Functional connectivity dynamics: Modeling the switching behavior of the resting state

By Enrique C. A. Hansen, Demian Battaglia, Andreas Spiegler, Gustavo Deco, Viktor Jirsa. NeuroImage, 2015

542 citations recorded by the source corpus.

Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state. Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Here we show that this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states. We also show that computational models optimized to fit time-averaged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. We then demonstrate that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)). Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d63b341ec9a4dd463bc0d33962811924d627c348

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Citations in the corpus

Linking Structure and Function in Macroscale Brain Networks. (2020, unknown): “As a result, thesemodels output realistic temporal waveforms [70,71], power spectra [72], and covariance patterns [28,49].”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “Models can involve highly parameterized simulations of regional activity (Hansen et al., 2015; Messe et al., 2014; Sanz-Leon et al., 2015), or sparser analytic predictions of FC using graph metrics of mutual communication (Goni et al., 2014; Grayson et al., 2016) (although, both approaches appear…”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, methodology): “In Hansen et al. (2015), the dynamic mean-field model was modified to adopt enhanced non-linearity in order to obtain a better fit with the FCD matrix.”
Dynamic models of large-scale brain activity (2017, background): “These technologies may facilitate a new era of testing whole brain models 105,138–140 .”
Network-Level Structure-Function Relationships in Human Neocortex (2016, methodology): “In addition, we investigate how network-level structure-function relationships determine the interaction among resting-state networks (RSNs; Supplementary Fig.”
Conscious Processing and the Global Neuronal Workspace Hypothesis (2020, background): “Simulations indicate that the spontaneous activity patterns already present during anesthesia may undergo a sudden phase transition toward a vastly more diverse and dynamic repertoire of states in the awake state (Hansen et al., 2015).”