Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics
Authors: Thomas A.W. Bolton, Elenor Morgenroth, Maria Giulia Preti, Dimitri Van De Ville
Journal: Trends in Neurosciences (2020)

The human brain is a dynamic system, giving rise to behavioral facets that fluctuate at distinctive time-scales. Alterations of dynamic neural processing, as seen in brain disorders, can lead to perturbed behavior.We conceptually relate fMRI methodologies for quantifying dynamic functional connectivity, and review their application to the study of healthy behavior and its alterations in autism, schizophrenia, and depression.Each method probes regional activity or cross-regional interactions at a given spatiotemporal resolution, focusing on the whole brain or on dedicated areas, and encodes dynamics with a specific complexity.On the whole, analytical outcomes are difficult to relate across studies, and only a few methodologies are broadly leveraged by the research community.Mechanistic models of brain dynamics, and large-scale collaborative and comparative efforts, are ways forward to circumvent these issues. Human behavior comprises many aspects that stand out by their dynamic nature. To quantify its neural underpinnings, time-resolved fMRI methods have blossomed over the past decade. In this review we conceptually organize a broad repertoire of dynamic analytical pipelines and extract general observations on their application to the study of behavior and brain disorders. We aim to provide an extensive overview instead of examining only selected methodological families or specific behavioral domains. We consider behavioral aspects with distinct long-term stability (e.g., physiological state versus personality), and also address selected brain disorders with complementary genetics and symptomatology. This synthesis exposes the somewhat limited consistency of dynamic findings in the literature, as well as the unbalanced application of the multitude of available approaches which would, owing to their technical specificities, have potential to reveal distinct aspects of dynamics. We call for further comparative and collaborative efforts in the future. Human behavior comprises many aspects that stand out by their dynamic nature. To quantify its neural underpinnings, time-resolved fMRI methods have blossomed over the past decade. In this review we conceptually organize a broad repertoire of dynamic analytical pipelines and extract general observations on their application to the study of behavior and brain disorders. We aim to provide an extensive overview instead of examining only selected methodological families or specific behavioral domains. We consider behavioral aspects with distinct long-term stability (e.g., physiological state versus personality), and also address selected brain disorders with complementary genetics and symptomatology. This synthesis exposes the somewhat limited consistency of dynamic findings in the literature, as well as the unbalanced application of the multitude of available approaches which would, owing to their technical specificities, have potential to reveal distinct aspects of dynamics. We call for further comparative and collaborative efforts in the future. Perhaps the most remarkable feature of humanity is the profound behavioral diversity across different individuals, which pertains to all factors involved in interactions with the physical and social environment. This diversity underlies variability in personality, physiology, and mental capacity, which in turn are not only constituted by biological influences (e.g., fatigue, the influence of drugs, genetic makeup) but also shaped by experience (e.g., social learning, trauma). Arguably, the brain is the most complex system known to humankind, and understanding this organ is crucial for explaining behavior. Studying the brain at rest has demonstrated that, although the environment has an influence on it, the brain operates intrinsically and is modulated rather than controlled by the environment [1.Fox M.D. et al.The human brain is intrinsically organized into dynamic, anticorrelated functional networks.Proc. Natl. Acad. Sci. U. S. A. 2005; 102: 9673-9678Crossref PubMed Scopus (5071) Google Scholar]. This modulation is a recursive process between the brain and the environment mediated by perception and action [2.Friston K. The free-energy principle: a rough guide to the brain?.Trends Cogn. Sci. 2009; 13: 293-301Abstract Full Text Full Text PDF PubMed Scopus (631) Google Scholar]. Evidently, this process is highly dynamic, as are the environment and the brain [3.Chang C. Glover G.H. Time-frequency dynamics of resting-state brain connectivity measured with fMRI.Neuroimage. 2010; 50: 81-98Crossref PubMed Scopus (930) Google Scholar]. Neuroscience, in particular neuroimaging research, aims to relate variability in behavior to changes in the brain. Since its discovery in the early 1990s, functional magnetic resonance imaging (fMRI; see Glossary) has become one of the most prominent methods to this end. fMRI is a non-invasive tool to probe whole-brain activity and enables the study of sophisticated processes that involve functional integration and segregation of different brain areas over time. The study of brain signals during task or other forms of stimulation has been a productive way to decode the representation of specific processes in the brain; however, studies on the intrinsic organization of the brain at rest are equally valuable, and have been shown to predict behavior and psychopathology [4.Van Den Heuvel M.P. Pop H.E.H. Exploring the brain network: a review on resting-state fMRI functional connectivity.Eur. Neuropsychopharmacol. 2010; 20: 519-534Crossref PubMed Scopus (1402) Google Scholar,5.Yang J. et al.Current methods and new directions in resting state fMRI.Clin. Imaging. 2020; 65: 47-53Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. Original analysis approaches evolved in parallel with progress in acquisition technology from harvesting regional activation to dispersed network connectivities, and until recently relied on static measures to understand brain function [6.Mišić B. Sporns O. From regions to connections and networks: new bridges between brain and behavior.Curr. Opin. Neurobiol. 2016; 40: 1-7Crossref PubMed Google Scholar]. However, static measures fall short of capturing the inherent dynamic nature of both brain and behavior, and thus may be limited in their explanatory value and omit important insights. Consistent with the assumption that all aspects of behavior are dynamic phenomena, tailored analytical approaches to study the fMRI signals in a time-resolved manner have become increasingly prominent ([7.Preti M.G. et al.The dynamic functional connectome: state-of-the-art and perspectives.Neuroimage. 2017; 160: 41-54Crossref PubMed Scopus (300) Google Scholar,8.Lurie D. et al.Questions and controversies in the study of time-varying functional connectivity in resting fMRI.Net. Neurosci. 2019; 4: 30-69Crossref Google Scholar] for methodological reviews). In addition, these methodologies have been applied to the study of cognition and psychiatric disorders (as reviewed in [9.Cohen J.R. The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity.Neuroimage. 2018; 180: 515-525Crossref PubMed Scopus (29) Google Scholar] for a subset of technical approaches). In this review, compared with past reports, we offer a more comprehensive overview of both dynamic analytical pipelines and their application to a range of behaviors and disorders. To this end, we provide a characterization of the primarily derived measures of functional brain dynamics as well as a conceptual organization of a broad array of dynamic methodologies. We hope that this will be a useful orientation not only for researchers who are new to these methods, but also for those who are familiar with them. We then examine the use of dynamic methods across healthy and perturbed behavior, and investigate whether commonalities regarding the results from dynamic methods can be extracted. In doing so, we probe a set of behavioral aspects and examine complementary brain disorders in terms of their genetic characteristics and symptoms. A few introductory remarks should be added. Although we include different aspects of behavior to discuss conceptually distinct methodologies, our coverage remains non-exhaustive owing to space constraints. In addition, in what follows we make the assumption that reported findings truly characterize neural substrates of behavior, which remains an open question in dynamic fMRI-based analyses (Box 1).Box 1Non-Neural Contributions to Dynamic fMRI-Based Brain/Behavior AnalysesTo meaningfully probe the neural correlates of behavior through functional brain dynamics, one would hope that the acquired data accurately describe electrical brain activity; however, more than half of BOLD signal contributions are in fact non-neural [95.Biancardi M. et al.Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study.Magn. Reson. Imaging. 2009; 27: 1019-1029Crossref PubMed Scopus (150) Google Scholar]. More precisely, changes in head motion of the scanned volunteers, as well as respiratory and cardiac fluctuations, exert strong confounding impacts that many have attempted to attenuate through dedicated preprocessing steps ([96.Caballero-Gaudes C. Reynolds R.C. Methods for cleaning the BOLD fMRI.Neuroimage. 2017; 154: 128-149Crossref PubMed Scopus (0) Google Scholar] for comprehensive review). Such choices can crucially influence the relationships between fMRI signals and behavior: for example, associations with resting-state functional connectivity are reinforced when the global signal (the fraction of the BOLD signal that varies similarly across the whole grey matter) is regressed out [97.Li J. et al.Global signal regression strengthens association between resting-state functional connectivity and behavior.Neuroimage. 2019; 196: 126-141Crossref PubMed Scopus (30) Google Scholar].It remains uncertain whether current preprocessing strategies fully remove physiological confounds, given their insidious similarity to neural networks. Recently, respiratory variation and heart-rate changes were shown to induce spatiotemporally complex BOLD responses in segregated brain regions, in a way that mimicked resting-state networks [98.Chen J.E. et al.Resting-state 'physiological networks'.Neuroimage. 2020; 213116707Crossref PubMed Scopus (0) Google Scholar]. In addition, fluctuations in end-tidal CO2 (inducing vascular, as opposed to neural, changes in the brain) also yielded vascular equivalents to several notorious networks [99.Bright M.G. et al.Vascular physiology drives functional brain networks.Neuroimage. 2020; 217116907Crossref PubMed Scopus (0) Google Scholar]. Because physiological rhythms are profoundly affected by many behavioral aspects, one may question the neural nature of BOLD-based behavioral correlates.In addition, preprocessing may instead amputate the original signals from a behaviorally meaningful fraction. This has recently been suggested in the context of head motion: indeed, several overlapping spatiotemporal motion components were unraveled over the course of a recording session, and were linked to a broad array of behaviors [100.Bolton T.A.W. et al.Agito ergo sum: correlates of spatio-temporal motion characteristics during fMRI.Neuroimage. 2020; 209: 116433Crossref PubMed Scopus (3) Google Scholar]. Correcting for motion may thus partly remove behaviorally relevant information from the data.Improvements in acquisition technology may help to reduce confounding impacts on the BOLD signals, while further favoring dynamic analyses. First, recording fMRI data at subsecond temporal resolution [101.Feinberg D.A. et al.Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging.PLoS One. 2010; 5e15710Crossref PubMed Scopus (713) Google Scholar] enables a better separation between neural and physiological signal sources [102.Chen J.E. et al.On the analysis of rapidly sampled fMRI data.Neuroimage. 2019; 188: 807-820Crossref PubMed Scopus (10) Google Scholar]. However, additional issues are also introduced: for example, estimates of head motion then become more strongly influenced by physiology [103.Power J.D. et al.Distinctions among real and apparent respiratory motions in human fMRI data.Neuroimage. 2019; 201: 116041Crossref PubMed Scopus (8) Google Scholar], rendering a proper account of these two types of confound even more challenging. Second, through multi-echo acquisition, physiology-, and motion-induced confounds are further attenuated [104.Kundu P. et al.Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI.Neuroimage. 2012; 60: 1759-1770Crossref PubMed Scopus (195) Google Scholar,105.Power J.D. et al.Ridding fMRI data of motion-related influences: removal of signals with distinct spatial and physical bases in multiecho data.Proc. Natl. Acad. Sci. U. S. A. 2018; 115: E2105-E2114Crossref PubMed Scopus (56) Google Scholar]. Dynamic analytical pipelines tailored to exploit the benefits provided by such data are emerging [106.Caballero-Gaudes C. et al.A deconvolution algorithm for multi-echo functional MRI: multi-echo sparse paradigm free mapping.Neuroimage. 2019; 202: 116081Crossref PubMed Scopus (0) Google Scholar]. To meaningfully probe the neural correlates of behavior through functional brain dynamics, one would hope that the acquired data accurately describe electrical brain activity; however, more than half of BOLD signal contributions are in fact non-neural [95.Biancardi M. et al.Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study.Magn. Reson. Imaging. 2009; 27: 1019-1029Crossref PubMed Scopus (150) Google Scholar]. More precisely, changes in head motion of the scanned volunteers, as well as respiratory and cardiac fluctuations, exert strong confounding impacts that many have attempted to attenuate through dedicated preprocessing steps ([96.Caballero-Gaudes C. Reynolds R.C. Methods for cleaning the BOLD fMRI.Neuroimage. 2017; 154: 128-149Crossref PubMed Scopus (0) Google Scholar] for comprehensive review). Such choices can crucially influence the relationships between fMRI signals and behavior: for example, associations with resting-state functional connectivity are reinforced when the global signal (the fraction of the BOLD signal that varies similarly across the whole grey matter) is regressed out [97.Li J. et al.Global signal regression strengthens association between resting-state functional connectivity and behavior.Neuroimage. 2019; 196: 126-141Crossref PubMed Scopus (30) Google Scholar]. It remains uncertain whether current preprocessing strategies fully remove physiological confounds, given their insidious similarity to neural networks. Recently, respiratory variation and heart-rate changes were shown to induce spatiotemporally complex BOLD responses in segregated brain regions, in a way that mimicked resting-state networks [98.Chen J.E. et al.Resting-state 'physiological networks'.Neuroimage. 2020; 213116707Crossref PubMed Scopus (0) Google Scholar]. In addition, fluctuations in end-tidal CO2 (inducing vascular, as opposed to neural, changes in the brain) also yielded vascular equivalents to several notorious networks [99.Bright M.G. et al.Vascular physiology drives functional brain networks.Neuroimage. 2020; 217116907Crossref PubMed Scopus (0) Google Scholar]. Because physiological rhythms are profoundly affected by many behavioral aspects, one may question the neural nature of BOLD-based behavioral correlates. In addition, preprocessing may instead amputate the original signals from a behaviorally meaningful fraction. This has recently been suggested in the context of head motion: indeed, several overlapping spatiotemporal motion components were unraveled over the course of a recording session, and were linked to a broad array of behaviors [100.Bolton T.A.W. et al.Agito ergo sum: correlates of spatio-temporal motion characteristics during fMRI.Neuroimage. 2020; 209: 116433Crossref PubMed Scopus (3) Google Scholar]. Correcting for motion may thus partly remove behaviorally relevant information from the data. Improvements in acquisition technology may help to reduce confounding impacts on the BOLD signals, while further favoring dynamic analyses. First, recording fMRI data at subsecond temporal resolution [101.Feinberg D.A. et al.Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging.PLoS One. 2010; 5e15710Crossref PubMed Scopus (713) Google Scholar] enables a better separation between neural and physiological signal sources [102.Chen J.E. et al.On the analysis of rapidly sampled fMRI data.Neuroimage. 2019; 188: 807-820Crossref PubMed Scopus (10) Google Scholar]. However, additional issues are also introduced: for example, estimates of head motion then become more strongly influenced by physiology [103.Power J.D. et al.Distinctions among real and apparent respiratory motions in human fMRI data.Neuroimage. 2019; 201: 116041Crossref PubMed Scopus (8) Google Scholar], rendering a proper account of these two types of confound even more challenging. Second, through multi-echo acquisition, physiology-, and motion-induced confounds are further attenuated [104.Kundu P. et al.Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI.Neuroimage. 2012; 60: 1759-1770Crossref PubMed Scopus (195) Google Scholar,105.Power J.D. et al.Ridding fMRI data of motion-related influences: removal of signals with distinct spatial and physical bases in multiecho data.Proc. Natl. Acad. Sci. U. S. A. 2018; 115: E2105-E2114Crossref PubMed Scopus (56) Google Scholar]. Dynamic analytical pipelines tailored to exploit the benefits provided by such data are emerging [106.Caballero-Gaudes C. et al.A deconvolution algorithm for multi-echo functional MRI: multi-echo sparse paradigm free mapping.Neuroimage. 2019; 202: 116081Crossref PubMed Scopus (0) Google Scholar]. Given its simplicity, the sliding window framework has strengthened its position as the most widely applied tool to track the dynamics of brain activity [7.Preti M.G. et al.The dynamic functional connectome: state-of-the-art and perspectives.Neuroimage. 2017; 160: 41-54Crossref PubMed Scopus (300) Google Scholar]. This is illustrated in Figure 1 in its canonical form (discussed later); more recent technical developments and directly associated methods are addressed in Box 2. Briefly, the statistical interdependence between regional activity timecourses, termed functional connectivity (FC) [10.Friston K.J. Functional and effective connectivity: a review.Brain Connect. 2011; 1: 13-36Crossref PubMed Google Scholar], is evaluated across consecutive temporal windows of 30–60 s over the whole duration of the acquisition. The regional timecourses are generally obtained by averaging the voxel-wise functional signals within regions of a chosen brain parcellation, or within spatial clusters from independent component analysis (ICA). The outcome from a sliding window-based analysis is a set of timecourses that denote temporal changes in cross-regional interactions throughout the brain. The often-used dynamic functional connectivity (dFC) jargon originally denoted this type of time-resolved correlational information; it has since become an umbrella term that includes other families of methods, as developed later.Box 2Improvements to Canonical Sliding Window dFC AnalysisSliding window-based investigations can be tuned at several stages [7.Preti M.G. et al.The dynamic functional connectome: state-of-the-art and perspectives.Neuroimage. 2017; 160: 41-54Crossref PubMed Scopus (300) Google Scholar]. First, instead of fixed size rectangular windows, averaging across temporal neighbors has been suggested [107.Vergara V.M. et al.An average sliding window correlation method for dynamic functional connectivity.Hum. Brain Mapp. 2019; 40: 2089-2103Crossref PubMed Scopus (4) Google Scholar], and modulated rectangular windows were shown to more efficiently capture dFC metrics [108.Mokhtari F. et al.Sliding window correlation analysis: modulating window shape for dynamic brain connectivity in resting state.Neuroimage. 2019; 189: 655-666Crossref PubMed Scopus (5) Google Scholar]. Time-varying window lengths were also proposed: stationarity with an initial length guess is assessed, and that length is iteratively increased until consistent stationarity across brain regions is achieved [109.Zou H. Yang J. Dynamic thresholding networks for schizophrenia diagnosis.Artif. Intell. Med. 2019; 96: 25-32Crossref PubMed Scopus (0) Google Scholar]. In other work, the BOLD standard deviation across regions was assessed to pinpoint moments putatively associated with a state transition [110.Lee Y. et al.Brain-state extraction algorithm based on the state transition (BEST): a dynamic functional brain network analysis in fMRI study.Brain Topogr. 2019; 32: 897-913Crossref PubMed Scopus (2) Google Scholar].To replace Pearson's correlation coefficient as the measure of FC, correlating two dFC timecourses (i.e., correlation of correlation timecourses) has been suggested, showcasing fair-to-moderate reliability [111.Zhang H. et al.Test-retest reliability of 'high-order' functional connectivity in young healthy adults.Front. Neurosci. 2017; 11: 439Crossref PubMed Scopus (20) Google Scholar]. Others have proposed to compute the first-order time derivatives between dFC estimates together with dFC itself, using finite difference approximations [112.Espinoza F.A. et al.Characterizing whole brain temporal variation of functional connectivity via zero and first order derivatives of sliding window correlations.Front. Neurosci. 2019; 13: 634Crossref PubMed Scopus (2) Google Scholar]. Framewise estimation methods are also available, such as dynamic conditional correlation [113.Lindquist M.A. et al.Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach.Neuroimage. 2014; 101: 531-546Crossref PubMed Google Scholar] and the combination of variable parameter regression and Kalman filtering [114.Kang J. et al.Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches.Neuroimage. 2011; 56: 1222-1234Crossref PubMed Scopus (73) Google Scholar]. Regarding more conceptually novel metrics, dynamic time warping arguably better accounts for nonlinearity and time lag between brain region timecourses: matching is made between two regional timecourses, enabling differential alignment over time, and the final element of the optimal alignment path is taken as the metric of interest [109.Zou H. Yang J. Dynamic thresholding networks for schizophrenia diagnosis.Artif. Intell. Med. 2019; 96: 25-32Crossref PubMed Scopus (0) Google Scholar]. Two BOLD timecourses may also be viewed in 2D space, from which the gradient magnitude weighted by its phase consists in an FC alternative [115.Faghiri A. et al.Weighted average of shared trajectory: a new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time.J. Neurosci. Methods. 2020; 334: 108600Crossref PubMed Scopus (0) Google Scholar].Although K-means clustering was originally applied to retrieve CS [13.Allen E.A. et al.Tracking whole-brain connectivity dynamics in the resting state.Cereb. Cortex. 2014; 24: 663-676Crossref PubMed Scopus (982) Google Scholar], several more sophisticated methodological developments have gradually blossomed: for instance, group information-guided ICA is a two-step procedure in which temporally overlapping, spatially independent connectivity patterns shared by the whole investigated subject population are first derived; in a second step, the dominant connectivity state of each subject is extracted with its associated timecourse [116.Du Y. et al.Identifying dynamic functional connectivity biomarkers using GIG-ICA: application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder.Hum. Brain Mapp. 2017; 38: 2683-2708Crossref PubMed Scopus (38) Google Scholar]. In another work, a two-stage community detection approach was suggested to replace K-means clustering and directly estimate the optimal number of states [117.Zhang L. et al.Tracking the main states of dynamic functional connectivity in resting state.Front. Neurosci. 2019; 13: 685Crossref PubMed Scopus (0) Google Scholar]. An extensive comparison of metrics to infer the correct number of states has also recently been performed [118.Vergara V.M. et al.Determining the number of states in dynamic functional connectivity using cluster validity indexes.J. Neurosci. Methods. 2020; 337: 108651Crossref PubMed Scopus (1) Google Scholar].Finally, it is worth noting that, aside from the employed analytical pipeline, an adequate representation of the data is essential because a tensor-based description of whole-brain FC leads to different analytical outcomes compared to a vectorized simplification [119.Mokhtari F. et al.Dynamic functional magnetic resonance imaging connectivity tensor decomposition: a new approach to analyse and interpret dynamic brain connectivity.Brain Connect. 2019; 9: 95-112Crossref PubMed Scopus (1) Google Scholar]. Sliding window-based investigations can be tuned at several stages [7.Preti M.G. et al.The dynamic functional connectome: state-of-the-art and perspectives.Neuroimage. 2017; 160: 41-54Crossref PubMed Scopus (300) Google Scholar]. First, instead of fixed size rectangular windows, averaging across temporal neighbors has been suggested [107.Vergara V.M. et al.An average sliding window correlation method for dynamic functional connectivity.Hum. Brain Mapp. 2019; 40: 2089-2103Crossref PubMed Scopus (4) Google Scholar], and modulated rectangular windows were shown to more efficiently capture dFC metrics [108.Mokhtari F. et al.Sliding window correlation analysis: modulating window shape for dynamic brain connectivity in resting state.Neuroimage. 2019; 189: 655-666Crossref PubMed Scopus (5) Google Scholar]. Time-varying window lengths were also proposed: stationarity with an initial length guess is assessed, and that length is iteratively increased until consistent stationarity across brain regions is achieved [109.Zou H. Yang J. Dynamic thresholding networks for schizophrenia diagnosis.Artif. Intell. Med. 2019; 96: 25-32Crossref PubMed Scopus (0) Google Scholar]. In other work, the BOLD standard deviation across regions was assessed to pinpoint moments putatively associated with a state transition [110.Lee Y. et al.Brain-state extraction algorithm based on the state transition (BEST): a dynamic functional brain network analysis in fMRI study.Brain Topogr. 2019; 32: 897-913Crossref PubMed Scopus (2) Google Scholar]. To replace Pearson's correlation coefficient as the measure of FC, correlating two dFC timecourses (i.e., correlation of correlation timecourses) has been suggested, showcasing fair-to-moderate reliability [111.Zhang H. et al.Test-retest reliability of 'high-order' functional connectivity in young healthy adults.Front. Neurosci. 2017; 11: 439Crossref PubMed Scopus (20) Google Scholar]. Others have proposed to compute the first-order time derivatives between dFC estimates together with dFC itself, using finite difference approximations [112.Espinoza F.A. et al.Characterizing whole brain temporal variation of functional connectivity via zero and first order derivatives of sliding window correlations.Front. Neurosci. 2019; 13: 634Crossref PubMed Scopus (2) Google Scholar]. Framewise estimation methods are also available, such as dynamic conditional correlation [113.Lindquist M.A. et al.Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach.Neuroimage. 2014; 101: 531-546Crossref PubMed Google Scholar] and the combination of variable parameter regression and Kalman filtering [114.Kang J. et al.Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches.Neuroimage. 2011; 56: 1222-1234Crossref PubMed Scopus (73) Google Scholar]. Regarding more conceptually novel metrics, dynamic time warping arguably better accounts for nonlinearity and time lag between brain region timecourses: matching is made between two regional timecourses, enabling differential alignment over time, and the final element of the optimal alignment path is taken as the metric of interest [109.Zou H. Yang J. Dynamic thresholding networks for schizophrenia diagnosis.Artif. Intell. Med. 2019; 96: 25-32Crossref PubMed Scopus (0) Google Scholar]. Two BOLD timecourses may also be viewed in 2D space, from which the gradient magnitude weighted by its phase consists in an FC alternative [115.Faghiri A. et al.Weighted average of shared trajectory: a new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time.J. Neurosci. Methods. 2020; 334: 108600Crossref PubMed Scopus (0) Google Scholar]. Although K-means clustering was originally applied to retrieve CS [13.Allen E.A. et al.Tracking whole-brain connectivity dynamics in the resting state.Cereb. Cortex. 2014; 24: 663-676Crossref PubMed Scopus (982) Google Scholar], several more sophisticated methodological developments have gradually blossomed: for instance, group information-guided ICA is a two-step procedure in which temporally overlapping, spatially independent connectivity patterns shared by the whole investigated subject population are first derived; in a second step, the dominant connectivity state of each subject is extracted with its associated timecourse [116.Du Y. et al.Identifying dynamic functional connectivity biomarkers using GIG-ICA: application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder.Hum. Brain Mapp. 2017; 38: 2683-2708Crossref PubMed Scopus (38