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

Drozdstoy Stoyanov,Sevdalina Kandilarova,Ferath Kherif,Anna Todeva-Radneva,Katrin Aryutova,Rositsa Paunova,Adeliya Latypova

doi:10.3390/diagnostics11010019

Drozdstoy Stoyanov, Sevdalina Kandilarova + Show 5 more

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https://doi.org/10.3390/diagnostics11010019

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Abstract

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.

Highlights

Schizophrenia (SCH) and depression are psychiatric disorders that have a very high prevalence in psychiatric clinical care and cause immense social burden in terms of disability and health care costs [1]
They are amongst the most detrimental and socially significant disorders which lead to chronic disability of the patients. Those individuals have an average mortality rate that is 2-3 times greater than the general population, resulting in a reduced lifetime of 10 to 20 years [2]. Both schizophrenia and depression are associated with a high risk of comorbidity with somatic illnesses as well as other psychiatric disorders, which leads to serious health consequences in addition to the substantial risk of self-inflicted death [3,4]
A number of studies exploring the symptomatologic overlap among different disorders have demonstrated the existence of neurobiological alterations associated with various dysfunctions which transcend beyond the categorical classification of SCH, bipolar disorder, and major depressive disorder (MDD) [7,8]

Summary

Introduction

Schizophrenia (SCH) and depression are psychiatric disorders that have a very high prevalence in psychiatric clinical care and cause immense social burden in terms of disability and health care costs [1] They are amongst the most detrimental and socially significant disorders which lead to chronic disability of the patients. Those individuals have an average mortality rate that is 2-3 times greater than the general population, resulting in a reduced lifetime of 10 to 20 years [2] Both schizophrenia and depression are associated with a high risk of comorbidity with somatic illnesses as well as other psychiatric disorders, which leads to serious health consequences in addition to the substantial risk of self-inflicted death [3,4]. There is a lack of valid consensus-based biomarkers to underpin the clinical diagnosis in psychiatry [10]

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