Comparing hemodynamic models with DCM

By Klaas E. Stephan, Nikolaus Weiskopf, Peter M. Drysdale, Peter A. Robinson, Karl J. Friston. NeuroImage, 2007

458 citations recorded by the source corpus.

The classical model of blood oxygen level dependent (BOLD) responses by Buxton et al. (1998) has been very important in providing a biophysically plausible framework for explaining different aspects of hemodynamic responses. It also plays an important role in the hemodynamic forward model for dynamic causal modeling (DCM) of fMRI data. A recent study by Obata et al. (2004) linearized the BOLD signal equation and suggested a revised form for the model coefficients. In this paper, we show that the classical and revised models are special cases of a generalized model. The BOLD signal equation of this generalized model can be reduced to that of the classical Buxton model by simplifying the coefficients or can be linearized to give the Obata model. Given the importance of hemodynamic models for investigating BOLD responses and analyses of effective connectivity with DCM, the question arises which formulation is the best model for empirically measured BOLD responses. In this article, we address this question by embedding different variants of the BOLD signal equation in a well-established DCM of functional interactions among visual areas. This allows us to compare the ensuing models using Bayesian model selection. Our model comparison approach had a factorial structure, comparing eight different hemodynamic models based on (i) classical vs. revised forms for the coefficients, (ii) linear vs. non-linear output equations, and (iii) fixed vs. free parameters, ε, for region-specific ratios of intra- and extravascular signals. Using fMRI data from a group of twelve subjects, we demonstrate that the best model is a non-linear model with a revised form for the coefficients, in which ε is treated as a free parameter.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4984e7bebcb8d39a55ca1dd80a745e4868864906

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In Cognitive Neuroscience

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Citations in the corpus

Modulating cortical connectivity in stroke patients by rTMS assessed with fMRI and dynamic causal modeling (2010, methodology): “The “winning”model should then represent the best balance between the relative fit and complexity of the model (Stephan et al., 2007a,b).”
Ten Simple Rules for Dynamic Causal Modelling (2009, unknown): “The response profile of such an area can be explained by taskdependent modulation of (i) one or several afferent connections from other areas identifiedby testing for amain effect of stimulus or of (ii) its self-connectivity (Allen et al., 2008; Stephan et al., 2007a).”
Comparing Dynamic Causal Models using AIC, BIC and Free Energy (2012, methodology): “In this regime it may be a better idea to make inferences over families of models (Penny et al., 2010) and to look for consistent differences over a group of subjects (Stephan et al., 2009).”
Bayesian model selection for group studies - Revisited (2014, unknown): “This random effects BMS procedure complements fixed effects procedures that assume that subjects are sampled from a homogenous population with one (unknown) model (cf. the log group Bayes factor that sums log-evidences over subjects; Stephan et al., 2007).”
Behavioral / Systems / Cognitive Striatal Prediction Error Modulates Cortical Coupling (2010, unknown): “This method has been shown to be considerably more robust than either the conventional fixed-effects analysis using the group Bayes factor (Stephan et al., 2007b) or frequentist tests applied to model evidences, especially in the presence of outliers (Stephan et al., 2009).”
Building a Science of Individual Differences from fMRI. (2016, background): “Factors such as vasodilatory signaling, blood vessel stiffness, neurovascular coupling delay, venous transit time, and the time constant of autoregulatory feedback contribute to this variability [48,49]; these factors are particularly affected by aging and disease [50].”
A DCM for resting state fMRI (2014, methodology): “(1) andtheusualhaemodynamic equationsof motion (Stephan et al., 2007).”
A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI (2019, background): “where ΔR2e and ΔR * 2i is the change in each compartment's R * 2 between activation and rest, εh 1⁄4 Si Se is the ratio of intra-to extra-vascular signal contributions and V1 is the venous blood volume fraction following activation (Obata et al., 2004; Stephan et al., 2007).”
Construct validation of a DCM for resting state fMRI (2015, background): “Inthispaper,weareonlyconcernedwiththe ﬁ rst(neuronal) part, without changing the second (haemodynamic) part: see Stephan et al. (2007).”
Handbook of Functional MRI Data Analysis: Index (2011, background): “The details of this estimation are quite complicated, and the interested reader should consult (Friston et al., 2003; Penny et al., 2004; Stephan et al., 2007) for more details.”
Nonlinear Dynamic Causal Models for fMRI (2008, unknown): “In the past, a variety of models have been proposed for inferring effective connectivity from neuroimaging data, including regressionbased models like psycho-physiological interactions (PPI; Friston et al., 1997), structural equation modelling (SEM; McIntosh and Gonzalez-Lima 1994; Büchel and Fristo”
Analysing connectivity with Granger causality and dynamic causal modelling (2013, methodology): “Neuronal activity is then transformed through a haemodynamic model (with region-specific parameters) to model measured responses [37].”
Functional and Effective Connectivity: A Review (2011, unknown): “hypotheses to be tested, where each DCM corresponds to a speciﬁc hypothesis about functional brain architectures (e.g., Acs and Greenlee, 2008; Allen et al., 2008; Grol et al., 2007; Heim et al., 2009; Smith et al., 2006; Stephan et al., 2007; Sum-merﬁeld and Koechlin, 2008).”
Dynamic intra- and interhemispheric interactions during unilateral and bilateral hand movements assessed with fMRI and DCM (2008, methodology): “Similar mechanisms have been observed also for cognitive tasks, e.g. in letter detection tasks or spatial judgement tasks (Stephan et al., 2007a).”
VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data (2014, background): “DCM also includes the effect of the hemodynamic response function, which effectively performs a temporal convolution operation on neural states [28].”