Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning

• Published in: Medical image analysis Vol. 65; p. 101768
• Main Authors: Mhiri, Islem, Khalifa, Anouar Ben, Mahjoub, Mohamed Ali, Rekik, Islem
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2020; Elsevier BV
• Subjects: Autism; Brain; Brain connectomes; Brain graph fusion; Cognitive tasks; Computer vision; Connectional brain template learning; Data analysis; Embedding; Glutamate receptors; Graph representations; Graph super-resolution; Graph theory; Graphical representations; Graphs; Image registration; Learning; Magnetic resonance imaging; Medical diagnosis; N-Methyl-D-aspartic acid receptors; Neurological diseases; Neurological disorder diagnosis; Nodes; Pattern recognition; Topology; Topology-based residual graph manifold learning; Training; Topology-based residual graph manifold learning; Connectional brain template learning; Graph super-resolution; Brain connectomes; Brain graph fusion; Neurological disorder diagnosis
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2020.101768

•Brain graph super-resolution framework for boosting neurological disorder diagnosis.•Connectional brain template estimation for graph represention in the residual space.•Multi-topology residual graph manifold learning for brain graph super-resolution.•Evaluation on large-scale synthetic and real functional connectomic datasets. [Display omitted] Existing graph analysis techniques generally focus on decreasing the dimensionality of graph data (i.e., removing nodes, edges, or both) in diverse predictive learning tasks in pattern recognition, computer vision, and medical data analysis such as dimensionality reduction, filtering and embedding techniques. However, graph super-resolution is strikingly lacking, i.e., the concept of super-resolving low-resolution (LR) graphs with nr nodes into high-resolution graphs (HR) with nr′>nr nodes. Particularly, learning how to automatically generate HR brain connectomes, without resorting to the computationally expensive MRI processing steps such as image registration and parcellation, remains unexplored. To fill this gap, we propose the first technique to super-resolve undirected fully connected graphs with application to brain connectomes. First, we root our brain graph super-resolution (BGSR) framework in learning how to estimate a centered LR population-based brain graph representation, coined as connectional brain template (CBT), acting as a proxy in the target BGSR task. Specifically, we hypothesize that the estimation of a well-representative and centered CBT would help better capture the individuality of each LR brain graph via its residual distance from the population-based CBT. This will eventually allow an accurate identification of the most similar individual graphs to a new testing graph in the LR domain for the target prediction task. Second, we leverage the estimated LR CBT (i.e., population mean) to derive residual LR brain graphs, capturing the deviation of all subjects from the estimated CBT. Third, we learn multi-topology LR graph manifolds using different graph topological measurements (e.g., degree, closeness, betweenness) by estimating residual LR similarity matrices modeling the relationship between pairs of residual LR graphs. These are then fused so we can effectively identify for each testing LR subject its most K similar training LR graphs. Last, the missing testing HR graph is predicted by averaging the HR graphs of the K selected training subjects. Predicted HR from LR functional brain graphs boosted classification results for autistic subjects by 16.48% compared with LR functional graphs.