Dynamic effective connectivity

• Published in: NeuroImage (Orlando, Fla.) Vol. 207; p. 116453
• Main Authors: Zarghami, Tahereh S., Friston, Karl J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.02.2020; Elsevier Limited; Elsevier
• Subjects: Bayesian; Bayesian analysis; Between-subjects design; Brain - physiology; Brain architecture; Brain mapping; Brain research; Connectome - methods; Dynamic causal modelling; Dynamic functional connectivity; Effective connectivity; fMRI; Free energy; Functional magnetic resonance imaging; Hidden Markov model; Humans; Image Processing, Computer-Assisted - methods; Inverse problems; Itinerancy; Mathematical models; Medical imaging; Metastability; Models, Neurological; Nerve Net - physiology; Neural networks; Neural Pathways - physiology; Reproducibility of Results; Rest - physiology; Resting state; Spectral DCM; Stable heteroclinic cycle; System theory; Transient dynamics; Bayesian; Itinerancy; Free energy; Dynamic causal modelling; Metastability; Transient dynamics; fMRI; Stable heteroclinic cycle; Resting state; Hidden Markov model; Dynamic functional connectivity; Spectral DCM; Effective connectivity
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.116453

Metastability is a key source of itinerant dynamics in the brain; namely, spontaneous spatiotemporal reorganization of neuronal activity. This itinerancy has been the focus of numerous dynamic functional connectivity (DFC) analyses – developed to characterize the formation and dissolution of distributed functional patterns over time, using resting state fMRI. However, aside from technical and practical controversies, these approaches cannot recover the neuronal mechanisms that underwrite itinerant (e.g., metastable) dynamics—due to their descriptive, model-free nature. We argue that effective connectivity (EC) analyses are more apt for investigating the neuronal basis of metastability. To this end, we appeal to biologically-grounded models (i.e., dynamic causal modelling, DCM) and dynamical systems theory (i.e., heteroclinic sequential dynamics) to create a probabilistic, generative model of haemodynamic fluctuations. This model generates trajectories in the parametric space of EC modes (i.e., states of connectivity) that characterize functional brain architectures. In brief, it extends an established spectral DCM, to generate functional connectivity data features that change over time. This foundational paper tries to establish the model’s face validity by simulating non-stationary fMRI time series and recovering key model parameters (i.e., transition probabilities among connectivity states and the parametric nature of these states) using variational Bayes. These data are further characterized using Bayesian model comparison (within and between subjects). Finally, we consider practical issues that attend applications and extensions of this scheme. Importantly, the scheme operates within a generic Bayesian framework – that can be adapted to study metastability and itinerant dynamics in any non-stationary time series.