Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts
Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to n...
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2021.667881/full |
| _version_ | 1818437173119025152 |
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| author | Ayumu Yamashita Yuki Sakai Takashi Yamada Takashi Yamada Noriaki Yahata Noriaki Yahata Noriaki Yahata Noriaki Yahata Akira Kunimatsu Akira Kunimatsu Naohiro Okada Naohiro Okada Takashi Itahashi Ryuichiro Hashimoto Ryuichiro Hashimoto Ryuichiro Hashimoto Hiroto Mizuta Naho Ichikawa Masahiro Takamura Go Okada Hirotaka Yamagata Kenichiro Harada Koji Matsuo Saori C. Tanaka Mitsuo Kawato Mitsuo Kawato Kiyoto Kasai Kiyoto Kasai Kiyoto Kasai Nobumasa Kato Nobumasa Kato Hidehiko Takahashi Hidehiko Takahashi Yasumasa Okamoto Okito Yamashita Okito Yamashita Hiroshi Imamizu Hiroshi Imamizu |
| author_facet | Ayumu Yamashita Yuki Sakai Takashi Yamada Takashi Yamada Noriaki Yahata Noriaki Yahata Noriaki Yahata Noriaki Yahata Akira Kunimatsu Akira Kunimatsu Naohiro Okada Naohiro Okada Takashi Itahashi Ryuichiro Hashimoto Ryuichiro Hashimoto Ryuichiro Hashimoto Hiroto Mizuta Naho Ichikawa Masahiro Takamura Go Okada Hirotaka Yamagata Kenichiro Harada Koji Matsuo Saori C. Tanaka Mitsuo Kawato Mitsuo Kawato Kiyoto Kasai Kiyoto Kasai Kiyoto Kasai Nobumasa Kato Nobumasa Kato Hidehiko Takahashi Hidehiko Takahashi Yasumasa Okamoto Okito Yamashita Okito Yamashita Hiroshi Imamizu Hiroshi Imamizu |
| author_sort | Ayumu Yamashita |
| collection | DOAJ |
| description | Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD. |
| first_indexed | 2024-12-14T17:20:27Z |
| format | Article |
| id | doaj.art-5e1285fb0a6c433b9b9b6941982e425f |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-12-14T17:20:27Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-5e1285fb0a6c433b9b9b6941982e425f2022-12-21T22:53:20ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402021-06-011210.3389/fpsyt.2021.667881667881Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent CohortsAyumu Yamashita0Yuki Sakai1Takashi Yamada2Takashi Yamada3Noriaki Yahata4Noriaki Yahata5Noriaki Yahata6Noriaki Yahata7Akira Kunimatsu8Akira Kunimatsu9Naohiro Okada10Naohiro Okada11Takashi Itahashi12Ryuichiro Hashimoto13Ryuichiro Hashimoto14Ryuichiro Hashimoto15Hiroto Mizuta16Naho Ichikawa17Masahiro Takamura18Go Okada19Hirotaka Yamagata20Kenichiro Harada21Koji Matsuo22Saori C. Tanaka23Mitsuo Kawato24Mitsuo Kawato25Kiyoto Kasai26Kiyoto Kasai27Kiyoto Kasai28Nobumasa Kato29Nobumasa Kato30Hidehiko Takahashi31Hidehiko Takahashi32Yasumasa Okamoto33Okito Yamashita34Okito Yamashita35Hiroshi Imamizu36Hiroshi Imamizu37Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, JapanBrain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, JapanBrain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, JapanMedical Institute of Developmental Disabilities Research, Showa University, Tokyo, JapanBrain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, JapanDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, JapanQuantum Life Informatics Group, Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Chiba, JapanDepartment of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, JapanDepartment of Radiology, The Institute of Medical Science The University of Tokyo (IMSUT) Hospital, Institute of Medical Science, The University of Tokyo, Tokyo, JapanDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, JapanDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, JapanThe International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, JapanMedical Institute of Developmental Disabilities Research, Showa University, Tokyo, JapanBrain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, JapanMedical Institute of Developmental Disabilities Research, Showa University, Tokyo, JapanDepartment of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan0Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan1Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan1Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan1Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan2Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan2Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan3Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, JapanBrain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, JapanBrain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan4Center for Advanced Intelligence Project, Institute of Physical and Chemical Research (RIKEN), Tokyo, JapanBrain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, JapanDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, JapanThe International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, JapanBrain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, JapanMedical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan0Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan5Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan1Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, JapanBrain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan4Center for Advanced Intelligence Project, Institute of Physical and Chemical Research (RIKEN), Tokyo, JapanBrain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan6Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, JapanLarge-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.https://www.frontiersin.org/articles/10.3389/fpsyt.2021.667881/fullresting-state functional magnetic resonance imagingmachine learningresting-state functional connectivitymajor depressive disorderdepression symptoms |
| spellingShingle | Ayumu Yamashita Yuki Sakai Takashi Yamada Takashi Yamada Noriaki Yahata Noriaki Yahata Noriaki Yahata Noriaki Yahata Akira Kunimatsu Akira Kunimatsu Naohiro Okada Naohiro Okada Takashi Itahashi Ryuichiro Hashimoto Ryuichiro Hashimoto Ryuichiro Hashimoto Hiroto Mizuta Naho Ichikawa Masahiro Takamura Go Okada Hirotaka Yamagata Kenichiro Harada Koji Matsuo Saori C. Tanaka Mitsuo Kawato Mitsuo Kawato Kiyoto Kasai Kiyoto Kasai Kiyoto Kasai Nobumasa Kato Nobumasa Kato Hidehiko Takahashi Hidehiko Takahashi Yasumasa Okamoto Okito Yamashita Okito Yamashita Hiroshi Imamizu Hiroshi Imamizu Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts Frontiers in Psychiatry resting-state functional magnetic resonance imaging machine learning resting-state functional connectivity major depressive disorder depression symptoms |
| title | Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts |
| title_full | Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts |
| title_fullStr | Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts |
| title_full_unstemmed | Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts |
| title_short | Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts |
| title_sort | common brain networks between major depressive disorder diagnosis and symptoms of depression that are validated for independent cohorts |
| topic | resting-state functional magnetic resonance imaging machine learning resting-state functional connectivity major depressive disorder depression symptoms |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2021.667881/full |
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