Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach
BackgroundStructural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotype...
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Human Neuroscience |
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| author | Kelly Rootes-Murdy Kelly Rootes-Murdy Jesse T. Edmond Jesse T. Edmond Wenhao Jiang Md A. Rahaman Jiayu Chen Nora I. Perrone-Bizzozero Vince D. Calhoun Vince D. Calhoun Theo G. M. van Erp Theo G. M. van Erp Stefan Ehrlich Ingrid Agartz Ingrid Agartz Ingrid Agartz Ingrid Agartz Erik G. Jönsson Erik G. Jönsson Ole A. Andreassen Ole A. Andreassen Lars T. Westlye Lars T. Westlye Lars T. Westlye Lei Wang Godfrey D. Pearlson Godfrey D. Pearlson David C. Glahn David C. Glahn Elliot Hong Robert W. Buchanan Peter Kochunov Aristotle Voineskos Anil Malhotra Carol A. Tamminga Jingyu Liu Jessica A. Turner |
| author_facet | Kelly Rootes-Murdy Kelly Rootes-Murdy Jesse T. Edmond Jesse T. Edmond Wenhao Jiang Md A. Rahaman Jiayu Chen Nora I. Perrone-Bizzozero Vince D. Calhoun Vince D. Calhoun Theo G. M. van Erp Theo G. M. van Erp Stefan Ehrlich Ingrid Agartz Ingrid Agartz Ingrid Agartz Ingrid Agartz Erik G. Jönsson Erik G. Jönsson Ole A. Andreassen Ole A. Andreassen Lars T. Westlye Lars T. Westlye Lars T. Westlye Lei Wang Godfrey D. Pearlson Godfrey D. Pearlson David C. Glahn David C. Glahn Elliot Hong Robert W. Buchanan Peter Kochunov Aristotle Voineskos Anil Malhotra Carol A. Tamminga Jingyu Liu Jessica A. Turner |
| author_sort | Kelly Rootes-Murdy |
| collection | DOAJ |
| description | BackgroundStructural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles.Materials and methodsWe analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP.ResultsComponent A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences.ConclusionThese multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP. |
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| language | English |
| last_indexed | 2024-04-11T08:11:21Z |
| publishDate | 2022-11-01 |
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| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-3f8d42cb59bb4dcbae7687bf979ebe582022-12-22T04:35:21ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612022-11-011610.3389/fnhum.2022.10016921001692Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approachKelly Rootes-Murdy0Kelly Rootes-Murdy1Jesse T. Edmond2Jesse T. Edmond3Wenhao Jiang4Md A. Rahaman5Jiayu Chen6Nora I. Perrone-Bizzozero7Vince D. Calhoun8Vince D. Calhoun9Theo G. M. van Erp10Theo G. M. van Erp11Stefan Ehrlich12Ingrid Agartz13Ingrid Agartz14Ingrid Agartz15Ingrid Agartz16Erik G. Jönsson17Erik G. Jönsson18Ole A. Andreassen19Ole A. Andreassen20Lars T. Westlye21Lars T. Westlye22Lars T. Westlye23Lei Wang24Godfrey D. Pearlson25Godfrey D. Pearlson26David C. Glahn27David C. Glahn28Elliot Hong29Robert W. Buchanan30Peter Kochunov31Aristotle Voineskos32Anil Malhotra33Carol A. Tamminga34Jingyu Liu35Jessica A. Turner36Department of Psychology, Georgia State University, Atlanta, GA, United StatesTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United StatesDepartment of Psychology, Georgia State University, Atlanta, GA, United StatesTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United StatesDepartment of Psychosomatics and Psychiatry, Medical School, Zhongda Hospital, Institute of Psychosomatics, Southeast University, Nanjing, ChinaTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United StatesTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United StatesDepartment of Neurosciences, University of New Mexico, Albuquerque, NM, United StatesDepartment of Psychology, Georgia State University, Atlanta, GA, United StatesTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United StatesClinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United StatesCenter for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United StatesDivision of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, GermanyDivision of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, NorwayDepartment of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden0K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway1Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, NorwayDivision of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, NorwayDepartment of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, SwedenDivision of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway0K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, NorwayDivision of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway0K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway2Department of Psychology, University of Oslo, Oslo, Norway3Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States4Department of Psychiatry, Yale University, New Haven, CT, United States5Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States5Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States6Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States7Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States7Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States7Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States8Department of Psychiatry, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada9Division of Psychiatry Research, Zucker Hillside Hospital, Queens, NY, United States0Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, United StatesTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States3Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United StatesBackgroundStructural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles.Materials and methodsWe analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP.ResultsComponent A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences.ConclusionThese multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.https://www.frontiersin.org/articles/10.3389/fnhum.2022.1001692/fullbipolar disorderschizophreniamultivariate analysisICAPANSS |
| spellingShingle | Kelly Rootes-Murdy Kelly Rootes-Murdy Jesse T. Edmond Jesse T. Edmond Wenhao Jiang Md A. Rahaman Jiayu Chen Nora I. Perrone-Bizzozero Vince D. Calhoun Vince D. Calhoun Theo G. M. van Erp Theo G. M. van Erp Stefan Ehrlich Ingrid Agartz Ingrid Agartz Ingrid Agartz Ingrid Agartz Erik G. Jönsson Erik G. Jönsson Ole A. Andreassen Ole A. Andreassen Lars T. Westlye Lars T. Westlye Lars T. Westlye Lei Wang Godfrey D. Pearlson Godfrey D. Pearlson David C. Glahn David C. Glahn Elliot Hong Robert W. Buchanan Peter Kochunov Aristotle Voineskos Anil Malhotra Carol A. Tamminga Jingyu Liu Jessica A. Turner Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach Frontiers in Human Neuroscience bipolar disorder schizophrenia multivariate analysis ICA PANSS |
| title | Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach |
| title_full | Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach |
| title_fullStr | Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach |
| title_full_unstemmed | Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach |
| title_short | Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach |
| title_sort | clinical and cortical similarities identified between bipolar disorder i and schizophrenia a multivariate approach |
| topic | bipolar disorder schizophrenia multivariate analysis ICA PANSS |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2022.1001692/full |
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