Brent G. Nelson

Altered resting state complexity in schizophrenia

The complexity of the human brains 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.

Nintendo Wii Balance Board is sensitive to effects of visual tasks on standing sway in healthy elderly adults

Research has shown that the Nintendo Wii Balance Board (WBB) can reliably detect the quantitative kinematics of the center of pressure in stance. Previous studies used relatively coarse manipulations (1- vs. 2-leg stance, and eyes open vs. closed). We sought to determine whether the WBB could reliably detect postural changes associated with subtle variations in visual tasks. Healthy elderly adults stood on a WBB while performing one of two visual tasks. In the Inspection task, they maintained their gaze within the boundaries of a featureless target. In the Search task, they counted the occurrence of designated target letters within a block of text. Consistent with previous studies using traditional force plates, the positional variability of the center of pressure was reduced during performance of the Search task, relative to movement during performance of the Inspection task. Using detrended fluctuation analysis, a measure of movement dynamics, we found that COP trajectories were more predictable during performance of the Search task than during performance of the Inspection task. The results indicate that the WBB is sensitive to subtle variations in both the magnitude and dynamics of body sway that are related to variations in visual tasks engaged in during stance. The WBB is an inexpensive, reliable technology that can be used to evaluate subtle characteristics of body sway in large or widely dispersed samples.

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.

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.

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.

Implementation of a platform-agnostic working memory span task using mobile device technology

Working Memory Span (WMS) tasks are some of the most commonly used tools in cognitive psychology. These tasks involve the presentation of a set of items in a specified sequence. The participant is then asked to recall the sequence of items in forward or reverse order with performance quantified by the maximum number of items correctly recalled. Though initially the test was developed for verbal administration, now with modern computing, these tasks are generally administered via a digital interface. As a result, a variety of implementations have been developed but generally target either research or consumer domains. There has yet to be an offering capable of making the necessary transition from validation in the research lab, through implementation in the clinic, to distribution directly to the consumer. The main barrier has been lack of an open, approachable architecture, robust enough for basic scientists but also technically scalable for wide distribution to consumers. Recent advances in software technology provide the necessary infrastructure for the development of a robust offering. We have developed a WMS task, built on the latest in multi-platform gaming technology, to act as a blueprint for how future tasks can be developed, tested, and distributed. Our implementation describes some of the fundamental principles necessary for cultivation of a rich ecosystem of health diagnostic and therapeutic software.