Predicting Biological Age and Sex using Spatial Temporal Graph CNN for functional MRI data
Functional Magnetic Resonance Imaging (fMRI) is a technique to measure brain activity by quantifying blood oxygen level (BOLD signal) (1) There have been different studies showing that human functional organization undergoes changes related to age and exhibits sexual dimorphism (2; 3; 4) We aim at leveraging deep learning techniques to develop a model that can be used to accurately predict the biological age and sex based on fMRI data. We used the state of art Spatial Temporal Graph Convolutional Networks (ST GCN) which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. As the first attempt to apply spatial temporal convolution on fMRI data, we overcame the challenge of limited sample size and are able to get to get an accuracy of 78% for sex and 63% for age (binary classification for young v/s old) These results manifest ST GCN as the novel model to predict biological sex and age from fMRI data.