Comparing Functional and Effective Connectivity Features in Diagnosis of Autism Spectrum Disorder Using Stacked Autoencoder by Resting-State fMRI Data

Pegah  Navaei; Ali  Khadem; Effat Jalaeian  Zaferani

doi:10.18502/fbt.v12i4.19819

Pegah Navaei Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Ali Khadem Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Effat Jalaeian Zaferani Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran

DOI: https://doi.org/10.18502/fbt.v12i4.19819

Keywords: Autism Spectrum Disorder; Resting-State functional Magnetic Resonance Imaging; Functional and Effective Brain Connectivity; Pearson Correlation Coefficient; Granger Causality; Stacked Autoencoder.

Abstract

Purpose: The objective of this paper is to study the feasibility of using effective connectivity (Granger Causality) (GC) obtained from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data and stacked autoencoder for diagnosing Autism Spectrum Disorder (ASD) and comparing the results with those obtained using functional connectivity (Pearson Correlation Coefficient) (PCC). ASD affects the normal development of the brain in the field of social interactions and communication skills. Because diagnosing ASD using behavioral symptoms is a time-consuming subjective process that needs the exact collaboration of the ASD subject or his/her relatives, in recent years diagnosing ASD using resting-state functional neuroimaging modalities like rs-fMRI, has been taken into consideration.

Materials and Methods: We used rs-fMRI data and compared the use of functional and effective connectivity features using an autoencoder to classify people with ASD from healthy subjects. We used ABIDE dataset and divided the brain into 100 regions using the Harvard-Oxford (HO) Atlas. We calculated the PCC in classification using functional connectivity, and we calculated the GC in classification using effective connectivity. We used a stacked autoencoder to reduce the dimension of feature-space and a multi-layered perceptron (MLP) neural network as a classifier in both classifications.

Results: We achieved an accuracy of 67.8%, a sensitivity of 68.5%, and a specificity of 66.6% in classification using functional connectivity, and we achieved an accuracy of 67.6%, a sensitivity of 73.1%, and a specificity of 60.8% in classification using effective connectivity.

Conclusion: Although the accuracy obtained using functional and effective connectivity are almost similar, the sensitivity is notably higher using effective connectivity. Since sensitivity is more important than specificity in the medical diagnosis, it seems that using effective connectivity features may outperform the ASD diagnosis in practice. The purpose of this paper is to diagnose ASD using effective connectivity measures and deep neural network by rs-fMRI data, but we compare its results with functional connectivity measures. As far as we know, this is the first time that Granger Causality (GC) and stacked autoencoder have been used to diagnose ASD together.