DG-GRU: Dynamic graph based gated recurrent unit for age and gender prediction using brain imaging
Kazi A, Markova V, Kondamadugula PR, Liu B, Adly A, Faghihroohi S, Navab N (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: SPIE
Book Volume: 12033
Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Event location: Virtual, Online
ISBN: 9781510649415
DOI: 10.1117/12.2607469
Abstract
Deep learning has revolutionized neuroimaging studies of psychiatric and neurological disorders. The difference between brain age and chronological age is a useful biomarker for identifying neurological disorders. Furthermore, delineating both age and gender is important for the study of illnesses exhibiting the phenotypic difference in these. In this paper, we focus on the prediction of age and gender from brain connectomes data which is a step further to full automation of disease prediction. We model the connectomes as brain graphs. Data is collected as functional MRI (fMRI) signals and the graphs are created by binarizing the correlation among the fMRI signals at the brain parcels considered as nodes. Such a graph represents the neurobiological functional connectivity. We further differentiate between static and dynamic connectivity. The former is constructed with the correlation of the overall signal at the nodes, while the latter is modeled as a sequence of brain graphs constructed over sequential time periods. Our hypothesis is that leveraging information from both the static and dynamic functional connectivity is beneficial to the task at hand. Our main contribution lies in our proposed novel input data representation and proposed recurrent graph-based deep learning model setting together. The proposed Dynamic Graph-based Gated Recurrent Unit (DG-GRU) comprises a mechanism to process both types of connectivities. In addition, it can be easily incorporated into any deep neural model. We show a thorough analysis of the model on two publicly available datasets HCP and ABIDE for two tasks to show the superiority of the model.
Involved external institutions
Germany (DE)How to cite
APA:
Kazi, A., Markova, V., Kondamadugula, P.R., Liu, B., Adly, A., Faghihroohi, S., & Navab, N. (2022). DG-GRU: Dynamic graph based gated recurrent unit for age and gender prediction using brain imaging. In Karen Drukker, Khan M. Iftekharuddin (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Virtual, Online: SPIE.
MLA:
Kazi, Anees, et al. "DG-GRU: Dynamic graph based gated recurrent unit for age and gender prediction using brain imaging." Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, Virtual, Online Ed. Karen Drukker, Khan M. Iftekharuddin, SPIE, 2022.
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