Common and distinct structural features of schizophrenia and bipolar disorder: The European Network on Psychosis, Affective disorders and Cognitive Trajectory (ENPACT) study
Although schizophrenia (SCZ) and bipolar disorder (BD) share elements of pathology, their neural underpinnings are still under investigation. Here, structural Magnetic Resonance Imaging (MRI) data collected from a large sample of BD and SCZ patients and healthy controls (HC) were analyzed in terms o...
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| Published in | PloS one Vol. 12; no. 11; p. e0188000 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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United States
Public Library of Science
14.11.2017
Public Library of Science (PLoS) |
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| Abstract | Although schizophrenia (SCZ) and bipolar disorder (BD) share elements of pathology, their neural underpinnings are still under investigation. Here, structural Magnetic Resonance Imaging (MRI) data collected from a large sample of BD and SCZ patients and healthy controls (HC) were analyzed in terms of gray matter volume (GMV) using both voxel based morphometry (VBM) and a region of interest (ROI) approach.
The analysis was conducted on two datasets, Dataset1 (802 subjects: 243 SCZ, 176 BD, 383 HC) and Dataset2, a homogeneous subset of Dataset1 (301 subjects: 107 HC, 85 BD and 109 SCZ). General Linear Model analyses were performed 1) at the voxel-level in the whole brain (VBM study), 2) at the regional level in the anatomical regions emerged from the VBM study (ROI study). The GMV comparison across groups was integrated with the analysis of GMV correlates of different clinical dimensions.
The VBM results of Dataset1 showed 1) in BD compared to HC, GMV deficits in right cingulate, superior temporal and calcarine cortices, 2) in SCZ compared to HC, GMV deficits in widespread cortical and subcortical areas, 3) in SCZ compared to BD, GMV deficits in insula and thalamus (p<0.05, cluster family wise error corrected). The regions showing GMV deficits in the BD group were mostly included in the SCZ ones. The ROI analyses confirmed the VBM results at the regional level in most of the clusters from the SCZ vs. HC comparison (p<0.05, Bonferroni corrected). The VBM and ROI analyses of Dataset2 provided further evidence for the enhanced GMV deficits characterizing SCZ. Based on the clinical-neuroanatomical analyses, we cannot exclude possible confounding effects due to 1) age of onset and medication in BD patients, 2) symptoms severity in SCZ patients.
Our study reported both shared and specific neuroanatomical characteristics between the two disorders, suggesting more severe and generalized GMV deficits in SCZ, with a specific role for insula and thalamus. |
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| AbstractList | Although schizophrenia (SCZ) and bipolar disorder (BD) share elements of pathology, their neural underpinnings are still under investigation. Here, structural Magnetic Resonance Imaging (MRI) data collected from a large sample of BD and SCZ patients and healthy controls (HC) were analyzed in terms of gray matter volume (GMV) using both voxel based morphometry (VBM) and a region of interest (ROI) approach.
The analysis was conducted on two datasets, Dataset1 (802 subjects: 243 SCZ, 176 BD, 383 HC) and Dataset2, a homogeneous subset of Dataset1 (301 subjects: 107 HC, 85 BD and 109 SCZ). General Linear Model analyses were performed 1) at the voxel-level in the whole brain (VBM study), 2) at the regional level in the anatomical regions emerged from the VBM study (ROI study). The GMV comparison across groups was integrated with the analysis of GMV correlates of different clinical dimensions.
The VBM results of Dataset1 showed 1) in BD compared to HC, GMV deficits in right cingulate, superior temporal and calcarine cortices, 2) in SCZ compared to HC, GMV deficits in widespread cortical and subcortical areas, 3) in SCZ compared to BD, GMV deficits in insula and thalamus (p<0.05, cluster family wise error corrected). The regions showing GMV deficits in the BD group were mostly included in the SCZ ones. The ROI analyses confirmed the VBM results at the regional level in most of the clusters from the SCZ vs. HC comparison (p<0.05, Bonferroni corrected). The VBM and ROI analyses of Dataset2 provided further evidence for the enhanced GMV deficits characterizing SCZ. Based on the clinical-neuroanatomical analyses, we cannot exclude possible confounding effects due to 1) age of onset and medication in BD patients, 2) symptoms severity in SCZ patients.
Our study reported both shared and specific neuroanatomical characteristics between the two disorders, suggesting more severe and generalized GMV deficits in SCZ, with a specific role for insula and thalamus. Although schizophrenia (SCZ) and bipolar disorder (BD) share elements of pathology, their neural underpinnings are still under investigation. Here, structural Magnetic Resonance Imaging (MRI) data collected from a large sample of BD and SCZ patients and healthy controls (HC) were analyzed in terms of gray matter volume (GMV) using both voxel based morphometry (VBM) and a region of interest (ROI) approach.INTRODUCTIONAlthough schizophrenia (SCZ) and bipolar disorder (BD) share elements of pathology, their neural underpinnings are still under investigation. Here, structural Magnetic Resonance Imaging (MRI) data collected from a large sample of BD and SCZ patients and healthy controls (HC) were analyzed in terms of gray matter volume (GMV) using both voxel based morphometry (VBM) and a region of interest (ROI) approach.The analysis was conducted on two datasets, Dataset1 (802 subjects: 243 SCZ, 176 BD, 383 HC) and Dataset2, a homogeneous subset of Dataset1 (301 subjects: 107 HC, 85 BD and 109 SCZ). General Linear Model analyses were performed 1) at the voxel-level in the whole brain (VBM study), 2) at the regional level in the anatomical regions emerged from the VBM study (ROI study). The GMV comparison across groups was integrated with the analysis of GMV correlates of different clinical dimensions.METHODSThe analysis was conducted on two datasets, Dataset1 (802 subjects: 243 SCZ, 176 BD, 383 HC) and Dataset2, a homogeneous subset of Dataset1 (301 subjects: 107 HC, 85 BD and 109 SCZ). General Linear Model analyses were performed 1) at the voxel-level in the whole brain (VBM study), 2) at the regional level in the anatomical regions emerged from the VBM study (ROI study). The GMV comparison across groups was integrated with the analysis of GMV correlates of different clinical dimensions.The VBM results of Dataset1 showed 1) in BD compared to HC, GMV deficits in right cingulate, superior temporal and calcarine cortices, 2) in SCZ compared to HC, GMV deficits in widespread cortical and subcortical areas, 3) in SCZ compared to BD, GMV deficits in insula and thalamus (p<0.05, cluster family wise error corrected). The regions showing GMV deficits in the BD group were mostly included in the SCZ ones. The ROI analyses confirmed the VBM results at the regional level in most of the clusters from the SCZ vs. HC comparison (p<0.05, Bonferroni corrected). The VBM and ROI analyses of Dataset2 provided further evidence for the enhanced GMV deficits characterizing SCZ. Based on the clinical-neuroanatomical analyses, we cannot exclude possible confounding effects due to 1) age of onset and medication in BD patients, 2) symptoms severity in SCZ patients.RESULTSThe VBM results of Dataset1 showed 1) in BD compared to HC, GMV deficits in right cingulate, superior temporal and calcarine cortices, 2) in SCZ compared to HC, GMV deficits in widespread cortical and subcortical areas, 3) in SCZ compared to BD, GMV deficits in insula and thalamus (p<0.05, cluster family wise error corrected). The regions showing GMV deficits in the BD group were mostly included in the SCZ ones. The ROI analyses confirmed the VBM results at the regional level in most of the clusters from the SCZ vs. HC comparison (p<0.05, Bonferroni corrected). The VBM and ROI analyses of Dataset2 provided further evidence for the enhanced GMV deficits characterizing SCZ. Based on the clinical-neuroanatomical analyses, we cannot exclude possible confounding effects due to 1) age of onset and medication in BD patients, 2) symptoms severity in SCZ patients.Our study reported both shared and specific neuroanatomical characteristics between the two disorders, suggesting more severe and generalized GMV deficits in SCZ, with a specific role for insula and thalamus.CONCLUSIONOur study reported both shared and specific neuroanatomical characteristics between the two disorders, suggesting more severe and generalized GMV deficits in SCZ, with a specific role for insula and thalamus. Introduction Although schizophrenia (SCZ) and bipolar disorder (BD) share elements of pathology, their neural underpinnings are still under investigation. Here, structural Magnetic Resonance Imaging (MRI) data collected from a large sample of BD and SCZ patients and healthy controls (HC) were analyzed in terms of gray matter volume (GMV) using both voxel based morphometry (VBM) and a region of interest (ROI) approach. Methods The analysis was conducted on two datasets, Dataset1 (802 subjects: 243 SCZ, 176 BD, 383 HC) and Dataset2, a homogeneous subset of Dataset1 (301 subjects: 107 HC, 85 BD and 109 SCZ). General Linear Model analyses were performed 1) at the voxel-level in the whole brain (VBM study), 2) at the regional level in the anatomical regions emerged from the VBM study (ROI study). The GMV comparison across groups was integrated with the analysis of GMV correlates of different clinical dimensions. Results The VBM results of Dataset1 showed 1) in BD compared to HC, GMV deficits in right cingulate, superior temporal and calcarine cortices, 2) in SCZ compared to HC, GMV deficits in widespread cortical and subcortical areas, 3) in SCZ compared to BD, GMV deficits in insula and thalamus (p<0.05, cluster family wise error corrected). The regions showing GMV deficits in the BD group were mostly included in the SCZ ones. The ROI analyses confirmed the VBM results at the regional level in most of the clusters from the SCZ vs. HC comparison (p<0.05, Bonferroni corrected). The VBM and ROI analyses of Dataset2 provided further evidence for the enhanced GMV deficits characterizing SCZ. Based on the clinical-neuroanatomical analyses, we cannot exclude possible confounding effects due to 1) age of onset and medication in BD patients, 2) symptoms severity in SCZ patients. Conclusion Our study reported both shared and specific neuroanatomical characteristics between the two disorders, suggesting more severe and generalized GMV deficits in SCZ, with a specific role for insula and thalamus. Although schizophrenia (SCZ) and bipolar disorder (BD) share elements of pathology, their neural underpinnings are still under investigation. Here, structural Magnetic Resonance Imaging (MRI) data collected from a large sample of BD and SCZ patients and healthy controls (HC) were analyzed in terms of gray matter volume (GMV) using both voxel based morphometry (VBM) and a region of interest (ROI) approach. The analysis was conducted on two datasets, Dataset1 (802 subjects: 243 SCZ, 176 BD, 383 HC) and Dataset2, a homogeneous subset of Dataset1 (301 subjects: 107 HC, 85 BD and 109 SCZ). General Linear Model analyses were performed 1) at the voxel-level in the whole brain (VBM study), 2) at the regional level in the anatomical regions emerged from the VBM study (ROI study). The GMV comparison across groups was integrated with the analysis of GMV correlates of different clinical dimensions. The VBM results of Dataset1 showed 1) in BD compared to HC, GMV deficits in right cingulate, superior temporal and calcarine cortices, 2) in SCZ compared to HC, GMV deficits in widespread cortical and subcortical areas, 3) in SCZ compared to BD, GMV deficits in insula and thalamus (p<0.05, cluster family wise error corrected). The regions showing GMV deficits in the BD group were mostly included in the SCZ ones. The ROI analyses confirmed the VBM results at the regional level in most of the clusters from the SCZ vs. HC comparison (p<0.05, Bonferroni corrected). The VBM and ROI analyses of Dataset2 provided further evidence for the enhanced GMV deficits characterizing SCZ. Based on the clinical-neuroanatomical analyses, we cannot exclude possible confounding effects due to 1) age of onset and medication in BD patients, 2) symptoms severity in SCZ patients. Our study reported both shared and specific neuroanatomical characteristics between the two disorders, suggesting more severe and generalized GMV deficits in SCZ, with a specific role for insula and thalamus. |
| Audience | Academic |
| Author | Maggioni, Eleonora Sauer, Heinrich Poletti, Sara Perlini, Cinzia Ruggeri, Mirella Marinelli, Veronica Brambilla, Paolo Crespo-Facorro, Benedicto Roiz-Santiañez, Roberto Altamura, A. Carlo Diwadkar, Vaibhav A. Benedetti, Francesco Gaser, Christian Nenadic, Igor Bellani, Marcella |
| AuthorAffiliation | 4 Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany 2 Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain King's College London, UNITED KINGDOM 6 Department of Clinical Neurosciences and Centro di Eccellenza Risonanza Magnetica ad Alto Campo, Scientific Institute and University Vita-Salute San Raffaele, Milan, Italy 1 Department of Neurosciences and Mental Health, IRCCS Fondazione Ca’ Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy 8 Department of Experimental and Clinical Medical Sciences (DISM), University of Udine, Udine, Italy 3 CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Santander, Spain 5 Department of Psychiatry and Psychotherapy, Philipps University Marburg / Marburg University Hospital UKGM, Marburg, Germany 12 Department of Psychiatry & Behavioral Neuroscience, Wayne State University, Detroit, MI, United States of America 7 D |
| AuthorAffiliation_xml | – name: 8 Department of Experimental and Clinical Medical Sciences (DISM), University of Udine, Udine, Italy – name: 1 Department of Neurosciences and Mental Health, IRCCS Fondazione Ca’ Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy – name: 13 IRCCS Scientific Institute “E. Medea”, Bosisio Parini, Lecco, Italy – name: 3 CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Santander, Spain – name: 6 Department of Clinical Neurosciences and Centro di Eccellenza Risonanza Magnetica ad Alto Campo, Scientific Institute and University Vita-Salute San Raffaele, Milan, Italy – name: 9 Section of Psychiatry, Azienda Ospedaliera Universitaria Integrata Verona, Verona, Italy – name: 11 Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy – name: 10 Department of Neurosciences, Biomedicine and Movement Sciences, Section of Clinical Psychology, University of Verona, Verona, Italy – name: 14 Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy – name: 12 Department of Psychiatry & Behavioral Neuroscience, Wayne State University, Detroit, MI, United States of America – name: King's College London, UNITED KINGDOM – name: 2 Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain – name: 4 Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany – name: 7 Department of Neurology, Jena University Hospital, Jena, Germany – name: 5 Department of Psychiatry and Psychotherapy, Philipps University Marburg / Marburg University Hospital UKGM, Marburg, Germany |
| Author_xml | – sequence: 1 givenname: Eleonora orcidid: 0000-0003-4331-4029 surname: Maggioni fullname: Maggioni, Eleonora – sequence: 2 givenname: Benedicto surname: Crespo-Facorro fullname: Crespo-Facorro, Benedicto – sequence: 3 givenname: Igor surname: Nenadic fullname: Nenadic, Igor – sequence: 4 givenname: Francesco surname: Benedetti fullname: Benedetti, Francesco – sequence: 5 givenname: Christian surname: Gaser fullname: Gaser, Christian – sequence: 6 givenname: Heinrich surname: Sauer fullname: Sauer, Heinrich – sequence: 7 givenname: Roberto surname: Roiz-Santiañez fullname: Roiz-Santiañez, Roberto – sequence: 8 givenname: Sara surname: Poletti fullname: Poletti, Sara – sequence: 9 givenname: Veronica surname: Marinelli fullname: Marinelli, Veronica – sequence: 10 givenname: Marcella surname: Bellani fullname: Bellani, Marcella – sequence: 11 givenname: Cinzia surname: Perlini fullname: Perlini, Cinzia – sequence: 12 givenname: Mirella surname: Ruggeri fullname: Ruggeri, Mirella – sequence: 13 givenname: A. Carlo surname: Altamura fullname: Altamura, A. Carlo – sequence: 14 givenname: Vaibhav A. surname: Diwadkar fullname: Diwadkar, Vaibhav A. – sequence: 15 givenname: Paolo surname: Brambilla fullname: Brambilla, Paolo |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29136642$$D View this record in MEDLINE/PubMed |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Membership of the ENPACT group is provided in the Acknowledgments. Current address: Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy |
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| Snippet | Although schizophrenia (SCZ) and bipolar disorder (BD) share elements of pathology, their neural underpinnings are still under investigation. Here, structural... Introduction Although schizophrenia (SCZ) and bipolar disorder (BD) share elements of pathology, their neural underpinnings are still under investigation.... |
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| SubjectTerms | Adolescent Adult Biology and Life Sciences Bipolar disorder Bipolar Disorder - diagnostic imaging Bipolar Disorder - pathology Care and treatment Cognition Diagnosis Europe Female Humans Magnetic Resonance Imaging Male Medicine and Health Sciences Middle Aged Research and Analysis Methods Risk factors Schizophrenia Schizophrenia - diagnostic imaging Schizophrenia - pathology Young Adult |
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| Title | Common and distinct structural features of schizophrenia and bipolar disorder: The European Network on Psychosis, Affective disorders and Cognitive Trajectory (ENPACT) study |
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