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DNN based Classification of ADHD fMRI Data using Functional Connectivity Coefficient
International Journal of Fuzzy Logic and Intelligent Systems 2020;20(4):255-260
Published online December 25, 2020
© 2020 Korean Institute of Intelligent Systems.

Nishant Chauhan and Byung-Jae Choi

Department of Electronic Engineering, Daegu University, Gyeongsan, Korea
Correspondence to: Byung-Jae Choi (
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 ( which permits unrestricted non-commercial 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

December 2020, 20 (4)
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Funding Information
  • CrossMark
  • Science Central