Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis

Version 1 : Received: 30 November 2020 / Approved: 3 December 2020 / Online: 3 December 2020 (10:38:31 CET)

A peer-reviewed article of this Preprint also exists.

Stoyanov, D.; Kandilarova, S.; Aryutova, K.; Paunova, R.; Todeva-Radneva, A.; Latypova, A.; Kherif, F. Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis. Diagnostics 2021, 11, 19. Stoyanov, D.; Kandilarova, S.; Aryutova, K.; Paunova, R.; Todeva-Radneva, A.; Latypova, A.; Kherif, F. Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis. Diagnostics 2021, 11, 19.

Journal reference: Diagnostics 2020, 11, 19
DOI: 10.3390/diagnostics11010019

Abstract

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 to reach 90 % accuracy of the prediction.

Subject Areas

multivariate linear method; validation; diagnosis; discriminative; signatures of disease; schizophrenia; depression

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