Mr. Prabhat Reddy Kondamadugula Profile

Prabhat Reddy Kondamadugula

at Technische Univ München

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Proceedings Article | 4 April 2022 Paper

DG-GRU: dynamic graph based gated recurrent unit for age and gender prediction using brain imaging

Anees Kazi, Viktoria Markova, Prabhat Kondamadugula, Beiyan Liu, Ahmed Adly, Shahrooz Faghihroohi, Nassir Navab

Proceedings Volume 12033, 1203312 (2022) https://doi.org/10.1117/12.2607469

KEYWORDS: Brain, Functional magnetic resonance imaging, Data modeling, Convolution, Matrices, Brain imaging, Performance modeling, Neurological disorders, Mathematical modeling, Signal processing

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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.

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