Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes
IntroductionIn this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and he...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1272933/full |
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| author | Rositsa Paunova Rositsa Paunova Cristina Ramponi Sevdalina Kandilarova Sevdalina Kandilarova Anna Todeva-Radneva Anna Todeva-Radneva Adeliya Latypova Drozdstoy Stoyanov Drozdstoy Stoyanov Ferath Kherif |
| author_facet | Rositsa Paunova Rositsa Paunova Cristina Ramponi Sevdalina Kandilarova Sevdalina Kandilarova Anna Todeva-Radneva Anna Todeva-Radneva Adeliya Latypova Drozdstoy Stoyanov Drozdstoy Stoyanov Ferath Kherif |
| author_sort | Rositsa Paunova |
| collection | DOAJ |
| description | IntroductionIn this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54).MethodsWe extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.ResultsAs a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 – for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.DiscussionOur results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders. |
| first_indexed | 2024-03-11T18:10:19Z |
| format | Article |
| id | doaj.art-92a2a3550a8145729436aa6f16f18e56 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-03-11T18:10:19Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-92a2a3550a8145729436aa6f16f18e562023-10-16T15:27:33ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402023-10-011410.3389/fpsyt.2023.12729331272933Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzesRositsa Paunova0Rositsa Paunova1Cristina Ramponi2Sevdalina Kandilarova3Sevdalina Kandilarova4Anna Todeva-Radneva5Anna Todeva-Radneva6Adeliya Latypova7Drozdstoy Stoyanov8Drozdstoy Stoyanov9Ferath Kherif10Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, BulgariaResearch Institute, Medical University Plovdiv, Plovdiv, BulgariaLaboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, BulgariaResearch Institute, Medical University Plovdiv, Plovdiv, BulgariaDepartment of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, BulgariaResearch Institute, Medical University Plovdiv, Plovdiv, BulgariaLaboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, BulgariaResearch Institute, Medical University Plovdiv, Plovdiv, BulgariaLaboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandIntroductionIn this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54).MethodsWe extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.ResultsAs a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 – for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.DiscussionOur results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1272933/fullschizophreniamajor depressive disorderbipolar disorderneuroimagingstructural covariance |
| spellingShingle | Rositsa Paunova Rositsa Paunova Cristina Ramponi Sevdalina Kandilarova Sevdalina Kandilarova Anna Todeva-Radneva Anna Todeva-Radneva Adeliya Latypova Drozdstoy Stoyanov Drozdstoy Stoyanov Ferath Kherif Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes Frontiers in Psychiatry schizophrenia major depressive disorder bipolar disorder neuroimaging structural covariance |
| title | Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes |
| title_full | Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes |
| title_fullStr | Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes |
| title_full_unstemmed | Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes |
| title_short | Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes |
| title_sort | degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes |
| topic | schizophrenia major depressive disorder bipolar disorder neuroimaging structural covariance |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1272933/full |
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