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 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.

Automated summary

Traditional psychiatric diagnosis has faced challenges in bridging the gap between neuro-biological and clinical assessments, leading to discrepancies under conventional statistical frameworks. To address this, a novel machine learning technique called multivariate linear method (MLM) was designed to capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging, as well as relevant clinical measures. Results from applying MLM analysis in patients with schizophrenia compared to depression show that combining neuroimaging and clinical data can inform differential diagnosis with incremental validity.