A Graph Neural Network Framework for Causal Inference in Brain Networks

• URL: http://arxiv.org/abs/2010.07143v1
• Date: Wed, 14 Oct 2020 15:01:21 GMT
• Title: A Graph Neural Network Framework for Causal Inference in Brain Networks
• Authors: Simon Wein, Wilhelm Malloni, Ana Maria Tom\'e, Sebastian M. Frank, Gina-Isabelle Henze, Stefan W\"ust, Mark W. Greenlee, Elmar W. Lang
• Abstract summary: A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static backbone. We present a graph neural network (GNN) framework to describe functional interactions based on structural anatomical layout. We show that GNNs are able to capture long-term dependencies in data and also scale up to the analysis of large-scale networks.
• Score: 0.3392372796177108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions learned by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model's accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier and different study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Therewith this approach can be promising for the characterization of the information flow in brain networks.

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