Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observation

Abstract

Predicting the evolution trajectories of brain data from a baseline timepoint is a challenging task in the fields of neuroscience and neuro-disorders. While existing predictive frameworks are able to handle Euclidean structured data (i.e, brain images), they might fail to generalize to geometric non-Euclidean data such as brain networks. Recently, a seminal brain network evolution prediction framework was introduced capitalizing on learning how to select the most similar training network samples at baseline to a given testing baseline network for the target prediction task. However, this rooted the sample selection step in using Euclidean or learned similarity measure between vectorized training and testing brain networks. Such sample connectomic representation might include irrelevant and redundant features that could mislead the training sample selection step. Undoubtedly, this fails to exploit and preserve the topology of the brain connectome. To overcome this major drawback, we propose Residual Embedding Similarity-Based Network selection (RESNets) for predicting brain network evolution trajectory from a single timepoint. RESNets first learns a compact geometric embedding of each training and testing sample using adversarial connectome embedding network. This nicely reduces the high-dimensionality of brain networks while preserving their topological properties via graph convolutional networks. Next, to compute the similarity between subjects, we introduce the concept of a connectional brain template (CBT), a fixed network reference, where we further represent each training and testing network as a deviation from the reference CBT in the embedding space. As such, we select the most similar training subjects to the testing subject at baseline by comparing their learned residual embeddings with respect to the pre-defined CBT. Once the best training samples are selected at baseline, we simply average their corresponding brain networks at follow-up timepoints to predict the evolution trajectory of the testing network. Our experiments on both healthy and disordered brain networks demonstrate the success of our proposed method in comparison

Graph Neural Networks in Network Neuroscience

“…Models that foresee the evolution of brain connectivity from a single baseline graph can be especially useful for both scientific discovery and clinical decisionmaking (23,24). Therefore, prior works proposed different GNN-based architectures that can be divided into two families: dichotomized learning-based models (47,48) and end-toend learning-based models (46). These works aim to produce a trajectory either represented with one brain graph such as predicting brain connectivities of an Alzheimer's disease patient at 9-month after first scans or multiple follow-up brain graphs acquired at different timepoints (Figure 2-C).…”

“…Challenges and insights. Although (47,48) methods can reliably predict the future brain connectivities, their performance is still less promising since their dichotomized learning strategy limits the scalability to predict jointly the follow-up brain graphs. Even though ( 46) is an end-to-end learning framework, it is still based on the edge convolution operation which hinders its scalability in terms of training time when applied on large-scale graphs.…”

Analyzing Brain Morphology in Alzheimer’s Disease Using Discriminative and Generative Spiral Networks

“…By construction, triangular meshes are undirected graphs, with analogous edges, and their intersections are interpreted as vertices. Several studies (Bessadok and Rekik, 2018;Fornito et al, 2015;Göktaş et al, 2020;Gurbuz and Rekik, 2020;Nebli and Rekik, 2020;Yang et al, 2020) have demonstrated that graphs derived from different types of brain-related connectivity, functional or structural, are more robust in accuracy and computation time, versus traditional neuroimaging methods.…”