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.

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.

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.

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.

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 standard clinical evaluation of neuropsychiatric disorders, leading to a biologically valid diagnosis, monitoring and treatment in everyday clinical practice.

Background: This study aimed to explore possible differences of the whole-brain functional connectivity of the anterior cingulate cortex (ACC) and anterior insula (AI), in a sample of depressed patients with major depressive disorder (MDD), bipolar disorder (BD) and healthy controls (HC). Methods: A hundred and three subjects (n_MDD = 35, n_BD = 25, and n_HC = 43) between the ages of eighteen and sixty-five years old underwent functional magnetic resonance imaging. The CONN Toolbox was used to process and analyze the functional connectivity of the ACC and AI. Results: The comparison between the patients (MDD/BD) and HC yielded increased resting-state functional connectivity (rsFC) between the ACC and the motor and somatosensory cortices (SSC), superior parietal lobule (SPL), precuneus, and lateral occipital cortex, which was driven by the BD group. In addition, hyperconnectivity between the right AI and the motor and SSC was found in BD, as compared to HC. In MDD, as compared to HC, hyperconnectivity between ACC and SPL and the lateral occipital cortex was found, with no statistical rsFC differences for the AI seed. Compared to BD, the MDD group showed ACC–cerebellum hyperconnectivity and a trend for increased rsFC between the right AI and the bilateral superior frontal cortex. Conclusions: Considering the observed hyperconnectivity between the ACC/somatosensory cortex in the patient group, we suggest depression may be related to an impairment of the sensory-discriminative function of the SSC, which results in the phenomenological signature of mental pain in both MDD and BD. These findings suggest that future research should investigate this particular network with respect to motor functions and executive control, as a potential differential diagnostic biomarker for MDD and BD.