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Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis.
Stoyanov, Drozdstoy; Kandilarova, Sevdalina; Aryutova, Katrin; Paunova, Rositsa; Todeva-Radneva, Anna; Latypova, Adeliya; Kherif, Ferath.
Afiliación
  • Stoyanov D; Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria.
  • Kandilarova S; Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria.
  • Aryutova K; Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria.
  • Paunova R; Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria.
  • Todeva-Radneva A; Department of Psychiatry and Medical Psychology and Research Institute at Medical University of Plovdiv, 4000 Plovdiv, Bulgaria.
  • Latypova A; Centre for Research in Neuroscience-Department of Clinical Neurosciences, CHUV-UNIL, 1010 Lausanne, Switzerland.
  • Kherif F; Centre for Research in Neuroscience-Department of Clinical Neurosciences, CHUV-UNIL, 1010 Lausanne, Switzerland.
Diagnostics (Basel) ; 11(1)2020 Dec 24.
Article en En | MEDLINE | ID: mdl-33374207
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Diagnostics (Basel) Año: 2020 Tipo del documento: Article País de afiliación: Bulgaria Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Diagnostics (Basel) Año: 2020 Tipo del documento: Article País de afiliación: Bulgaria Pais de publicación: Suiza