2014 Jul 1
A DCM for Resting State fMRI
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A DCM for Resting State fMRI
This technical note introduces a dynamic causal model (DCM) for resting state fMRI time series based upon observed functional connectivity--as measured by the cross spectra among different brain regions. This DCM is based upon a deterministic model that generates predicted crossed spectra from a biophysically plausible model of coupled neuronal fluctuations in a distributed neuronal network or graph. Effectively, the resulting scheme finds the best effective connectivity among hidden neuronal states that explains the observed functional connectivity among haemodynamic responses. This is because the cross spectra contain all the information about (second order) statistical dependencies among regional dynamics. In this note, we focus on describing the model, its relationship to existing measures of directed and undirected functional connectivity and establishing its face validity using simulations. In subsequent papers, we will evaluate its construct validity in relation to stochastic DCM and its predictive validity in Parkinson's and Huntington's disease.
Bayesian; Dynamic causal modelling; Effective connectivity; Functional connectivity; Graph; Resting state; fMRI.
Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
This schematic illustrates the relationship between different formulations of dependencies among multivariate time series—of the sort used in fMRI. The upper panel illustrates the form of a state space
model that comprises differential equations coupling hidden states (first equation) and an observer equation mapping hidden states x( t) to observed responses y( t) (second equation). Crucially, both the motion of hidden states and responses are subject to random fluctuations, also known as state v( t) and observation e( t) noise. The form of these fluctuations are modelled in terms of their cross covariance functions ρ( t) of time t or cross spectral density functions g( ω) of radial frequency ω, as shown in the lower equations. Given this state space model and its parameters θ (which include effective connectivity) one can now parameterise a series of representations of statistical dependencies among successive responses as shown in the second row. These include convolution and autoregressive formulations shown on the left and right respectively—in either time (light green) or frequency (light purple) space. The mapping between these representations rests on the Fourier transform, denoted by F and its inverse. For example, given the equations of motion and observer function of the state space model, one can compute the convolution kernels applied to state noise that produce changes in the response variables. This allows one to express observed responses in terms of a convolution of hidden fluctuations and observation noise. The Fourier transform of these convolution kernels κ( t) is called a transfer function K( ω). Note that the transfer function in the convolution formulation K( ω) maps from fluctuations in hidden states to response variables, whereas the directed transfer function in the autoregressive formulation S( ω) maps directly among different response variables. These representations can be used to generate second order statistics or measures that summarise the dependencies as shown in the third row; for example, cross covariance functions and cross spectra. The normalised or standardised variants of these measures are shown in the lower row and include the cross correlation function (in time) or coherence (in frequency). The equations show how the various representations can be derived from each other, where Fourier transforms of variables are (generally) in uppercase such that F( x( t)) = X( ω). All variables are either vector or matrix functions of time or frequency. For simplicity, the autoregressive formulations shown in discrete form for the univariate case (the same algebra applies to the multivariate case but the notation becomes more complicated). Here, z( t) is a unit normal innovation. Finally, note the Granger causality is only appropriate for bivariate time series. In this figure, ⊗ corresponds to a convolution operator, * denotes the complex conjugate transpose, 〈⋅〉 denotes expectation and ~ denotes discrete time lagged forms (as shown in the upper inserts). This particular layout of models and associated sample statistics in this figure is greatly simplified and is just meant to contextualise commonly used measures in fMRI functional connectivity research. The relationships among the sample statistics and models could be nuanced in many ways; for example, there are continuous time formulations of autoregressive models that are closely related to formulations in terms of stochastic differential equations. Furthermore, discrete time models are not necessarily linear—we have focused on linear models because the cross spectra and covariance functions (second order statistics) are derived easily under local linearity assumptions (Robinson et al. 2004).
This figure summarises the results of simulating fMRI responses to endogenous fluctuations. The simulation was based upon a simple three-region hierarchy, shown on the lower right, with positive effective connectivity (black) in the forward or ascending direction and negative (red) in the backward or descending direction. The three regions were driven by endogenous fluctuations (upper right panel) generated from an AR(1) process within autoregression coefficient of one half. These fluctuations caused distributed variations in neuronal states and consequent changes in haemodynamic states – shown in cyan – (upper right panel), which produce the final fMRI response (lower left panel). These simulations were based upon 256 scans with a repetition time (TR) of two seconds (only the first 256 s are show).
This figure reports the results of Bayesian model inversion using data shown in the previous figure. The posterior means (grey bars) and 90% confidence intervals (pink bars) are shown with the true values (black bars) in the upper panel. The light grey bars depict intrinsic or self connections in terms of their log scaling (such that a value of zero corresponds to a scaling of one). The dark grey bars report extrinsic connections measured in Hz. It can be seen that, largely, the true values fall within the Bayesian confidence intervals. These estimates produced predictions (solid lines) of sample cross spectra (dotted lines) and cross covariance functions, shown in the lower panels. The real values are shown on the left and the imaginary values on the right. Imaginary values are produced only by coupling between regions. The first half of these responses and predictions correspond to the cross spectra between all pairs of regions, while the second half are the equivalent cross covariance functions (scaled by a factor of eight).
This figure reports the results of Monte Carlo simulations assessing the accuracy of posterior estimates in terms of root mean square error (RMS) from the true value is. The left panel shows the results of 32 simulations (red diamonds) for different run or session lengths. The average root mean square error (black bars) decreases with increasing run length to reach acceptable (less than 0.1 Hz) levels after about 300 scans. The right panels report the Bayesian parameter averages of the effective connection strengths using the same format as the previous figure. Note that because we have pooled over 32 simulated subjects, the confidence intervals are much smaller. Note also the characteristic shrinkage one obtains with Bayesian estimators. Finally, note the similarity between the Bayesian parameter averages from long runs (upper panel) and shorter runs (lower panel), of 1024 and 256 scans, respectively.
This figure reports the results of a simulated group comparison study of two groups of 16 subjects (with 512 scans per subject). The upper left panel shows the Bayesian parameter averages of the differences using the same format as previous figures. It can be seen that decreases in the extrinsic backward connections from the second to the first region (fourth parameter) have been estimated accurately, while the decrease in the self connection of the first region is underestimated. The equivalent classical inference—based upon the
t-statistic is shown on the upper right. Here the posterior means from each of 32 subjects were used as summary statistics and entered into a series of univariate t-tests to assess differences in group means. The red lines correspond to significance thresholds at a nominal false-positive rate of p = 0.05 corrected (solid lines) and uncorrected (broken lines). The lower panels report the results of a canonical variates analysis (the equivalent multivariate classical inference) using the same summary statistics. The corresponding canonical variate shows reliable group discrimination (lower left), while the canonical vector has correctly identified the greatest effect in the first backward connections (lower right). The effect of group was highly significant with a canonical correlation of r = 0.0198; p = 0.0003.
Summary of empirical time series used for the illustrative analysis. The time series (right-hand panels) from six regions show experimental effects of visual motion and attention to visual motion (see main text). These time series are the principal eigenvariates of regions identified using a conventional SPM analysis (upper left insert). These time series we used to invert a DCM with the architecture shown in the lower left panel. See Table 1 for details.
This figure summarises the results of model inversion using the model and data of the previous figure. The upper left panel's show the predicted and observed data features using the same format as Fig. 3. The lower left panels show the predicted and observed auto spectra in the six regions, where spectral peaks induced by experimental manipulations are highlighted with cyan circles. The underlying auto spectra predicted for the hidden neuronal states (lower right) show a greater preponderance of higher frequencies with a 1/f form. The right panel reports the posterior expectations of effective connectivity using the same format as Fig. 2. The key thing to note here is that negative or inhibitory values are restricted to backwards or descending connections.
All figures (7)
Construct Validation of a DCM for Resting State fMRI
A Razi et al.
Neuroimage 106, 1-14.
Recently, there has been a lot of interest in characterising the connectivity of resting state brain networks. Most of the literature uses functional connectivity to exam …
Generalised Filtering and Stochastic DCM for fMRI
B Li et al.
Neuroimage 58 (2), 442-57.
This paper is about the fitting or inversion of dynamic causal models (DCMs) of fMRI time series. It tries to establish the validity of stochastic DCMs that accommodate r …
Network Discovery With DCM
KJ Friston et al.
Neuroimage 56 (3), 1202-21.
This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers t …
On Nodes and Modes in Resting State fMRI
KJ Friston et al.
Neuroimage 99 (100), 533-47.
This paper examines intrinsic brain networks in light of recent developments in the characterisation of resting state fMRI timeseries--and simulations of neuronal fluctua …
Dynamic Causal Modelling: A Critical Review of the Biophysical and Statistical Foundations
J Daunizeau et al.
Neuroimage 58 (2), 312-22.
The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced changes in functional integration among brain regions. This requires (i …
PubMed Central articles
Effects of Exogenous Testosterone Application on Network Connectivity Within Emotion Regulation Systems
M Votinov et al.
Sci Rep 10 (1), 2352.
Studies with steroid hormones underlined the vital role of testosterone on social-emotional processing. However, there is still a lack of studies investigating whether te …
Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database
H Kurashige et al.
Front Hum Neurosci 13, 457.
To characterize each cognitive function
per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to e …
Current Challenges in Translational and Clinical fMRI and Future Directions
Front Psychiatry 10, 924.
Translational neuroscience is an important field that brings together clinical praxis with neuroscience methods. In this review article, the focus will be on functional n …
fMRI in Non-human Primate: A Review on Factors That Can Affect Interpretation and Dynamic Causal Modeling Application
DB Jovellar et al.
Front Neurosci 13, 973.
Dynamic causal modeling (DCM)-a framework for inferring hidden neuronal states from brain activity measurements (e. g., fMRI) and their context-dependent modulation-was d …
Applications of Dynamic Functional Connectivity to Pain and Its Modulation
EA Necka et al.
Pain Rep 4 (4), e752.
Since early work attempting to characterize the brain's role in pain, it has been clear that pain is not generated by a specific brain region, but rather by coordinated a …
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Beal M.J. University College London; 2003. Variational Algorithms for Approximate Bayesian Inference. (PhD. Thesis)
Research Support, Non-U.S. Gov't
Brain Mapping / methods
Data Interpretation, Statistical
Image Interpretation, Computer-Assisted / methods
Magnetic Resonance Imaging / methods
Motion Perception / physiology
Reproducibility of Results
Sensitivity and Specificity
Visual Cortex / physiology