Whole-brain estimates of directed connectivity for human connectomics
Stefan Frässle,
Zina M. Manjaly,
Cao T. Do,
Lars Kasper,
Klaas P. Pruessmann,
Klaas E. Stephan
Affiliations
Stefan Frässle
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland; Corresponding author.
Zina M. Manjaly
Department of Neurology, Schulthess, 8008 Zurich, Switzerland & Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
Cao T. Do
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland
Lars Kasper
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich & University of Zurich, 8092 Zurich, Switzerland
Klaas P. Pruessmann
Institute for Biomedical Engineering, ETH Zurich & University of Zurich, 8092 Zurich, Switzerland
Klaas E. Stephan
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, United Kingdom; Max Planck Institute for Metabolism Research, Cologne, Germany
Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation.Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.
