Transcranial Direct Current Stimulation (tDCS) paired with a decision-making task reduces risk-taking in a clinically impulsive sample.

BACKGROUND: Impulsivity is a multidimensional personality trait observed across a variety of psychiatric disorders. Transcranial direct current stimulation (tDCS) applied over dorsolateral prefrontal cortex (DLPFC) has shown promise as an intervention to reduce impulsivity. OBJECTIVE: To investigate the effects of tDCS paired with a decision-making task on risk-taking in Veterans with a clinical history of impulsive behavior. METHODS: This was a randomized, single-blind, sham-controlled study. Participants performed the Balloon Analogue Risk Task (BART) while concurrently receiving either active or sham tDCS (right anodal/left cathodal over DLPFC) twice a day for five days. To evaluate generalization, the Risk Task was performed before and after the complete course of intervention. To evaluate durability, the BART and Risk Task were administered again at one and two month follow-up sessions. RESULTS: Thirty Veterans participated: 15 received active tDCS and 15 received sham tDCS. For the trained BART task, individual growth curve analysis (IGC) examining individual variation of the growth rates over time showed no significant variations in individual trajectory changes over time (ß = 0.02, p > 0.05). For the untrained Risk Task, IGC showed that the active tDCS group had a significant 46% decrease in risky choice from pre-to post-intervention, which persisted through the one and two month follow-up sessions. The sham tDCS group showed no significant change in risky choice from pre-to post-intervention. CONCLUSIONS: tDCS over DLPFC paired with a decision-making task effectively reduced risk-taking behavior in a group of Veterans with clinically-relevant impulsivity. Results suggest that this approach may be an effective neuroplasticity-based intervention for patients affected by impulsivity.

Consensus Recommendations for the Clinical Application of Repetitive Transcranial Magnetic Stimulation (rTMS) in the Treatment of Depression.

OBJECTIVE: To provide expert recommendations for the safe and effective application of repetitive transcranial magnetic stimulation (rTMS) in the treatment of major depressive disorder (MDD). PARTICIPANTS: Participants included a group of 17 expert clinicians and researchers with expertise in the clinical application of rTMS, representing both the National Network of Depression Centers (NNDC) rTMS Task Group and the American Psychiatric Association Council on Research (APA CoR) Task Force on Novel Biomarkers and Treatments. EVIDENCE: The consensus statement is based on a review of extensive literature from 2 databases (OvidSP MEDLINE and PsycINFO) searched from 1990 through 2016. The search terms included variants of major depressive disorder and transcranial magnetic stimulation. The results were limited to articles written in English that focused on adult populations. Of the approximately 1,500 retrieved studies, a total of 118 publications were included in the consensus statement and were supplemented with expert opinion to achieve consensus recommendations on key issues surrounding the administration of rTMS for MDD in clinical practice settings. CONSENSUS PROCESS: In cases in which the research evidence was equivocal or unclear, a consensus decision on how rTMS should be administered was reached by the authors of this article and is denoted in the article as "expert opinion." CONCLUSIONS: Multiple randomized controlled trials and published literature have supported the safety and efficacy of rTMS antidepressant therapy. These consensus recommendations, developed by the NNDC rTMS Task Group and APA CoR Task Force on Novel Biomarkers and Treatments, provide comprehensive information for the safe and effective clinical application of rTMS in the treatment of MDD.

Comparison of large-scale human brain functional and anatomical networks in schizophrenia.

Schizophrenia is a disease with disruptions in thought, emotion, and behavior. The dysconnectivity hypothesis suggests these disruptions are due to aberrant brain connectivity. Many studies have identified connectivity differences but few have been able to unify gray and white matter findings into one model. Here we develop an extension of the Network-Based Statistic (NBS) called NBSm (Multimodal Network-based statistic) to compare functional and anatomical networks in schizophrenia. Structural, resting functional, and diffusion magnetic resonance imaging data were collected from 29 chronic patients with schizophrenia and 29 healthy controls. Images were preprocessed, and average time courses were extracted for 90 regions of interest (ROI). Functional connectivity matrices were estimated by pairwise correlations between wavelet coefficients of ROI time series. Following diffusion tractography, anatomical connectivity matrices were estimated by white matter streamline counts between each pair of ROIs. Global and regional strength were calculated for each modality. NBSm was used to find significant overlap between functional and anatomical components that distinguished health from schizophrenia. Global strength was decreased in patients in both functional and anatomical networks. Regional strength was decreased in all regions in functional networks and only one region in anatomical networks. NBSm identified a distinguishing functional component consisting of 46 nodes with 113 links (p < 0.001), a distinguishing anatomical component with 47 nodes and 50 links (p = 0.002), and a distinguishing intermodal component with 26 nodes (p < 0.001). NBSm is a powerful technique for understanding network-based group differences present in both anatomical and functional data. In light of the dysconnectivity hypothesis, these results provide compelling evidence for the presence of significant overlapping anatomical and functional disruption in people with schizophrenia.

Low-Intensity Transcranial Current Stimulation in Psychiatry.

Neurostimulation is rapidly emerging as an important treatment modality for psychiatric disorders. One of the fastest-growing and least-regulated approaches to noninvasive therapeutic stimulation involves the application of weak electrical currents. Widespread enthusiasm for low-intensity transcranial electrical current stimulation (tCS) is reflected by the recent surge in direct-to-consumer device marketing, do-it-yourself enthusiasm, and an escalating number of clinical trials. In the wake of this rapid growth, clinicians may lack sufficient information about tCS to inform their clinical practices. Interpretation of tCS clinical trial data is aided by familiarity with basic neurophysiological principles, potential mechanisms of action of tCS, and the complicated regulatory history governing tCS devices. A growing literature includes randomized controlled trials of tCS for major depression, schizophrenia, cognitive disorders, and substance use disorders. The relative ease of use and abundant access to tCS may represent a broad-reaching and important advance for future mental health care. Evidence supports application of one type of tCS, transcranial direct current stimulation (tDCS), for major depression. However, tDCS devices do not have regulatory approval for treating medical disorders, evidence is largely inconclusive for other therapeutic areas, and their use is associated with some physical and psychiatric risks. One unexpected finding to arise from this review is that the use of cranial electrotherapy stimulation devices-the only category of tCS devices cleared for use in psychiatric disorders-is supported by low-quality evidence.

Evaluation of relational and NoSQL database architectures to manage genomic annotations.

While the adoption of next generation sequencing has rapidly expanded, the informatics infrastructure used to manage the data generated by this technology has not kept pace. Historically, relational databases have provided much of the framework for data storage and retrieval. Newer technologies based on NoSQL architectures may provide significant advantages in storage and query efficiency, thereby reducing the cost of data management. But their relative advantage when applied to biomedical data sets, such as genetic data, has not been characterized. To this end, we compared the storage, indexing, and query efficiency of a common relational database (MySQL), a document-oriented NoSQL database (MongoDB), and a relational database with NoSQL support (PostgreSQL). When used to store genomic annotations from the dbSNP database, we found the NoSQL architectures to outperform traditional, relational models for speed of data storage, indexing, and query retrieval in nearly every operation. These findings strongly support the use of novel database technologies to improve the efficiency of data management within the biological sciences.

Altered resting state complexity in schizophrenia.

The complexity of the human brain's activity and connectivity varies over temporal scales and is altered in disease states such as schizophrenia. Using a multi-level analysis of spontaneous low-frequency fMRI data stretching from the activity of individual brain regions to the coordinated connectivity pattern of the whole brain, we investigate the role of brain signal complexity in schizophrenia. Specifically, we quantitatively characterize the univariate wavelet entropy of regional activity, the bivariate pairwise functional connectivity between regions, and the multivariate network organization of connectivity patterns. Our results indicate that univariate measures of complexity are less sensitive to disease state than higher level bivariate and multivariate measures. While wavelet entropy is unaffected by disease state, the magnitude of pairwise functional connectivity is significantly decreased in schizophrenia and the variance is increased. Furthermore, by considering the network structure as a function of correlation strength, we find that network organization specifically of weak connections is strongly correlated with attention, memory, and negative symptom scores and displays potential as a clinical biomarker, providing up to 75% classification accuracy and 85% sensitivity. We also develop a general statistical framework for the testing of group differences in network properties, which is broadly applicable to studies where changes in network organization are crucial to the understanding of brain function.