1 citations · 1 across the 9 of their papers we have counts for
11 papers
Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint
Ahmed Nebli, Ugur Ali Kaplan, Islem Rekik
Learning how to predict the brain connectome (i.e. graph) development and aging is of paramount importance for charting the future of within-disorder and cross-disorder landscape o…
Multi-View Brain HyperConnectome AutoEncoder For Brain State Classification
Alin Banka, Inis Buzi, Islem Rekik
Graph embedding is a powerful method to represent graph neurological data (e.g., brain connectomes) in a low dimensional space for brain connectivity mapping, prediction and classi…
Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State Profiling
Mustafa Saglam, Islem Rekik
The individual brain can be viewed as a highly-complex multigraph (i.e. a set of graphs also called connectomes), where each graph represents a unique connectional view of pairwise…
Adversarial Brain Multiplex Prediction From a Single Network for High-Order Connectional Gender-Specific Brain Mapping
Ahmed Nebli, Islem Rekik
Brain connectivity networks, derived from magnetic resonance imaging (MRI), non-invasively quantify the relationship in function, structure, and morphology between two brain region…
Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer
Zeynep Gurler, Ahmed Nebli, Islem Rekik
Foreseeing the brain evolution as a complex highly inter-connected system, widely modeled as a graph, is crucial for mapping dynamic interactions between different anatomical regio…
Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observation
Ahmet Serkan Goktas, Alaa Bessadok, Islem Rekik
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 n…