| A method for evaluating dynamic functional network connectivity and task-modulation: Application to schizophrenia Sakoglu et al., 2010;https://doi.org/10.1007/s10334-010-0197-8 Time-frequency dynamics of resting-state brain connectivity measured with fMRI Chang & Glover, 2010;https://doi.org/10.1016/j.neuroimage.2009.12.011Published almost simultaneously, these two papers were among the first to apply sliding-window and time-frequency analyses to the study of BOLD TVFC. |
| Tracking whole-brain connectivity dynamics in the resting stateAllen et al., 2014 (published online in 2012);https://doi.org/10.1093/cercor/bhs352 One of the first papers to combine sliding-window analysis and clustering to estimate functional connectivity states and study their dynamics. |
| Dynamic BOLD functional connectivity in humans and its electrophysiological correlates Tagliazucchi et al., 2012;https://doi.org/10.3389/fnhum.2012.00339 EEG correlates of time-varying BOLD functional connectivity Chang et al., 2013;https://doi.org/10.1016/j.neuroimage.2013.01.049 Two of the earliest studies to explore the electrophysiological basis of BOLD TVFC using simultaneous EEG/fMRI. |
| Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaquesHutchison et al., 2013;https://doi.org/10.1002/hbm.22058 One of the first studies to directly investigate the extent to which BOLD TVFC may exist independently of ongoing cognition. |
| Dynamic functional connectivity: Promise, issues, and interpretationsHutchison et al., 2013 ;https://doi.org/10.1016/j.neuroimage.2013.05.079 Important early review of BOLD TVFC findings and methods. |
| Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activityPetridou et al., 2013;https://doi.org/10.1002/hbm.21513Time-varying functional network information extracted from brief instances of spontaneous brain activityLiu and Duyn, 2013;https://doi.org/10.1073/pnas.1216856110 Two early studies suggesting that BOLD FC may be shaped by the dynamics of transient coactivation patterns (CAPs). |
| Time-resolved resting-state brain networksZalesky et al., 2014;https://doi.org/10.1073/pnas.1400181111 Early example of how sliding-window BOLD TVFC can be combined with graph theory analyses to investigate dynamic reorganization of functional brain networks during rest. |
| Dynamic functional connectivity of the default mode network tracks daydreamingKucyi and Davis, 2014;https://doi.org/10.1016/j.neuroimage.2014.06.044 Early demonstration that resting BOLD TVFC is associated with time-resolved self-reports of ongoing cognition. |
| The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discoveryCalhoun et al., 2014 ; https://doi.org/10.1016/j.neuron.2014.10.015 Review of BOLD TVFC methods, including an in-depth discussion of approaches that seek to estimate functional connectivity states. |
| Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approachLindquist et al., 2014;https://doi.org/10.1016/j.neuroimage.2014.06.052Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?Hindriks et al., 2016;https://doi.org/10.1016/j.neuroimage.2015.11.055On spurious and real fluctuations of dynamic functional connectivity during rest Leonardi and Van De Ville, 2015;https://doi.org/10.1016/j.neuroimage.2014.09.007 Three papers that carefully evaluate the potential pitfalls of sliding-window approaches and emphasize the importance of comparing against null models. |
| Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivityRashid et al., 2016;https://doi.org/10.1016/j.neuroimage.2016.04.051 One of the first studies to demonstrate the superiority of BOLD TVFC over static FC for classifying individuals based on psychiatric diagnosis. |
| The dynamic functional connectome: State-of-the-art and perspectivesPreti et al., 2017;https://doi.org/10.1016/j.neuroimage.2016.12.061 Detailed review of a wide range of methods for studying BOLD TVFC. |
| Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attentionShine et al., 2016;https://doi.org/10.1073/pnas.1604898113 A TVFC analysis of two large longitudinal single-subject datasets identified replicable temporal metastates with distinct functional network topologies, time-varying properties, and associations with cognition. |
| On the stability of BOLD fMRI correlationsLaumann et al., 2017;https://doi.org/10.1093/cercor/bhw265 Influential paper challenging the notion that resting BOLD TVFC is related to ongoing cognition. Argues that resting BOLD is consistent with a stationary process and that resting TVFC can largely be explained by sampling variability, apparent head motion, and fluctuations in arousal. |
| Interpreting temporal fluctuations in resting-state functional connectivity MRILiegeois et al., 2017;https://doi.org/10.1016/j.neuroimage.2017.09.012 Detailed exploration of which statistical properties are consistent with “dynamic” FC. Includes a detailed review of the concept of statistical stationarity, as well as an assessment of several common statistical models. |
| Comparing test-retest reliability of dynamic functional connectivity methodsChoe et al., 2017;https://doi.org/10.1016/j.neuroimage.2017.07.005Replicability of time-varying connectivity patterns in large resting state fMRI samplesAbrol et al., 2017;https://doi.org/10.1016/j.neuroimage.2017.09.020 Two of the first large, systematic evaluations of the reliability of methods for estimating BOLD TVFC and identifying functional connectivity states. |
| Brain network dynamics are hierarchically organized in timeVidaurre et al., 2017;https://doi.org/10.1073/pnas.1705120114 HMM analysis reveals a rich hierarchical temporal structure in the pattern of transitions between FC states, and that individual differences in “meta state” occupancy are related to cognition. |
| Dynamic models of large-scale brain activityBreakspear, 2017;https://doi.org/10.1038/nn.4497 Accessible review of methods for modeling large-scale brain dynamics. Includes a primer on core concepts from dynamical systems theory. |
| Neuronal origin of the temporal dynamics of spontaneous BOLD activity correlationMatsui et al., 2019;https://doi.org/10.1093/cercor/bhy045 Simultaneous recording of calcium imaging and optical hemodynamics reveal a clear neural basis for BOLD TVFC, and that fluctuations in BOLD TVFC are related to transient neural CAPs. |
| Simulations to benchmark time-varying connectivity methods for fMRIThompson et al., 2018;https://doi.org/10.1371/journal.pcbi.1006196 Recent work using multiple simulation strategies to undertake a systematic evaluation of the sensitivity of common TVFC methods. Provides an open-source toolbox for simulation and benchmarking. |
| Putting the “dynamic” back into dynamic functional connectivityHeitmann and Breakspear, 2018;https://doi.org/10.1162/netn_a_00041 Application of large-scale modeling to investigate which kinds of neural dynamics may give rise to BOLD TVFC. Argues that BOLD TVFC likely reflects complex nonlinear and nonstationary neural dynamics. |
| A method for evaluating dynamic functional network connectivity and task-modulation: Application to schizophrenia Sakoglu et al., 2010;https://doi.org/10.1007/s10334-010-0197-8 Time-frequency dynamics of resting-state brain connectivity measured with fMRI Chang & Glover, 2010;https://doi.org/10.1016/j.neuroimage.2009.12.011Published almost simultaneously, these two papers were among the first to apply sliding-window and time-frequency analyses to the study of BOLD TVFC. |
| Tracking whole-brain connectivity dynamics in the resting stateAllen et al., 2014 (published online in 2012);https://doi.org/10.1093/cercor/bhs352 One of the first papers to combine sliding-window analysis and clustering to estimate functional connectivity states and study their dynamics. |
| Dynamic BOLD functional connectivity in humans and its electrophysiological correlates Tagliazucchi et al., 2012;https://doi.org/10.3389/fnhum.2012.00339 EEG correlates of time-varying BOLD functional connectivity Chang et al., 2013;https://doi.org/10.1016/j.neuroimage.2013.01.049 Two of the earliest studies to explore the electrophysiological basis of BOLD TVFC using simultaneous EEG/fMRI. |
| Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaquesHutchison et al., 2013;https://doi.org/10.1002/hbm.22058 One of the first studies to directly investigate the extent to which BOLD TVFC may exist independently of ongoing cognition. |
| Dynamic functional connectivity: Promise, issues, and interpretationsHutchison et al., 2013 ;https://doi.org/10.1016/j.neuroimage.2013.05.079 Important early review of BOLD TVFC findings and methods. |
| Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activityPetridou et al., 2013;https://doi.org/10.1002/hbm.21513Time-varying functional network information extracted from brief instances of spontaneous brain activityLiu and Duyn, 2013;https://doi.org/10.1073/pnas.1216856110 Two early studies suggesting that BOLD FC may be shaped by the dynamics of transient coactivation patterns (CAPs). |
| Time-resolved resting-state brain networksZalesky et al., 2014;https://doi.org/10.1073/pnas.1400181111 Early example of how sliding-window BOLD TVFC can be combined with graph theory analyses to investigate dynamic reorganization of functional brain networks during rest. |
| Dynamic functional connectivity of the default mode network tracks daydreamingKucyi and Davis, 2014;https://doi.org/10.1016/j.neuroimage.2014.06.044 Early demonstration that resting BOLD TVFC is associated with time-resolved self-reports of ongoing cognition. |
| The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discoveryCalhoun et al., 2014 ; https://doi.org/10.1016/j.neuron.2014.10.015 Review of BOLD TVFC methods, including an in-depth discussion of approaches that seek to estimate functional connectivity states. |
| Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approachLindquist et al., 2014;https://doi.org/10.1016/j.neuroimage.2014.06.052Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?Hindriks et al., 2016;https://doi.org/10.1016/j.neuroimage.2015.11.055On spurious and real fluctuations of dynamic functional connectivity during rest Leonardi and Van De Ville, 2015;https://doi.org/10.1016/j.neuroimage.2014.09.007 Three papers that carefully evaluate the potential pitfalls of sliding-window approaches and emphasize the importance of comparing against null models. |
| Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivityRashid et al., 2016;https://doi.org/10.1016/j.neuroimage.2016.04.051 One of the first studies to demonstrate the superiority of BOLD TVFC over static FC for classifying individuals based on psychiatric diagnosis. |
| The dynamic functional connectome: State-of-the-art and perspectivesPreti et al., 2017;https://doi.org/10.1016/j.neuroimage.2016.12.061 Detailed review of a wide range of methods for studying BOLD TVFC. |
| Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attentionShine et al., 2016;https://doi.org/10.1073/pnas.1604898113 A TVFC analysis of two large longitudinal single-subject datasets identified replicable temporal metastates with distinct functional network topologies, time-varying properties, and associations with cognition. |
| On the stability of BOLD fMRI correlationsLaumann et al., 2017;https://doi.org/10.1093/cercor/bhw265 Influential paper challenging the notion that resting BOLD TVFC is related to ongoing cognition. Argues that resting BOLD is consistent with a stationary process and that resting TVFC can largely be explained by sampling variability, apparent head motion, and fluctuations in arousal. |
| Interpreting temporal fluctuations in resting-state functional connectivity MRILiegeois et al., 2017;https://doi.org/10.1016/j.neuroimage.2017.09.012 Detailed exploration of which statistical properties are consistent with “dynamic” FC. Includes a detailed review of the concept of statistical stationarity, as well as an assessment of several common statistical models. |
| Comparing test-retest reliability of dynamic functional connectivity methodsChoe et al., 2017;https://doi.org/10.1016/j.neuroimage.2017.07.005Replicability of time-varying connectivity patterns in large resting state fMRI samplesAbrol et al., 2017;https://doi.org/10.1016/j.neuroimage.2017.09.020 Two of the first large, systematic evaluations of the reliability of methods for estimating BOLD TVFC and identifying functional connectivity states. |
| Brain network dynamics are hierarchically organized in timeVidaurre et al., 2017;https://doi.org/10.1073/pnas.1705120114 HMM analysis reveals a rich hierarchical temporal structure in the pattern of transitions between FC states, and that individual differences in “meta state” occupancy are related to cognition. |
| Dynamic models of large-scale brain activityBreakspear, 2017;https://doi.org/10.1038/nn.4497 Accessible review of methods for modeling large-scale brain dynamics. Includes a primer on core concepts from dynamical systems theory. |
| Neuronal origin of the temporal dynamics of spontaneous BOLD activity correlationMatsui et al., 2019;https://doi.org/10.1093/cercor/bhy045 Simultaneous recording of calcium imaging and optical hemodynamics reveal a clear neural basis for BOLD TVFC, and that fluctuations in BOLD TVFC are related to transient neural CAPs. |
| Simulations to benchmark time-varying connectivity methods for fMRIThompson et al., 2018;https://doi.org/10.1371/journal.pcbi.1006196 Recent work using multiple simulation strategies to undertake a systematic evaluation of the sensitivity of common TVFC methods. Provides an open-source toolbox for simulation and benchmarking. |
| Putting the “dynamic” back into dynamic functional connectivityHeitmann and Breakspear, 2018;https://doi.org/10.1162/netn_a_00041 Application of large-scale modeling to investigate which kinds of neural dynamics may give rise to BOLD TVFC. Argues that BOLD TVFC likely reflects complex nonlinear and nonstationary neural dynamics. |