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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871050/ https://www.ncbi.nlm.nih.gov/pubmed/35204560 http://dx.doi.org/10.3390/diagnostics12020469 |
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author | Paunova, Rositsa Kandilarova, Sevdalina Todeva-Radneva, Anna Latypova, Adeliya Kherif, Ferath Stoyanov, Drozdstoy |
author_facet | Paunova, Rositsa Kandilarova, Sevdalina Todeva-Radneva, Anna Latypova, Adeliya Kherif, Ferath Stoyanov, Drozdstoy |
author_sort | Paunova, Rositsa |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-8871050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88710502022-02-25 Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression Paunova, Rositsa Kandilarova, Sevdalina Todeva-Radneva, Anna Latypova, Adeliya Kherif, Ferath Stoyanov, Drozdstoy Diagnostics (Basel) Article 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. MDPI 2022-02-12 /pmc/articles/PMC8871050/ /pubmed/35204560 http://dx.doi.org/10.3390/diagnostics12020469 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Paunova, Rositsa Kandilarova, Sevdalina Todeva-Radneva, Anna Latypova, Adeliya Kherif, Ferath Stoyanov, Drozdstoy Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression |
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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871050/ https://www.ncbi.nlm.nih.gov/pubmed/35204560 http://dx.doi.org/10.3390/diagnostics12020469 |
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