Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI


  • Abhay M S Aradhya Nanyang Technological University
  • Aditya Joglekar Nanyang Technological University
  • Sundaram Suresh Nanyang Technological University
  • M. Pratama Nanyang Technology University



Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.




How to Cite

Aradhya, A. M. S., Joglekar, A., Suresh, S., & Pratama, M. (2019). Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2556-2563.



AAAI Technical Track: Humans and AI