1,462 research outputs found
Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models
Recent studies on analyzing dynamic brain connectivity rely on sliding-window
analysis or time-varying coefficient models which are unable to capture both
smooth and abrupt changes simultaneously. Emerging evidence suggests
state-related changes in brain connectivity where dependence structure
alternates between a finite number of latent states or regimes. Another
challenge is inference of full-brain networks with large number of nodes. We
employ a Markov-switching dynamic factor model in which the state-driven
time-varying connectivity regimes of high-dimensional fMRI data are
characterized by lower-dimensional common latent factors, following a
regime-switching process. It enables a reliable, data-adaptive estimation of
change-points of connectivity regimes and the massive dependencies associated
with each regime. We consider the switching VAR to quantity the dynamic
effective connectivity. We propose a three-step estimation procedure: (1)
extracting the factors using principal component analysis (PCA) and (2)
identifying dynamic connectivity states using the factor-based switching vector
autoregressive (VAR) models in a state-space formulation using Kalman filter
and expectation-maximization (EM) algorithm, and (3) constructing the
high-dimensional connectivity metrics for each state based on subspace
estimates. Simulation results show that our proposed estimator outperforms the
K-means clustering of time-windowed coefficients, providing more accurate
estimation of regime dynamics and connectivity metrics in high-dimensional
settings. Applications to analyzing resting-state fMRI data identify dynamic
changes in brain states during rest, and reveal distinct directed connectivity
patterns and modular organization in resting-state networks across different
states.Comment: 21 page
Advancing functional connectivity research from association to causation
Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures
Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models
abstract: The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons.View the article as published at http://journal.frontiersin.org/article/10.3389/fnins.2013.00070/ful
A Predictor-Informed Multi-Subject Bayesian Approach for Dynamic Functional Connectivity
Time Varying Functional Connectivity (TVFC) investigates how the interactions
among brain regions vary over the course of an fMRI experiment. The transitions
between different individual connectivity states can be modulated by changes in
underlying physiological mechanisms that drive functional network dynamics,
e.g., changes in attention or cognitive effort as measured by pupil dilation.
In this paper, we develop a multi-subject Bayesian framework for estimating
dynamic functional networks as a function of time-varying exogenous
physiological covariates that are simultaneously recorded in each subject
during the fMRI experiment. More specifically, we consider a dynamic Gaussian
graphical model approach, where a non-homogeneous hidden Markov model is
employed to classify the fMRI time series into latent neurological states,
borrowing strength over the entire time course of the experiment. The
state-transition probabilities are assumed to vary over time and across
subjects, as a function of the underlying covariates, allowing for the
estimation of recurrent connectivity patterns and the sharing of networks among
the subjects. Our modeling approach further assumes sparsity in the network
structures, via shrinkage priors. We achieve edge selection in the estimated
graph structures, by introducing a multi-comparison procedure for
shrinkage-based inferences with Bayesian false discovery rate control. We apply
our modeling framework on a resting-state experiment where fMRI data have been
collected concurrently with pupillometry measurements, leading us to assess the
heterogeneity of the effects of changes in pupil dilation, previously linked to
changes in norepinephrine-containing locus coeruleus, on the subjects'
propensity to change connectivity states
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