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

Authors

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

DOI:

https://doi.org/10.1609/aaai.v33i01.33012556

Abstract

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.

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Published

2019-07-17

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. https://doi.org/10.1609/aaai.v33i01.33012556

Issue

Section

AAAI Technical Track: Humans and AI