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Stoliker, D., Novelli, L., Vollenweider, F.X., Egan, G.F., Preller, K.H., Razi, A., 2022. Effective connectivity of functionally anticorrelated networks under lysergic acid diethylamide. Biol. Psychiatry doi:10.1016/j.biopsych.2022.07.013, S0006-3223(22)01446-9.

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Tasserie, J., Uhrig, L., Sitt, J.D., Manasova, D., Dupont, M., Dehaene, S., Jarraya, B., 2022. Deep brain stimulation of the thalamus restores signatures of consciousness in a non-human primate model. Sci. Adv. 8, 5547. doi:10.1126/SCIADV.ABL5547.

Tu, C., Rocha, R.P., Corbetta, M., Zampieri, S., Zorzi, M., Suweis, S., 2018. Warnings and caveats in brain controllability. Neuroimage 176, 83-91. doi:10.1016/j.neuroimage.2018.04.010.

Uddin, L.Q., 2021. Cognitive and behavioural flexibility: neural mechanisms and clinical considerations. Nat. Rev. Neurosci. 22 (3), 167-179.

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Váša, F., Shanahan, M., Hellyer, P.J., Scott, G., Cabral, J., Leech, R., 2015. Effects of lesions on synchrony and metastability in cortical networks. Neuroimage 118, 456-467. doi:10.1016/j.neuroimage.2015.05.042.

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. doi:10.1016/j.neuroimage.2015.11.047.

Vidaurre, D., Smith, S.M., Woolrich, M.W., 2017. Brain network dynamics are hierarchically organized in time. Proc. Natl. Acad. Sci. USA 114, 12827-12832. doi:10.1073/pnas.1705120114.

Vohryzek, J., Cabral, J., Castaldo, F., Sanz-Perl, Y., Lord, L.D., Fernandes, H., Litvak, V., Kringelbach, M.L., Deco, G., 2022. Dynamic sensitivity analysis: defining personalised strategies to drive brain state transitions via whole brain modelling. Comput. Struct. Biotechnol. J. doi:10.1016/j.csbj.2022.11.060.

Vohryzek, J., Deco, G., Cessac, B., Kringelbach, M.L., Cabral, J., 2020. Ghost attractors in spontaneous brain activity: recurrent excursions into functionally-relevant BOLD phase-locking states. Front. Syst. Neurosci. 14, 20.

Wielek, T., Lechinger, J., Wislowska, M., Blume, C., Ott, P., Wegenkittl, S., del Giudice, R., Heib, D.P.J., Mayer, H.A., Laureys, S., Pichler, G., Schabus, M., 2018. Sleep in patients with disorders of consciousness characterized by means of machine learning. PLoS ONE 13, e0190458. doi:10.1371/journal.pone.0190458.

Zarghami, T.S., Friston, K.J., 2020. Dynamic effective connectivity. Neuroimage 207, 116453.

Zarkali, A., McColgan, P., Ryten, M., Reynolds, R., Leyland, L.A., Lees, A.J., Rees, G., Weil, R.S., 2020. Differences in network controllability and regional gene expression underlie hallucinations in Parkinson's disease. Brain 143, 3435-3448. doi:10.1093/brain/awaa270.

Zheng, Z.S., Reggente, N., Lutkenhoff, E., Owen, A.M., Monti, M.M., 2017. Disentangling disorders of consciousness: insights from diffusion tensor imaging and machine learning. Hum. Brain Mapp. 38, 431-443. doi:10.1002/hbm.23370.

Zilles, K., Palomero-Gallagher, N., 2017. Multiple transmitter receptors in regions and layers of the human cerebral cortex. Front. Neuroanat. 11, 78. doi:10.3389/fnana.2017.00078.
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Song, M., Yang, Y., He, J., Yang, Z., Yu, S., Xie, Q., Xia, X., Dang, Y., Zhang, Q., Wu, X., Cui, Y., Hou, B., Yu, R., Xu, R., Jiang, T., 2018. Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics. eLife 7. doi:10.7554/eLife.36173.

Spindler, L.R.B., Luppi, A.I., Adapa, R.M., Craig, M.M., Coppola, P., Peattie, A.R.D., Manktelow, A.E., Finoia, P., Sahakian, B.J., Williams, G.B., Allanson, J., Pickard, J.D., Menon, D.K., Stamatakis, E.A., 2021. Dopaminergic brainstem disconnection is common to pharmacological and pathological consciousness perturbation. Proc. Natl. Acad. Sci. USA 118, e2026289118. doi:10.1073/pnas.2026289118.

Stefan, S., Schorr, B., Lopez-Rolon, A., Kolassa, I.T., Shock, J.P., Rosenfelder, M., Heck, S., Bender, A., 2018. Consciousness indexing and outcome prediction with resting-state EEG in severe disorders of consciousness. Brain Topogr. 31, 848-862. doi:10.1007/s10548-018-0643-x.

Stramaglia, S., Pellicoro, M., Angelini, L., Amico, E., Aerts, H., Cortés, J.M., ... Marinazzo, D., 2017. Ising model with conserved magnetization on the human connectome: implications on the relation structure-function in wakefulness and anesthesia. Chaos Interdiscip. J. Nonlinear Sci. 27 (4), 047407.

Sitt, J.D., King, J.R., El Karoui, I., Rohaut, B., Faugeras, F., Gramfort, A., ... Naccache, L., 2014. Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state. Brain 137 (8), 2258-2270.

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. Nat. Commun. 10. doi:10.1038/s41467-019-08934-3.

Stoliker, D., Novelli, L., Vollenweider, F.X., Egan, G.F., Preller, K.H., Razi, A., 2022. Effective connectivity of functionally anticorrelated networks under lysergic acid diethylamide. Biol. Psychiatry doi:10.1016/j.biopsych.2022.07.013, S0006-3223(22)01446-9.

Suweis, S., Tu, C., Rocha, R.P., Zampieri, S., Zorzi, M., Corbetta, M., 2019. Brain controllability: not a slam dunk yet. Neuroimage 200, 552-555. doi:10.1016/j.neuroimage.2019.07.012.

Tang, E., Giusti, C., Baum, G.L., Gu, S., Pollock, E., Kahn, A.E., Roalf, D.R., Moore, T.M., Ruparel, K., Gur, R.C., Gur, R.E., Satterthwaite, T.D., Bassett, D.S., 2017. Developmental increases in white matter network controllability support a growing diversity of brain dynamics. Nat. Commun. 8. doi:10.1038/s41467-017-01254-4.

Tang, E., Ju, H., Baum, G.L., Roalf, D.R., Satterthwaite, T.D., Pasqualetti, F., Bassett, D.S., 2020. Control of brain network dynamics across diverse scales of space and time. Phys. Rev. E 101, 62301. doi:10.1103/PhysRevE.101.062301.

Tasserie, J., Uhrig, L., Sitt, J.D., Manasova, D., Dupont, M., Dehaene, S., Jarraya, B., 2022. Deep brain stimulation of the thalamus restores signatures of consciousness in a non-human primate model. Sci. Adv. 8, 5547. doi:10.1126/SCIADV.ABL5547.

Tu, C., Rocha, R.P., Corbetta, M., Zampieri, S., Zorzi, M., Suweis, S., 2018. Warnings and caveats in brain controllability. Neuroimage 176, 83-91. doi:10.1016/j.neuroimage.2018.04.010.

Uddin, L.Q., 2021. Cognitive and behavioural flexibility: neural mechanisms and clinical considerations. Nat. Rev. Neurosci. 22 (3), 167-179.

Varoquaux, G., 2018. Cross-validation failure: Small sample sizes lead to large error bars. Neuroimage 180, 68-77.

Váša, F., Mišić, B., 2022. Null models in network neuroscience. Nat. Rev. Neurosci. 1-12. doi:10.1038/s41583-022-00601-9.

Váša, F., Shanahan, M., Hellyer, P.J., Scott, G., Cabral, J., Leech, R., 2015. Effects of lesions on synchrony and metastability in cortical networks. Neuroimage 118, 456-467. doi:10.1016/j.neuroimage.2015.05.042.

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. doi:10.1016/j.neuroimage.2015.11.047.

Vidaurre, D., Smith, S.M., Woolrich, M.W., 2017. Brain network dynamics are hierarchically organized in time. Proc. Natl. Acad. Sci. USA 114, 12827-12832. doi:10.1073/pnas.1705120114.

Vohryzek, J., Cabral, J., Castaldo, F., Sanz-Perl, Y., Lord, L.D., Fernandes, H., Litvak, V., Kringelbach, M.L., Deco, G., 2022. Dynamic sensitivity analysis: defining personalised strategies to drive brain state transitions via whole brain modelling. Comput. Struct. Biotechnol. J. doi:10.1016/j.csbj.2022.11.060.

Vohryzek, J., Deco, G., Cessac, B., Kringelbach, M.L., Cabral, J., 2020. Ghost attractors in spontaneous brain activity: recurrent excursions into functionally-relevant BOLD phase-locking states. Front. Syst. Neurosci. 14, 20.

Wielek, T., Lechinger, J., Wislowska, M., Blume, C., Ott, P., Wegenkittl, S., del Giudice, R., Heib, D.P.J., Mayer, H.A., Laureys, S., Pichler, G., Schabus, M., 2018. Sleep in patients with disorders of consciousness characterized by means of machine learning. PLoS ONE 13, e0190458. doi:10.1371/journal.pone.0190458.

Zarghami, T.S., Friston, K.J., 2020. Dynamic effective connectivity. Neuroimage 207, 116453.

Zarkali, A., McColgan, P., Ryten, M., Reynolds, R., Leyland, L.A., Lees, A.J., Rees, G., Weil, R.S., 2020. Differences in network controllability and regional gene expression underlie hallucinations in Parkinson's disease. Brain 143, 3435-3448. doi:10.1093/brain/awaa270.

Zheng, Z.S., Reggente, N., Lutkenhoff, E., Owen, A.M., Monti, M.M., 2017. Disentangling disorders of consciousness: insights from diffusion tensor imaging and machine learning. Hum. Brain Mapp. 38, 431-443. doi:10.1002/hbm.23370.

Zilles, K., Palomero-Gallagher, N., 2017. Multiple transmitter receptors in regions and layers of the human cerebral cortex. Front. Neuroanat. 11, 78. doi:10.3389/fnana.2017.00078.