Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Paunova, Rositsa, Kandilarova, Sevdalina, Todeva-Radneva, Anna, Latypova, Adeliya, Kherif, Ferath, Stoyanov, Drozdstoy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784656903537688576
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
work_keys_str_mv AT paunovarositsa applicationofmassmultivariateanalysisonneuroimagingdatasetsforprecisiondiagnosticsofdepression
AT kandilarovasevdalina applicationofmassmultivariateanalysisonneuroimagingdatasetsforprecisiondiagnosticsofdepression
AT todevaradnevaanna applicationofmassmultivariateanalysisonneuroimagingdatasetsforprecisiondiagnosticsofdepression
AT latypovaadeliya applicationofmassmultivariateanalysisonneuroimagingdatasetsforprecisiondiagnosticsofdepression
AT kherifferath applicationofmassmultivariateanalysisonneuroimagingdatasetsforprecisiondiagnosticsofdepression
AT stoyanovdrozdstoy applicationofmassmultivariateanalysisonneuroimagingdatasetsforprecisiondiagnosticsofdepression