Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis
Traditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, which are supposed to deliver biological explanations...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/11/1/19 |
| _version_ | 1797543659319590912 |
|---|---|
| author | Drozdstoy Stoyanov Sevdalina Kandilarova Katrin Aryutova Rositsa Paunova Anna Todeva-Radneva Adeliya Latypova Ferath Kherif |
| author_facet | Drozdstoy Stoyanov Sevdalina Kandilarova Katrin Aryutova Rositsa Paunova Anna Todeva-Radneva Adeliya Latypova Ferath Kherif |
| author_sort | Drozdstoy Stoyanov |
| collection | DOAJ |
| description | Traditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, which are supposed to deliver biological explanations of disease. In that context the neuro-biological and clinical assessment in psychiatry remained discrepant and incommensurable under conventional statistical frameworks. The emerging field of translational neuroimaging attempted to bridge the explanatory gap by means of simultaneous application of clinical assessment tools and functional magnetic resonance imaging, which also turned out to be problematic when analyzed with standard statistical methods. In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity. |
| first_indexed | 2024-03-10T13:48:46Z |
| format | Article |
| id | doaj.art-cc73772fa1884d039dc40f80fe38dabc |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T13:48:46Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-cc73772fa1884d039dc40f80fe38dabc2023-11-21T02:22:58ZengMDPI AGDiagnostics2075-44182020-12-011111910.3390/diagnostics11010019Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential DiagnosisDrozdstoy Stoyanov0Sevdalina Kandilarova1Katrin Aryutova2Rositsa Paunova3Anna Todeva-Radneva4Adeliya Latypova5Ferath Kherif6Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, BulgariaDepartment of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, BulgariaDepartment of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, BulgariaDepartment of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, BulgariaDepartment of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, BulgariaCentre for Research in Neuroscience—Department of Clinical Neurosciences, CHUV—UNIL, 1010 Lausanne, SwitzerlandCentre for Research in Neuroscience—Department of Clinical Neurosciences, CHUV—UNIL, 1010 Lausanne, SwitzerlandTraditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, which are supposed to deliver biological explanations of disease. In that context the neuro-biological and clinical assessment in psychiatry remained discrepant and incommensurable under conventional statistical frameworks. The emerging field of translational neuroimaging attempted to bridge the explanatory gap by means of simultaneous application of clinical assessment tools and functional magnetic resonance imaging, which also turned out to be problematic when analyzed with standard statistical methods. In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity.https://www.mdpi.com/2075-4418/11/1/19multivariate linear methodvalidationdiagnosisdiscriminativesignatures of diseaseschizophrenia |
| spellingShingle | Drozdstoy Stoyanov Sevdalina Kandilarova Katrin Aryutova Rositsa Paunova Anna Todeva-Radneva Adeliya Latypova Ferath Kherif Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis Diagnostics multivariate linear method validation diagnosis discriminative signatures of disease schizophrenia |
| title | Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis |
| title_full | Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis |
| title_fullStr | Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis |
| title_full_unstemmed | Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis |
| title_short | Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis |
| title_sort | multivariate analysis of structural and functional neuroimaging can inform psychiatric differential diagnosis |
| topic | multivariate linear method validation diagnosis discriminative signatures of disease schizophrenia |
| url | https://www.mdpi.com/2075-4418/11/1/19 |
| work_keys_str_mv | AT drozdstoystoyanov multivariateanalysisofstructuralandfunctionalneuroimagingcaninformpsychiatricdifferentialdiagnosis AT sevdalinakandilarova multivariateanalysisofstructuralandfunctionalneuroimagingcaninformpsychiatricdifferentialdiagnosis AT katrinaryutova multivariateanalysisofstructuralandfunctionalneuroimagingcaninformpsychiatricdifferentialdiagnosis AT rositsapaunova multivariateanalysisofstructuralandfunctionalneuroimagingcaninformpsychiatricdifferentialdiagnosis AT annatodevaradneva multivariateanalysisofstructuralandfunctionalneuroimagingcaninformpsychiatricdifferentialdiagnosis AT adeliyalatypova multivariateanalysisofstructuralandfunctionalneuroimagingcaninformpsychiatricdifferentialdiagnosis AT ferathkherif multivariateanalysisofstructuralandfunctionalneuroimagingcaninformpsychiatricdifferentialdiagnosis |