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Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model—both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy.

Over the recent years, there has been an increasing application of noninvasive brain stimulation techniques such as transcranial direct-current stimulation (tDCS) to modify eating behaviors in healthy population and persons with eating disorders. tDCS is a noninvasive, neuromodulatory intervention which is well-tolerated and safe. In this case report, we describe the successful application of add-on tDCS in a patient with schizophrenia to reduce the craving for food that in turn, helped in reversing the weight gain.

Abnormalities in resting and event-related brain oscillations are known to be associated with cognitive deficits in schizophrenia. Transcranial alternating current stimulation (tACS) modulates these rhythms across the neuronal circuits and could have a potential therapeutic role in psychiatric disorders. In this report, we describe, for the first time, application of online tACS in a schizophrenia patient with working memory deficits. This case report supports the feasibility and potential utility of online tACS in schizophrenia, which needs further systematic research.

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses.Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods.Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models.Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.

Background: Deficient prefrontal cortex inhibitory control is of particular interest with regard to the pathogenesis of auditory hallucinations (AHs) in schizophrenia. Antisaccade task performance is a sensitive index of prefrontal inhibitory function and has been consistently found to be abnormal in schizophrenia. Methods: This study investigated the effect of transcranial direct current stimulation (tDCS) on antisaccade performance in 13 schizophrenia patients. Results: The tDCS resulted in significant reduction in antisaccade error percentage (t = 3.4; P = 0.005), final eye position gain (t = 2.3; P = 0.042), and AHs severity (t = 4.1; P = 0.003). Conclusion: Our results raise the possibility that improvement in antisaccade performance and severity of AH may be mechanistically related.

Transcranial Direct Current Stimulation (tDCS) involves application of weak direct electric currents (up to 2mA) using scalp electrodes with resultant neuroplasticity modulation by altering the cortical excitability. Though the side effect profile of tDCS is benign and less severe, the utility and safety of tDCS in dermatological conditions remains a concern. In this context, we report the safe administration of tDCS in a subject with substance induced psychosis and co-morbid psoriasis.

Hippocampal deficits are an established feature of schizophrenia and are complementary with recent evidences of marked allocentric processing deficits being reported in this disorder. By "Cognitive mapping" we intend to refer to the concepts from the seminal works of O′Keefe and Nadel (1978) that led to the development of cognitive map theory of hippocampal function. In this review, we summarize emerging evidences and issues that indicate that "Cognitive mapping deficits" form one of the important cognitive aberrations in schizophrenia. The importance has been placed upon hippocampally mediated allocentric processing deficits and their role in pathology of schizophrenia, for spatial/representational cognitive deficits and positive symptoms in particular. It is modestly summarized that emerging evidences point toward a web of spatial and cognitive representation errors concurrent with pronounced hippocampal dysfunction. In general, it can be stated that there are clear and consistent evidences that favor the cognitive mapping theory in explaining certain deficits of schizophrenia and for drawing out a possible and promising endophenotype/biomarkers. Further research in this regard demands attention.