Identifying resting‐state effective connectivity abnormalities in drug‐naïve major depressive disorder diagnosis via graph convolutional networks
Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use...
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Published in | Human brain mapping Vol. 41; no. 17; pp. 4997 - 5014 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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John Wiley & Sons, Inc
01.12.2020
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Abstract | Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting‐state functional magnetic resonance imaging (rs‐fMRI) and estimate functional connectivity for brain‐disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph‐based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole‐brain data‐driven manner from rs‐fMRI. To distinguish drug‐naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD.
This study validated the use of effective connectivity (EC) for major depressive disorder (MDD) identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole‐brain data‐driven manner from resting‐state functional magnetic resonance imaging. To distinguish drug‐naive MDD patients from healthy controls, we utilize spectral graph convolutional networks (GCNs) based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. |
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AbstractList | Abstract
Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting‐state functional magnetic resonance imaging (rs‐fMRI) and estimate functional connectivity for brain‐disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph‐based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole‐brain data‐driven manner from rs‐fMRI. To distinguish drug‐naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD. Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting-state functional magnetic resonance imaging (rs-fMRI) and estimate functional connectivity for brain-disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph-based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole-brain data-driven manner from rs-fMRI. To distinguish drug-naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD. Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting‐state functional magnetic resonance imaging (rs‐fMRI) and estimate functional connectivity for brain‐disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph‐based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole‐brain data‐driven manner from rs‐fMRI. To distinguish drug‐naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD. This study validated the use of effective connectivity (EC) for major depressive disorder (MDD) identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole‐brain data‐driven manner from resting‐state functional magnetic resonance imaging. To distinguish drug‐naive MDD patients from healthy controls, we utilize spectral graph convolutional networks (GCNs) based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. |
Author | Suk, Heung‐Il Lee, Jiyeon Kang, Wooyoung Jun, Eunji Ham, Byung‐Joo Na, Kyoung‐Sae |
AuthorAffiliation | 5 Department of Psychiatry Korea University Anam Hospital, Korea University College of Medicine Seoul Republic of Korea 1 Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea 3 Department of Biomedical Sciences Korea University College of Medicine Seoul Republic of Korea 2 Department of Psychiatry Gachon University Gil Medical Center Incheon Republic of Korea 4 Department of Artificial Intelligence Korea University Seoul Republic of Korea |
AuthorAffiliation_xml | – name: 3 Department of Biomedical Sciences Korea University College of Medicine Seoul Republic of Korea – name: 5 Department of Psychiatry Korea University Anam Hospital, Korea University College of Medicine Seoul Republic of Korea – name: 1 Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea – name: 2 Department of Psychiatry Gachon University Gil Medical Center Incheon Republic of Korea – name: 4 Department of Artificial Intelligence Korea University Seoul Republic of Korea |
Author_xml | – sequence: 1 givenname: Eunji orcidid: 0000-0002-3121-7734 surname: Jun fullname: Jun, Eunji organization: Korea University – sequence: 2 givenname: Kyoung‐Sae orcidid: 0000-0002-0148-9827 surname: Na fullname: Na, Kyoung‐Sae organization: Gachon University Gil Medical Center – sequence: 3 givenname: Wooyoung orcidid: 0000-0003-4733-027X surname: Kang fullname: Kang, Wooyoung organization: Korea University College of Medicine – sequence: 4 givenname: Jiyeon orcidid: 0000-0002-8400-2729 surname: Lee fullname: Lee, Jiyeon organization: Korea University – sequence: 5 givenname: Heung‐Il orcidid: 0000-0001-7019-8962 surname: Suk fullname: Suk, Heung‐Il email: heungilsuk@gmail.com organization: Korea University – sequence: 6 givenname: Byung‐Joo orcidid: 0000-0002-0108-2058 surname: Ham fullname: Ham, Byung‐Joo email: byungjoo.ham@gmail.com organization: Korea University Anam Hospital, Korea University College of Medicine |
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Keywords | deep learning Sparse Group LASSO resting-state functional magnetic resonance imaging (rs-fMRI) graph convolutional networks (GCNs) major depressive disorder (MDD) effective connectivity |
Language | English |
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Snippet | Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning.... Abstract Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic... |
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SubjectTerms | Abnormalities Artificial neural networks Brain Brain mapping deep learning Diagnosis effective connectivity Functional magnetic resonance imaging graph convolutional networks (GCNs) Magnetic resonance imaging major depressive disorder (MDD) Medical imaging Mental depression Neural networks Neuroimaging Phenotypes resting‐state functional magnetic resonance imaging (rs‐fMRI) Sensitivity analysis Signs and symptoms Sparse Group LASSO |
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Title | Identifying resting‐state effective connectivity abnormalities in drug‐naïve major depressive disorder diagnosis via graph convolutional networks |
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