Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls.

This study was conducted to examine whether there are quantitative or qualitative differences in the connectome between psychiatric patients and healthy controls and to delineate the connectome features of major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BD), as well as the severity of these disorders. Toward this end, we performed an effective connectivity analysis of resting state functional MRI data in these three patient groups and healthy controls. We used spectral Dynamic Causal Modeling (spDCM), and the derived connectome features were further subjected to machine learning. The results outlined a model of five connections, which discriminated patients from controls, comprising major nodes of the limbic system (amygdala (AMY), hippocampus (HPC) and anterior cingulate cortex (ACC)), the salience network (anterior insula (AI), and the frontoparietal and dorsal attention network (middle frontal gyrus (MFG), corresponding to the dorsolateral prefrontal cortex, and frontal eye field (FEF)). Notably, the alterations in the self-inhibitory connection of the anterior insula emerged as a feature of both mood disorders and SCZ. Moreover, four out of the five connectome features that discriminate mental illness from controls are features of mood disorders (both MDD and BD), namely the MFG→FEF, HPC→FEF, AI→AMY, and MFG→AMY connections, whereas one connection is a feature of SCZ, namely the AMY→SPL connectivity. A large part of the variance in the severity of depression (31.6%) and SCZ (40.6%) was explained by connectivity features. In conclusion, dysfunctions in the self-regulation of the salience network may underpin major mental disorders, while other key connectome features shape differences between mood disorders and SCZ, and can be used as potential imaging biomarkers.

Clinical Use of Neurophysiological Biomarkers and Self-Assessment Scales to Predict and Monitor Treatment Response for Psychotic and Affective Disorders.

Psychoses and affective disorders are severe mental illnesses with a considerable negative effect on an individual and global scale. They are among the most damaging and socially significant diseases, which contribute to permanent disabilities for the patients. The aim of this review is to analyse the capacity of neuroscientific methods as tools to reform psychiatry into a biologically valid medical discipline. Furthermore, it will focus on the application of the translational approach towards the diagnostic and therapeutic processes, as well as monitoring of treatment response by using valid biomarkers and psychometric instruments. By combining translational neuroscience with the latest psychopharmacology advances, clinicians might be able to provide better quality of precision and individualized medical care for their patients. We visualise a reality in which neuroimaging methods will modify the standard clinical evaluation of neuropsychiatric disorders, leading to a biologically valid diagnosis, monitoring and treatment in everyday clinical practice.