Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes

Frontiers in Psychiatry

Psychiatry

Anna Todeva-Radneva¹  Rositsa Paunova¹  Sevdalina Kandilarova¹  Drozdstoy Stoyanov¹  Cristina Ramponi²  Ferath Kherif²  Adeliya Latypova² 

[1] Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria;Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria;Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland;

关键词: schizophrenia;    major depressive disorder;    bipolar disorder;    neuroimaging;    structural covariance;   

DOI  :  10.3389/fpsyt.2023.1272933

 received in 2023-08-04, accepted in 2023-10-02,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Paunova, Ramponi, Kandilarova, Todeva-Radneva, Latypova, Stoyanov and Kherif.