Dynamic functional connectivity using state-based dynamic community structure: Method and application to opioid analgesia

Robinson, Lucy; Atlas, Lauren Y.; Wager, Tor D.

doi:10.1016/j.neuroimage.2014.12.034

Cited by 48 publications

(42 citation statements)

References 75 publications

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“…Specific examples of this would include using techniques derived from graph theory [53][54][55][56], connectivity analysis [57], or cognitive architectures [58,59].…”

Section: Discussionmentioning

confidence: 99%

Social influence and the brain: persuasion, susceptibility to influence and retransmission

Cascio

Scholz

Falk

2015

Current Opinion in Behavioral Sciences

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Social influence is an important topic of research, with a particularly long history in the social sciences. Recently, social influence has also become a topic of interest among neuroscientists. The aim of this review is to highlight current research that has examined neural systems associated with social influence, from the perspective of being influenced as well as influencing others, and highlight studies that link neural mechanisms with real-world behavior change beyond the laboratory. Although many of the studies reviewed focus on localizing brain regions implicated in influence within the lab, we argue that approaches that account for networks of brain regions and that integrate neural data with data beyond the laboratory are likely to be most fruitful in understanding influence. AbstractSocial influence is an important topic of research, with a particularly long history in the social sciences. Recently, social influence has also become a topic of interest among neuroscientists.The aim of this review is to highlight current research that has examined neural systems associated with social influence, from the perspective of being influenced as well as influencing others, and highlight studies that link neural mechanisms with real-world behavior change beyond the laboratory. Although many of the studies reviewed focus on localizing brain regions implicated in influence within the lab, we argue that approaches that account for networks of brain regions and that integrate neural data with data beyond the laboratory are likely to be most fruitful in understanding influence.

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“…Specific examples of this would include using techniques derived from graph theory [53][54][55][56], connectivity analysis [57], or cognitive architectures [58,59].…”

Section: Discussionmentioning

confidence: 99%

Social influence and the brain: persuasion, susceptibility to influence and retransmission

Cascio

Scholz

Falk

2015

Current Opinion in Behavioral Sciences

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“…HMMs have been previously largely applied to EEG and MEG data, e.g., (Baker et al, 2014). However, their applications to dynamic functional connectivity analysis of the fMRI data have been relatively limited (Højen-Sørensen and Hansen, 2000; Eavani et al, 2013; Suk et al, 2015; Robinson et al, 2015) given their potentials. Crucially, HMMs assume that observations are coupled through a set of temporally-dependent latent-state (hidden-state) variables, where the dependence is expressed as a first-order Markov chain.…”

Section: Introductionmentioning

confidence: 99%

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI

et al. 2017

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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.

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“…Many authors have developed methods, e.g., based on nonstationary graphical Markov modeling with temporal smoothness [32–36], or state-space modeling combined with so-called stochastic blockmodels or related probabilistic models [37–40], whose applications to brain imaging data can also be found (e.g., [41, 42]). In contrast to such model-based methods, PCA-based eigenconnectivity analysis like MCF puts more emphasis on extracting the relevant aspects of data in a condensed manner rather than fully modeling and predicting the network (connectivity) changes.…”

Section: Discussionmentioning

confidence: 99%

Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

et al. 2016

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Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated “eigenconnectivity” patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra- and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module-based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods.

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Dynamic functional connectivity using state-based dynamic community structure: Method and application to opioid analgesia

Cited by 48 publications

References 75 publications

Social influence and the brain: persuasion, susceptibility to influence and retransmission

Social influence and the brain: persuasion, susceptibility to influence and retransmission

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI

Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

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