title: Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.
abstract: There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity.
authors.affiliation: Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA. Electronic address: taghia@stanford.edu.
authors.forename: Jalil
authors.initials: J
authors.lastname: Taghia
authors.affiliation: Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA.
authors.forename: Srikanth
authors.initials: S
authors.lastname: Ryali
authors.affiliation: Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA.
authors.forename: Tianwen
authors.initials: T
authors.lastname: Chen
authors.affiliation: Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA.
authors.forename: Kaustubh
authors.initials: K
authors.lastname: Supekar
authors.affiliation: Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA.
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.affiliation: Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA; Department of Neurology & Neurological Sciences, School of Medicine, Stanford, CA 94305, USA; Stanford Neurosciences Institute Stanford University, School of Medicine, Stanford, CA 94305, USA. Electronic address: menon@stanford.edu.
authors.forename: Vinod
authors.initials: V
authors.lastname: Menon
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008962:Models, Theoretical; D009415:Nerve Net / Q000000981:diagnostic imaging / Q000502:physiology*; D055815:Young Adult
keywords: Bayesian inference; Dynamic functional networks; Factor analysis; Hidden Markov model; Resting-state fMRI