Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint

09/28/2020
by   Ahmed Nebli, et al.
10

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 of brain dysconnectivity evolution. Indeed, predicting the longitudinal (i.e., time-dependent ) brain dysconnectivity as it emerges and evolves over time from a single timepoint can help design personalized treatments for disordered patients in a very early stage. Despite its significance, evolution models of the brain graph are largely overlooked in the literature. Here, we propose EvoGraphNet, the first end-to-end geometric deep learning-powered graph-generative adversarial network (gGAN) for predicting time-dependent brain graph evolution from a single timepoint. Our EvoGraphNet architecture cascades a set of time-dependent gGANs, where each gGAN communicates its predicted brain graphs at a particular timepoint to train the next gGAN in the cascade at follow-up timepoint. Therefore, we obtain each next predicted timepoint by setting the output of each generator as the input of its successor which enables us to predict a given number of timepoints using only one single timepoint in an end- to-end fashion. At each timepoint, to better align the distribution of the predicted brain graphs with that of the ground-truth graphs, we further integrate an auxiliary Kullback-Leibler divergence loss function. To capture time-dependency between two consecutive observations, we impose an l1 loss to minimize the sparse distance between two serialized brain graphs. A series of benchmarks against variants and ablated versions of our EvoGraphNet showed that we can achieve the lowest brain graph evolution prediction error using a single baseline timepoint. Our EvoGraphNet code is available at http://github.com/basiralab/EvoGraphNet.

READ FULL TEXT
research
09/23/2020

Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer

Foreseeing the brain evolution as a complex highly inter-connected syste...
research
10/06/2021

Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates

Learning how to estimate a connectional brain template(CBT) from a popul...
research
09/23/2020

Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph

Several works based on Generative Adversarial Networks (GAN) have been r...
research
10/06/2021

StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis

Synthesizing multimodality medical data provides complementary knowledge...
research
09/13/2022

Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification

A central challenge in training one-shot learning models is the limited ...
research
05/03/2022

Learning Label Initialization for Time-Dependent Harmonic Extension

Node classification on graphs can be formulated as the Dirichlet problem...

Please sign up or login with your details

Forgot password? Click here to reset