum potential detection. Journal of Neural Engineering, 9(5), Article 056009. https://doi.org/10.1088/1741-2560/9/5/056009

Schependom, J. V., Vidaurre, D., Costers, L., Sjøgård, M., Dtextquotesinglehooghe, M. B., Dtextquotesinglehaeseleer, M., Wens, V., Tiège, X. D., Goldman, S., Woolrich, M. & Nagels, G. (2019). Altered transient brain dynamics in multiple sclerosis: Treatment or pathology? Human Brain Mapping, 40(16), 4789-4800. https://doi.org/10.1002/hbm.24737

B, H., MW, W., AJ, Q., D, V. & M, D. V. (2019). A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates. NeuroImage, 185, 72-82. https://doi.org/10.1016/j.neuroimage.2018.09.082

Rossi, C., Vidaurre, D., Costers, L., Akbarian, F., Woolrich, M., Nagels, G. & Van Schependom, J. (2023). A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes. Communications Biology, 6(1), Article 1079. https://doi.org/10.1038/s42003-023-05448-z

Oyarzo, P., Cichy, R. M. & Vidaurre, D. (2025). ADA: A decoding algorithm for temporally-variable brain responses. Computational and Structural Biotechnology Journal, 27, 4943-4951. https://doi.org/10.1016/j.csbj.2025.10.044

Revised 22.09.2025

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Henriette Blæsild Vuust