Title: BRep: Graph-structured Brain Representation Learning via Parametric High-order Dependence Measures
Primary area: learning on graphs and other geometries & topologies
Keywords: Graph Neural Network, Brain Network, Topology Optimization

Abstract: The brain network plays an important role in diagnosing neurological disorders. Brain functional network construction often follows the hand-crafted Correlation Coefficients of blood-oxygen-level-dependent (BOLD) time series without any learnable components. At the same time, most efforts are made to the models that predict individual neurological disorders with the constructed brain network as input, such as graph neural networks. Unfortunately, the fixed brain network may lose critical information during construction and lead to difficulty in performance improvement, even with deliberately designed graph models. From this perspective, the current situation is similar to the machine learning community, i.e., hand-crafted features and learnable predictors, before the advent of representation learning.
In fact, the brain network can be regarded as a graph-structured learnable representation of the brain.  By drawing on representation learning, this paper presents the Brain Representation (BRep) learning problem. To this end, the widely used linear and nonlinear correlations are enhanced to be high-order, parametric, and learnable. The flexible brain representation makes the following predictor simple and leads the framework to possess an end-to-end characteristic. The framework is implemented by combining the parametric correlation and a TopK sparsification. Extensive evaluations demonstrate that the proposed BRep possesses superior performance, high efficiency, and interpretability. The source code is publicly available at https://anonymous.4open.science/r/BRep-demo/

TL;DR: Brain Representation learning problem is presented and sovled via learnable, parametric, and high-order dependence measures.