Aberrant individual large-scale functional network connectivity and topology in chronic insomnia disorder with and without depression
Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and n...
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Published in | Progress in neuro-psychopharmacology & biological psychiatry Vol. 136; p. 111158 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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England
Elsevier Inc
10.01.2025
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Abstract | Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches.
•The neural basis for chronic insomnia disorder with and without depression was explored.•Individual large-scale functional network mapping was conducted for each subject.•Both static and dynamic large-scale functional network connectivity and network topology were analyzed.•The shared- and specific-neural markers for chronic insomnia disorder with and without depression were established. |
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AbstractList | Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches. Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches. •The neural basis for chronic insomnia disorder with and without depression was explored.•Individual large-scale functional network mapping was conducted for each subject.•Both static and dynamic large-scale functional network connectivity and network topology were analyzed.•The shared- and specific-neural markers for chronic insomnia disorder with and without depression were established. Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches.Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches. |
ArticleNumber | 111158 |
Author | Chen, Kexuan Shao, Heng Wang, Libo Zhu, Baosheng Ma, Jianing Chen, Jin Wang, Jiaojian Zhong, Jingmei Gong, Liang Li, Junying Chen, Meiling Bi, Bin |
Author_xml | – sequence: 1 givenname: Meiling surname: Chen fullname: Chen, Meiling organization: Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, China – sequence: 2 givenname: Heng surname: Shao fullname: Shao, Heng organization: Department of Geriatrics, the First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China – sequence: 3 givenname: Libo surname: Wang fullname: Wang, Libo organization: The Second People's Hospital of Yuxi, the Affiliated Hospital of Kunming University of Science and Technology, Yuxi, China – sequence: 4 givenname: Jianing surname: Ma fullname: Ma, Jianing organization: State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China – sequence: 5 givenname: Jin surname: Chen fullname: Chen, Jin organization: State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China – sequence: 6 givenname: Junying surname: Li fullname: Li, Junying organization: Department of Neurology, Chengdu Second People's Hospital, Chengdu, China – sequence: 7 givenname: Jingmei surname: Zhong fullname: Zhong, Jingmei organization: Department of Clinical Psychology, the First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China – sequence: 8 givenname: Baosheng surname: Zhu fullname: Zhu, Baosheng organization: Department of Medical Genetics, the First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China – sequence: 9 givenname: Bin surname: Bi fullname: Bi, Bin email: yoyoga9853@qq.com organization: Department of Clinical Psychology, the Second People's Hospital of Guizhou Province, Guiyang, China – sequence: 10 givenname: Kexuan surname: Chen fullname: Chen, Kexuan email: kexuanchen@kust.edu.cn organization: Medical School, Kunming University of Science and Technology, Kunming, China – sequence: 11 givenname: Jiaojian surname: Wang fullname: Wang, Jiaojian email: jiaojianwang@uestc.edu.cn organization: State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China – sequence: 12 givenname: Liang surname: Gong fullname: Gong, Liang email: seugongliang@hotmail.com organization: Department of Neurology, Chengdu Second People's Hospital, Chengdu, China |
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Keywords | Individual functional networks Dynamic functional connectivity Chronic insomnia disorder Functional network connectivity Classification |
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SubjectTerms | Adult Brain - diagnostic imaging Brain - physiopathology Chronic insomnia disorder Classification Depression - complications Depression - diagnostic imaging Depression - physiopathology Dynamic functional connectivity Female Functional network connectivity Humans Individual functional networks Magnetic Resonance Imaging Male Middle Aged Nerve Net - diagnostic imaging Nerve Net - physiopathology Neural Pathways - diagnostic imaging Neural Pathways - physiopathology Sleep Initiation and Maintenance Disorders - complications Sleep Initiation and Maintenance Disorders - diagnostic imaging Sleep Initiation and Maintenance Disorders - physiopathology Sleep Initiation and Maintenance Disorders - psychology |
Title | Aberrant individual large-scale functional network connectivity and topology in chronic insomnia disorder with and without depression |
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