Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes
| Title: | Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes |
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| Authors: | Rositsa Paunova, Cristina Ramponi, Sevdalina Kandilarova, Anna Todeva-Radneva, Adeliya Latypova, Drozdstoy Stoyanov, Ferath Kherif |
| Source: | Frontiers in Psychiatry, Vol 14 (2023) |
| Publisher Information: | Frontiers Media S.A., 2023. |
| Publication Year: | 2023 |
| Collection: | LCC:Psychiatry |
| Subject Terms: | schizophrenia, major depressive disorder, bipolar disorder, neuroimaging, structural covariance, Psychiatry, RC435-571 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1664-0640 81457294 |
| Relation: | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1272933/full; https://doaj.org/toc/1664-0640 |
| DOI: | 10.3389/fpsyt.2023.1272933 |
| Access URL: | https://doaj.org/article/92a2a3550a8145729436aa6f16f18e56 |
| Accession Number: | edsdoj.92a2a3550a8145729436aa6f16f18e56 |
| Database: | Directory of Open Access Journals |
| ISSN: | 16640640 81457294 |
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| DOI: | 10.3389/fpsyt.2023.1272933 |
| Published in: | Frontiers in Psychiatry |
| Language: | English |