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

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 this http URL.

[1]  Islem Rekik,et al.  Joint Prediction and Classification of Brain Image Evolution Trajectories from Baseline Brain Image with Application to Early Dementia , 2018, MICCAI.

[2]  Dinggang Shen,et al.  Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI , 2017, NeuroImage.

[3]  John G. Csernansky,et al.  Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults , 2010, Journal of Cognitive Neuroscience.

[4]  Yoshua Bengio,et al.  Generative Adversarial Networks , 2014, ArXiv.

[5]  Nikos Komodakis,et al.  Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[7]  Islem Rekik,et al.  Pairing-based Ensemble Classifier Learning using Convolutional Brain Multiplexes and Multi-view Brain Networks for Early Dementia Diagnosis , 2017, CNI@MICCAI.

[8]  Islem Rekik,et al.  Joint functional brain network atlas estimation and feature selection for neurological disorder diagnosis with application to autism , 2019, Medical Image Anal..

[9]  Jyoti Islam,et al.  A Novel Deep Learning Based Multi-class Classification Method for Alzheimer's Disease Detection Using Brain MRI Data , 2017, BI.

[10]  Islem Rekik,et al.  Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation , 2019, MICCAI.

[11]  Mohamad Habes,et al.  A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal MRI , 2019, ArXiv.

[12]  Yudong Zhang,et al.  Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning , 2015, Front. Comput. Neurosci..

[13]  Dimitris N. Metaxas,et al.  StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[14]  Xiaogang Wang,et al.  Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Silke Schicktanz,et al.  Attitudes towards prediction and early diagnosis of late-onset dementia: a comparison of tested persons and family caregivers , 2020, Aging & mental health.

[16]  M. Mildner,et al.  Re-epithelialization and immune cell behaviour in an ex vivo human skin model , 2020, Scientific Reports.

[17]  A Jon Stoessl,et al.  Neuroimaging in the early diagnosis of neurodegenerative disease , 2012, Translational Neurodegeneration.

[18]  Islem Rekik,et al.  Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis , 2018, Front. Neuroinform..

[19]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Anders Eklund,et al.  Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT , 2018, ArXiv.

[21]  Manhua Liu,et al.  A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease , 2019, NeuroImage.

[22]  Frank Hutter,et al.  Fixing Weight Decay Regularization in Adam , 2017, ArXiv.

[23]  Jan Eric Lenssen,et al.  Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.

[24]  Islem Rekik,et al.  Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states , 2018, Scientific Reports.

[25]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[26]  O. Sporns,et al.  A cross-disorder connectome landscape of brain dysconnectivity , 2019, Nature Reviews Neuroscience.

[27]  R. Jia,et al.  Determinants of duck Tembusu virus NS2A/2B polyprotein procession attenuated viral replication and proliferation in vitro , 2020, Scientific Reports.

[28]  Utkarsh,et al.  Stimuli Effect of the Human Brain Using EEG SPM Dataset , 2020 .

[29]  Nannan Li,et al.  MRI Cross-Modality Image-to-Image Translation , 2020, Scientific Reports.

[30]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.