Assessing parameter identifiability for dynamic causal modeling of fMRI data.

Carolin Arand,Benjamin Seeber,Bjã¶Rn Schelter,Stefan Klã¶Ppel,Jens Timmer,Elisa Scheller

doi:10.3389/fnins.2015.00043

Carolin Arand, Benjamin Seeber + Show 4 more

Open Access

https://doi.org/10.3389/fnins.2015.00043

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Abstract

Deterministic dynamic causal modeling (DCM) for fMRI data is a sophisticated approach to analyse effective connectivity in terms of directed interactions between brain regions of interest. To date it is difficult to know if acquired fMRI data will yield precise estimation of DCM parameters. Focusing on parameter identifiability, an important prerequisite for research questions on directed connectivity, we present an approach inferring if parameters of an envisaged DCM are identifiable based on information from fMRI data. With the freely available “attention to motion” dataset, we investigate identifiability of two DCMs and show how different imaging specifications impact on identifiability. We used the profile likelihood, which has successfully been applied in systems biology, to assess the identifiability of parameters in a DCM with specified scanning parameters. Parameters are identifiable when minima of the profile likelihood as well as finite confidence intervals for the parameters exist. Intermediate epoch duration, shorter TR and longer session duration generally increased the information content in the data and thus improved identifiability. Irrespective of biological factors such as size and location of a region, attention should be paid to densely interconnected regions in a DCM, as those seem to be prone to non-identifiability. Our approach, available in the DCMident toolbox, enables to judge if the parameters of an envisaged DCM are sufficiently determined by underlying data without priors as opposed to primarily reflecting the Bayesian priors in a SPM–DCM. Assessments with the DCMident toolbox prior to a study will lead to improved identifiability of the parameters and thus might prevent suboptimal data acquisition. Thus, the toolbox can be used as a preprocessing step to provide immediate statements on parameter identifiability.

Highlights

Connectivity analyses of fMRI data are noninvasive tools to investigate interactions within a network of brain regions (Smith, 2012)
A decrease in TR combined with a moderate increase in session duration, i.e., the acquisition of additional 90 volumes, systematically improved parameter identifiability of the forward as well as the backward model, such that a precise parameter estimation should be warranted when acquiring real fMRI data with these settings
ON THE DCMident TOOLBOX The DCMident toolbox presented in the current report provides solutions to ensure Dynamic Causal Modeling (DCM) parameter identifiability based on fMRI acquisition specifications

Summary

Introduction

Connectivity analyses of fMRI data are noninvasive tools to investigate interactions within a network of brain regions (Smith, 2012). Functional connectivity in task or resting state fMRI data (Biswal et al, 1995, 2010; Lowe et al, 1998; Van de Ven et al, 2004; Calhoun and Adali, 2012) can be investigated e.g., by correlating the time series of activated regions, though interpretation toward causality is limited here (Stephan, 2004). The application of effective connectivity methods such as Dynamic Causal Modeling (DCM; Friston et al, 2003) provides insights into the causality of interactions between certain brain areas (Friston, 2011; Stephan and Roebroeck, 2012). It is difficult to determine if a given research question can at least in principal be answered using DCM prior to actual data acquisition

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