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The development of whole-brain models that infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. We have recently introduced a novel generative model of fMRI data, regression dynamic causal modeling (rDCM), that moves towards this goal as it scales gracefully to very large networks. Our approach rests on several modifications and simplifications of the original DCM framework which enable the derivation of a highly efficient variational Bayesian (VB) inversion scheme. Additionally, by incorporating sparsity constraints into the model, rDCM does not require any a priori assumptions about the network’s connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. For this framework, we first demonstrate in comprehensive simulation studies the face validity of the approach and then apply rDCM to various empirical datasets to also investigate its practical utility. Our analyses suggest that effective connection strengths can indeed be inferred from fMRI data using a network with more than 200 regions and 16,000 connections. In particular, we apply rDCM to an fMRI dataset from a simple hand movement paradigm and obtain plausible connectivity patterns among regions of the visuomotor system. This represents an initial demonstration of the feasibility of whole-brain inference on effective connectivity from fMRI data. We anticipate that rDCM may find useful application in connectomics and clinical neuroimaging – for example, for whole-brain phenotyping of individual patients.

Stefan Frässle
Postdoctoral Research Fellow
Translational Neuromodeling Unit
University of Zurich and ETH Zurich

Stefan Frässle – A generative model of whole-brain effective connectivity