Effective Connectivity in Default Mode Network for Alcoholism Diagnosis

Alcohol Use Disorder (AUD) is a chronic relapsing brain disease characterized by excessive alcohol use, loss of control over alcohol intake, and negative emotional states under no alcohol consumption. The key factor in successful treatment of AUD is the accurate diagnosis for better medical and ther...

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Main Authors: Khan, D.M., Yahya, N., Kamel, N., Faye, I.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23762 /
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105116468&doi=10.1109%2fTNSRE.2021.3075737&partnerID=40&md5=5a052c2004f370e83ec53c64a14b9592
http://eprints.utp.edu.my/23762/
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spelling utp-eprints.237622021-08-19T10:01:43Z Effective Connectivity in Default Mode Network for Alcoholism Diagnosis Khan, D.M. Yahya, N. Kamel, N. Faye, I. Alcohol Use Disorder (AUD) is a chronic relapsing brain disease characterized by excessive alcohol use, loss of control over alcohol intake, and negative emotional states under no alcohol consumption. The key factor in successful treatment of AUD is the accurate diagnosis for better medical and therapy management. Conventionally, for individuals to be diagnosed with AUD, certain criteria as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM) should be met. However, this process is subjective in nature and could be misleading due to memory problems and dishonesty of some AUD patients. In this paper, an assessment scheme for objective diagnosis of AUD is proposed. For this purpose, EEG recording of 31 healthy controls and 31 AUD patients are used for the calculation of effective connectivity (EC) between the various regions of the brain Default Mode Network (DMN). The EC is estimated using partial directed coherence (PDC) which are then used as input to a 3D Convolutional Neural Network (CNN) for binary classification of AUD cases. Using 5-fold cross validation, the classification of AUD vs. HC effective connectivity matrices using the proposed 3D-CNN gives an accuracy of 87.85 ± 4.64 . For further validation, 32 and 30 subjects are randomly selected for training and testing, respectively, giving 100 correct classification of all the testing subjects. © 2001-2011 IEEE. Institute of Electrical and Electronics Engineers Inc. 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105116468&doi=10.1109%2fTNSRE.2021.3075737&partnerID=40&md5=5a052c2004f370e83ec53c64a14b9592 Khan, D.M. and Yahya, N. and Kamel, N. and Faye, I. (2021) Effective Connectivity in Default Mode Network for Alcoholism Diagnosis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29 . pp. 796-808. http://eprints.utp.edu.my/23762/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Alcohol Use Disorder (AUD) is a chronic relapsing brain disease characterized by excessive alcohol use, loss of control over alcohol intake, and negative emotional states under no alcohol consumption. The key factor in successful treatment of AUD is the accurate diagnosis for better medical and therapy management. Conventionally, for individuals to be diagnosed with AUD, certain criteria as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM) should be met. However, this process is subjective in nature and could be misleading due to memory problems and dishonesty of some AUD patients. In this paper, an assessment scheme for objective diagnosis of AUD is proposed. For this purpose, EEG recording of 31 healthy controls and 31 AUD patients are used for the calculation of effective connectivity (EC) between the various regions of the brain Default Mode Network (DMN). The EC is estimated using partial directed coherence (PDC) which are then used as input to a 3D Convolutional Neural Network (CNN) for binary classification of AUD cases. Using 5-fold cross validation, the classification of AUD vs. HC effective connectivity matrices using the proposed 3D-CNN gives an accuracy of 87.85 ± 4.64 . For further validation, 32 and 30 subjects are randomly selected for training and testing, respectively, giving 100 correct classification of all the testing subjects. © 2001-2011 IEEE.
format Article
author Khan, D.M.
Yahya, N.
Kamel, N.
Faye, I.
spellingShingle Khan, D.M.
Yahya, N.
Kamel, N.
Faye, I.
Effective Connectivity in Default Mode Network for Alcoholism Diagnosis
author_sort Khan, D.M.
title Effective Connectivity in Default Mode Network for Alcoholism Diagnosis
title_short Effective Connectivity in Default Mode Network for Alcoholism Diagnosis
title_full Effective Connectivity in Default Mode Network for Alcoholism Diagnosis
title_fullStr Effective Connectivity in Default Mode Network for Alcoholism Diagnosis
title_full_unstemmed Effective Connectivity in Default Mode Network for Alcoholism Diagnosis
title_sort effective connectivity in default mode network for alcoholism diagnosis
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105116468&doi=10.1109%2fTNSRE.2021.3075737&partnerID=40&md5=5a052c2004f370e83ec53c64a14b9592
http://eprints.utp.edu.my/23762/
_version_ 1741196728175951872
score 11.62408

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