Graph Neural Networks to Process Brain Connectomes скачать в хорошем качестве

Graph Neural Networks to Process Brain Connectomes

In this tutorial, we will explore how Graph Neural Networks (GNNs) can be applied to brain connectome data. We start by explaining why connectomes, which map neural connections, are naturally represented as graphs. Using a toy connectome dataset, we walk through the process of converting a brain network into a graph structure. Finally, we implement a simple Graph Neural Network (GNN) using PyTorch Geometric to demonstrate how these networks can be trained to learn from neural graphs. By Anna-Lena Krause, Intern at UC Santa Barbara and Master's Student at Maastricht University Tutorial Outline: Slide 1: Introduction --Brief overview of the tutorial's purpose: learn how graph neural networks (GNNs) can be used to model brain connectome data Slide 2: Understanding Connectomes --Definition of a connectome --Importance of connectomes in neuroscience research Slide 3: Graph Neural Networks --Explanation of what GNNs are and their applications (network data, e.g. social, biological) --Why graph neural networks are useful for connectome data (representation of connectomes as graphs → nodes as brain regions, edges as connections) Slide 4: Toy Connectome Dataset --Overview of the toy dataset used for demonstration Coding tutorial (in a Jupyter notebook) --Import toy data set (+ maybe visualize) --Import a GNN from pytorch-geometric --Train a GNN on toy dataset Slide 5 --Summary of what was covered in the tutorial --The significance of applying GNNs to brain connectome data