A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data

Warnick, Ryan; Guindani, Michele; Erhardt, Erik B.; Allen, Elena A.; Calhoun, Vince D.; Vannucci, Marina

doi:10.1080/01621459.2017.1379404

Cited by 59 publications

(58 citation statements)

References 80 publications

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“…Shrinkage belongs to the more general family of Bayesian approaches. Fully Bayesian approaches, such as that proposed by Warnick et al (2017), use a latent variable model in which the unknown connectivity for each subject gives rise to the unobserved “true” time series plus random noise, and subjects are drawn from some population distribution. In this framework, prior distributions are assumed on the parameters controlling the population distribution and the random noise, including the variance within and across subjects.…”

Section: Introductionmentioning

confidence: 99%

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage

et al. 2018

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Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICC) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully when using partial correlations.

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Section: Introductionmentioning

confidence: 99%

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage

et al. 2018

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“…Firstly, the vast majority of analyses have utilized a sliding-window approach to estimate dynamic connectivity, which tends to produce artificial fluctuations in connectivity [ 19 ]. Recently, some efforts have been made to separate dynamic fluctuations caused by true changes versus those caused by statistical uncertainty [ 13 , 18 , 20 ]. Model-based approaches, such as Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, were investigated by [ 21 ] and found to provide more reliability than sliding-window approaches for dFC detection due to decreased sensitivity to parameter settings and susceptibility to noise.…”

Section: Introductionmentioning

confidence: 99%

Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity

et al. 2018

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Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function.

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“…Moreover, Baker et al [22] applied HMMs to band-limited amplitude envelopes of source reconstructed MEG data and identified brain states that match up to previously established resting-state networks and which fluctuate at time scales two orders of magnitude faster than previously shown. Warnick et al [23] introduce a Bayesian approach to HMMs and dynamic functional connectivity, where they identify latent network states, but they do so in a manner where they assume the network/connectivity structures at each time point are related within one “super-graph”. They impose a sparsity inducing Markov random field prior on the presence of the edges in the super-graph.…”

Section: Introductionmentioning

confidence: 99%

Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models

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Caffo

Pekar

et al. 2019

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The study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring timevarying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subject's most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms.Interest in studying brain networks, where the brain is viewed as a system 2 of interacting regions (nodes) that produce complex behaviors, has grown 3 tremendously over the past decade. Networks have become a popular ap-4 proach towards illustrating both the physiological connections (structural 5 networks) and the coupling of dynamic brain activity (functional networks) 6 linking different areas of the brain. It is within this paradigm shift that sci-7 entists have begun investigating how networks behave in healthy brains and 8 how they are altered in neurological and psychiatric disorders [1]. 9 The study of functional connectivity (FC), or the undirected association 10 between two or more fMRI time series, has come to the forefront of research 11 efforts in the field of neuroimaging. Studies have revealed information about 12 the functional connections between different brain regions and local networks 13 and have provided significant insights into the organization of functional com-14 munication in the brain. Brain networks can be created by performing a FC 15 analysis, where the strength of the relationship between nodes is assessed 16 using the functional magnetic resonance imaging (fMRI) time series associ-17 ated with each node. The nodes can consist of individual voxels, [2, 3, 4], 18 pre-specified regions of interest (ROIs) [5], or sets of regions estimated using 19 independent component analysis (ICA) [6]. The relationship between nodes 20 can be quantified using a variety of metrics, and we present the networks in 21 this paper using both Pea...

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A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data

Cited by 59 publications

References 80 publications

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage

Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity

Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models

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