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International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(4): 255-260

Published online December 25, 2020

https://doi.org/10.5391/IJFIS.2020.20.4.255

© The Korean Institute of Intelligent Systems

DNN based Classification of ADHD fMRI Data using Functional Connectivity Coefficient

Nishant Chauhan and Byung-Jae Choi

Department of Electronic Engineering, Daegu University, Gyeongsan, Korea

Correspondence to :
Byung-Jae Choi (bjchoi@daegu.ac.kr)

Received: November 9, 2020; Revised: December 4, 2020; Accepted: December 4, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

DNN based Classification of ADHD fMRI Data using Functional Connectivity CoefficientFunctional magnetic resonance imaging (fMRI) has emerged as a popular research topic in neuroimaging for automated classification and recognition of different brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common behavioral disorders in young children because its underlying mechanism is still not completely understood. The use of fMRI data in ADHD research is utilized to reflect the neural mechanism and functional integration of the brain. Alteration in the functional connectivity of the brain is expected to provide useful information for classifying or predicting brain disorders. In this study, a deep neural network (DNN) approach was applied to classify ADHD using functional connectivity-based fMRI data. The functional connectivity coefficient was extracted between regions determined by independent component analysis (ICA) and used to feed the DNN for classification. The DNN model demonstrated an accuracy of 95% with the preprocessed fMRI data from Nilearn, which is a Python module for neuroimaging data.

Keywords: Functional Magnetic Resonance Imaging (fMRI), Deep Neural Network (DNN), Attention Deficit Hyperactivity Disorder (ADHD), Independent Component Analysis (ICA), Nilearn

This research was partly supported by the Daegu University and Korea Institute of Advancement of Technology (KAIT) grant funded by the Korean government (MOTIE) (P0012724, The Competency Development Program for Industry Specialist).

No potential conflict of interest relevant to this article was reported.

Nishant Chauhan received his B.S. degree in Computer Science from AKTU, India in 2012, and an M.S. degree in Control and Instrumentation from Daegu University, South Korea in 2020. He is currently a full-time Ph.D. student and working towards a Ph.D. degree in Control and Instrumentation at Daegu University, Department of Electronic Engineering (Graduate School). His research interests include intelligent control, fuzzy logic, Image processing, machine learning, and deep learning for medical images, Brain MRI, fMRI.

E-mail: nishantsep1090@daegu.ac.kr.


Byung-Jae Choi received his B.S. in Electronic Engineering, in 1987 from Kyungpook National University, Daegu. And he received his M.S. and a Ph.D. degree in Electrical and Electronic Engineering, 1989 and 1998, KAIST in Daejeon. He is a Professor of the School of Electronic and Electrical Engineering, Daegu University, Daegu, Korea, since 1999. His current research interests include intelligent control and its applications.

E-mail: bjchoi@daegu.ac.kr.


Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(4): 255-260

Published online December 25, 2020 https://doi.org/10.5391/IJFIS.2020.20.4.255

Copyright © The Korean Institute of Intelligent Systems.

DNN based Classification of ADHD fMRI Data using Functional Connectivity Coefficient

Nishant Chauhan and Byung-Jae Choi

Department of Electronic Engineering, Daegu University, Gyeongsan, Korea

Correspondence to:Byung-Jae Choi (bjchoi@daegu.ac.kr)

Received: November 9, 2020; Revised: December 4, 2020; Accepted: December 4, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

DNN based Classification of ADHD fMRI Data using Functional Connectivity CoefficientFunctional magnetic resonance imaging (fMRI) has emerged as a popular research topic in neuroimaging for automated classification and recognition of different brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common behavioral disorders in young children because its underlying mechanism is still not completely understood. The use of fMRI data in ADHD research is utilized to reflect the neural mechanism and functional integration of the brain. Alteration in the functional connectivity of the brain is expected to provide useful information for classifying or predicting brain disorders. In this study, a deep neural network (DNN) approach was applied to classify ADHD using functional connectivity-based fMRI data. The functional connectivity coefficient was extracted between regions determined by independent component analysis (ICA) and used to feed the DNN for classification. The DNN model demonstrated an accuracy of 95% with the preprocessed fMRI data from Nilearn, which is a Python module for neuroimaging data.

Keywords: Functional Magnetic Resonance Imaging (fMRI), Deep Neural Network (DNN), Attention Deficit Hyperactivity Disorder (ADHD), Independent Component Analysis (ICA), Nilearn

Fig 1.

Figure 1.

ADHD classification framework.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 255-260https://doi.org/10.5391/IJFIS.2020.20.4.255

Fig 2.

Figure 2.

Decomposition using ICA.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 255-260https://doi.org/10.5391/IJFIS.2020.20.4.255

Fig 3.

Figure 3.

Functional connectivity coefficients matrices based on correlation.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 255-260https://doi.org/10.5391/IJFIS.2020.20.4.255

Fig 4.

Figure 4.

Functional connectivity coefficients visualization.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 255-260https://doi.org/10.5391/IJFIS.2020.20.4.255

Fig 5.

Figure 5.

DNN model architecture.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 255-260https://doi.org/10.5391/IJFIS.2020.20.4.255

Fig 6.

Figure 6.

(a) DNN model accuracy and (b) DNN model loss.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 255-260https://doi.org/10.5391/IJFIS.2020.20.4.255

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