Liao, W., Wu, G.-R., Xu, Q., Ji, G.-J., Zhang, Z., Zang, Y.-F., Lu, G., 2014. Dynamicbc: a matlab toolbox for dynamic brain connectome analysis. Brain Connectivity 4 (10), 780–790. Liegeois, R., Ziegler, E., Geurts, P., Gomez, F., Bahri, M. A., Phillips, C., Soddu, A., Vanhaudenhuyse, A., Laureys, S., Sepulchre, R., 2015. Cerebral functional connectivity periodically (de) synchronizes with anatomical con- straints. Brain Structure and Function. Lindquist, M. A., Xu, Y., Nebel, M. B., Caffo, B. S., 2014. Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach. Neuroimage 101, 531–546. Liu, X., Duyn, J. H., 2013. Time-varying functional network information ex- tracted from brief instances of spontaneous brain activity. Proceedings of the National Academy of Sciences USA 110 (11), 4392–4397. MacMahon, M., Garlaschelli, D., 2015. Community detection for correlation matrices. Physical Review X 5, 021006. Miller, R. L., Yaesoubi, M., Calhoun, V. D., 2014. Higher dimensional analysis shows reduced dynamism of time-varying network connectivity in schizophre- nia patients. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. IEEE, pp. 3837–3840. Newman, M. E., Girvan, M., 2004. Finding and evaluating community structure in networks. Physical Review E 69 (2), 026113. Nooner, K. B., Colcombe, S. J., Tobe, R. H., Mennes, M., Benedict, M. M., Moreno, A. L., Panek, L. J., Brown, S., Zavitz, S. T., Li, Q., et al., 2012. The nki-rockland sample: a model for accelerating the pace of discovery science in psychiatry. Frontiers in Neuroscience 6, 152. 34