Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression
We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left pla...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/2/469 |
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| author | Rositsa Paunova Sevdalina Kandilarova Anna Todeva-Radneva Adeliya Latypova Ferath Kherif Drozdstoy Stoyanov |
| author_facet | Rositsa Paunova Sevdalina Kandilarova Anna Todeva-Radneva Adeliya Latypova Ferath Kherif Drozdstoy Stoyanov |
| author_sort | Rositsa Paunova |
| collection | DOAJ |
| description | We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry. |
| first_indexed | 2024-03-09T22:10:14Z |
| format | Article |
| id | doaj.art-4721029f031a4f02858814e217ea725c |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T22:10:14Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-4721029f031a4f02858814e217ea725c2023-11-23T19:32:34ZengMDPI AGDiagnostics2075-44182022-02-0112246910.3390/diagnostics12020469Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of DepressionRositsa Paunova0Sevdalina Kandilarova1Anna Todeva-Radneva2Adeliya Latypova3Ferath Kherif4Drozdstoy Stoyanov5Department of Psychiatry and Medical Psychology, Medical University Plovdiv, 4002 Plovdiv, BulgariaDepartment of Psychiatry and Medical Psychology, Medical University Plovdiv, 4002 Plovdiv, BulgariaDepartment of Psychiatry and Medical Psychology, Medical University Plovdiv, 4002 Plovdiv, BulgariaCentre for Research in Neuroscience, Department of Clinical Neurosciences, CHUV—UNIL, 1011 Lausanne, SwitzerlandCentre for Research in Neuroscience, Department of Clinical Neurosciences, CHUV—UNIL, 1011 Lausanne, SwitzerlandDepartment of Psychiatry and Medical Psychology, Medical University Plovdiv, 4002 Plovdiv, BulgariaWe used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry.https://www.mdpi.com/2075-4418/12/2/469mass multivariate analysisneuroimagingdepressionschizophrenia |
| spellingShingle | Rositsa Paunova Sevdalina Kandilarova Anna Todeva-Radneva Adeliya Latypova Ferath Kherif Drozdstoy Stoyanov Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression Diagnostics mass multivariate analysis neuroimaging depression schizophrenia |
| title | Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression |
| title_full | Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression |
| title_fullStr | Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression |
| title_full_unstemmed | Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression |
| title_short | Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression |
| title_sort | application of mass multivariate analysis on neuroimaging data sets for precision diagnostics of depression |
| topic | mass multivariate analysis neuroimaging depression schizophrenia |
| url | https://www.mdpi.com/2075-4418/12/2/469 |
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