TY - GEN
T1 - Multi-Task Learning for Efficient Diagnosis of ASD and ADHD using Resting-State fMRI Data
AU - Huang, Zhi-An
AU - Liu, Rui
AU - Tan, Kay Chen
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China (NSFC) under grant No. 61876162, by the Shenzhen Scientific Research and Development Funding Program under grant JCYJ20180307123637294, and by the Research Grants Council of the Hong Kong SAR under grant No. CityU11202418 and CityU11209219.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7
Y1 - 2020/7
N2 - Increasing mental disorders have emerged as an urgent public health concern such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Related mental disorders may share high overlap in clinical symptoms. Therefore, their diagnosis can be challenging to merely rely on the observation of cognitive phenotypes and behavioral manifestations. Unfortunately, there is no additional support of biochemical markers, laboratory tests, or neuroimaging analysis, which can be used as a diagnostic gold standard currently. Over the past decades, resting-state functional magnetic resonance imaging (rs-fMRI) has been considered as one of the most promising modality to capture the intrinsic neural activation patterns between regions in the brain. In this work, we focus on ASD and ADHD due to their high prevalence and relevance with the aim to exploit the multi-task learning (MTL) paradigm for their diagnosis. To the best of our knowledge, this is the first time to make use of the disease-specific heterogeneities for the MTL classification of ASD and ADHD via rs-fMRI signal. We propose a novel graph-based feature selection method to filter out irrelevant functional connectivity features. Then an efficient structure of multi-gate mixture-of-experts (MMoE) is applied to the MTL classification framework. Finally, the experiment results demonstrate that the proposed model can achieve a reliable classification performance in a short term, yielding the mean accuracies of 0.687±0.005 and 0.650±0.014 in ASD and ADHD datasets, respectively. The graph-based feature selection method and MMoE model are demonstrated to make great contribution to performance improvement.
AB - Increasing mental disorders have emerged as an urgent public health concern such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Related mental disorders may share high overlap in clinical symptoms. Therefore, their diagnosis can be challenging to merely rely on the observation of cognitive phenotypes and behavioral manifestations. Unfortunately, there is no additional support of biochemical markers, laboratory tests, or neuroimaging analysis, which can be used as a diagnostic gold standard currently. Over the past decades, resting-state functional magnetic resonance imaging (rs-fMRI) has been considered as one of the most promising modality to capture the intrinsic neural activation patterns between regions in the brain. In this work, we focus on ASD and ADHD due to their high prevalence and relevance with the aim to exploit the multi-task learning (MTL) paradigm for their diagnosis. To the best of our knowledge, this is the first time to make use of the disease-specific heterogeneities for the MTL classification of ASD and ADHD via rs-fMRI signal. We propose a novel graph-based feature selection method to filter out irrelevant functional connectivity features. Then an efficient structure of multi-gate mixture-of-experts (MMoE) is applied to the MTL classification framework. Finally, the experiment results demonstrate that the proposed model can achieve a reliable classification performance in a short term, yielding the mean accuracies of 0.687±0.005 and 0.650±0.014 in ASD and ADHD datasets, respectively. The graph-based feature selection method and MMoE model are demonstrated to make great contribution to performance improvement.
KW - attention deficit hyperactivity disorder (ADHD)
KW - Autism spectrum disorder (ASD)
KW - functional connectivity (FC)
KW - functional magnetic resonance imaging (fMRI)
KW - multi-task learning (MTL)
UR - http://www.scopus.com/inward/record.url?scp=85093860222&partnerID=8YFLogxK
U2 - 10.1109/IJCNN48605.2020.9206852
DO - 10.1109/IJCNN48605.2020.9206852
M3 - Conference article published in proceeding or book
AN - SCOPUS:85093860222
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 1
EP - 7
BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020
Y2 - 19 July 2020 through 24 July 2020
ER -