We constructed a novel design integrating the administration of a clinical self-assessment scale with simultaneous acquisition of functional Magnetic Resonance Imaging (fMRI), aiming at cross-validation between psychopathology evaluation and neuroimaging techniques. We hypothesized that areas demonstrating differential activation in two groups of patients (the first group exhibiting paranoid delusions in the context of paranoid schizophrenia—SCH—and second group with a depressive episode in the context of major depressive disorder or bipolar disorder—DEP) will have distinct connectivity patterns and structural differences. Fifty-one patients with SCH (n = 25) or DEP (n = 26) were scanned with three different MRI sequences: a structural and two functional sequences—resting-state and task-related fMRI (the stimuli represent items from a paranoid-depressive self-evaluation scale). While no significant differences were found in gray matter volumes, we were able to discriminate between the two clinical entities by identifying two significant clusters of activations in the SCH group—the left Precuneus (PreCu) extending to the left Posterior Cingulate Cortex (PCC) and the right Angular Gyrus (AG). Additionally, the effective connectivity of the middle frontal gyrus (MFG), a part of the Dorsolateral Prefrontal Cortex (DLPFC) to the Anterior Insula (AI), demonstrated a significant difference between the two groups with inhibitory connection demonstrated only in SCH. The observed activations of PreCu, PCC, and AG (involved in the Default Mode Network DMN) might be indirect evidence of the inhibitory connection from the DLPFC to AI, interfering with the balancing function of the insula as the dynamic switch in the DMN. The findings of our current study might suggest that the connectivity from DLPFC to the anterior insula can be interpreted as evidence for the presence of an aberrant network that leads to behavioral abnormalities, the manifestation of which depends on the direction of influence. The reduced effective connectivity from the AI to the DLPFC is manifested as depressive symptoms, and the inhibitory effect from the DLPFC to the AI is reflected in the paranoid symptoms of schizophrenia.

Introduction: There exists over the past decades a constant debate driven by controversies in the validity of psychiatric diagnosis. This debate is grounded in queries about both the validity and evidence strength of clinical measures. Materials and Methods: The objective of the study is to construct a bottom-up unsupervised machine learning approach, where the brain signatures identified by three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI). Results: We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. Following the individual and group MLM, we identified the three brain patterns that summarized all the individual variabilities of the individual brain patterns. Discussion: This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.

Traditional therapeutic methods in psychiatry, such as psychopharmacology and psychotherapy help many people suffering from mental disorders, but in the long-term prove to be effective in a relatively small proportion of those affected. Therapeutically, resistant forms of mental disorders such as schizophrenia, major depressive disorder, and bipolar disorder lead to persistent distress and dysfunction in personal, social, and professional aspects. In an effort to address these problems, the translational approach in neuroscience has initiated the inclusion of novel or modified unconventional diagnostic and therapeutic techniques with promising results. For instance, neuroimaging data sets from multiple modalities provide insight into the nature of pathophysiological mechanisms such as disruptions of connectivity, integration, and segregation of neural networks, focusing on the treatment of mental disorders through instrumental biomedical methods such as electro-convulsive therapy (ECT), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS) and deep brain stimulation (DBS). These methodologies have yielded promising results that have yet to be understood and improved to enhance the prognosis of the severe and persistent psychotic and affective disorders. The current review is focused on the translational approach in the management of schizophrenia and mood disorders, as well as the adaptation of new transdisciplinary diagnostic tools such as neuroimaging with concurrently administered psychopathological questionnaires and integration of the results into the therapeutic framework using various advanced instrumental biomedical tools such as ECT, TMS, tDCS and DBS.

BACKGROUND Over the past decade, resting-state functional magnetic resonance imaging (rs-fMRI) has concentrated on brain networks such as the default mode network (DMN), the salience network (SN), and the central executive network (CEN), allowing for a better understanding of cognitive deficits observed in mental disorders, as well as other characteristic psychopathological phenomena such as thought and behavior disorganization. AIM To investigate differential patterns of effective connectivity across distributed brain networks involved in schizophrenia (SCH) and mood disorders. METHODS The sample comprised 58 patients with either paranoid syndrome in the context of SCH ( n = 26) or depressive syndrome (Ds) ( n = 32), in the context of major depressive disorder or bipolar disorder. The methods used include rs-fMRI and subsequent dynamic causal modeling to determine the direction and strength of connections to and from various nodes in the DMN, SN and CEN. RESULTS A significant excitatory connection from the dorsal anterior cingulate cortex to the anterior insula (aI) was observed in the SCH patient group, whereas inhibitory connections from the precuneus to the ventrolateral prefrontal cortex and from the aI to the precuneus were observed in the Ds group. CONCLUSION The results delineate specific patterns associated with SCH and Ds and offer a better explanation of the underlying mechanisms of these disorders, and inform differential diagnosis and precise treatment targeting.

Introduction:In the current study, we used the Stroop Color and Word Test (SCWT) combined with an n-back component in functional magnetic resonance imaging (fMRI) in order to activate the working memory and cognitive interference in patients with Major Depressive Disorder (MDD) as compared to healthy controls. Our hypothesis was that there would be significant alterations in the selective visual attention processing regions of the brain which may identify mechanisms underlying major depression. Materials and Methods: Fifty participants, of which 24 were patients with depression and 26 healthy controls were recruited. Results: The first major finding of the current study was hypoactivation in the lingual gyrus during the condition with instructions to track the sequence of the words (word>color) of the Stroop n-back task and hyperactivation of the same structure in the opposite (color>word) condition where subjects had to focus on the order of the word color in depressed patients as compared to healthy controls. Conclusions: Changes in these regions have been consistently reported across studies with different fMRI techniques in both adolescent and adult patients with MDD reinforcing the role of the region in the pathophysiology of depression. Further studies are needed to examine possible longitudinal changes in the region and its activity in remission.