Gender Differences in Current Received during Transcranial Electrical Stimulation.

Low current transcranial electrical stimulation (tCS) is an effective but somewhat inconsistent tool for augmenting neuromodulation. In this study, we used 3D MRI guided electrical transcranial stimulation modeling to estimate the range of current intensities received at cortical brain tissues. Combined T1, T2, and proton density MRIs from 24 adult subjects (12 male and 12 female) were modeled with virtual electrodes placed at F3, F4, C3, and C4. Two sizes of electrodes 20 mm round and 50 mm × 45 mm were examined at 0.5, 1, and 2 mA input currents. The intensity of current received was sampled in a 1-cm sphere placed at the cortex directly under each scalp electrode. There was a 10-fold difference in the amount of current received by individuals. A large gender difference was observed with female subjects receiving significantly less current at targeted parietal cortex than male subjects when stimulated at identical current levels (P < 0.05). Larger electrodes delivered somewhat larger amounts of current than the smaller ones (P < 0.01). Electrodes in the frontal regions delivered less current than those in the parietal region (P < 0.05). There were large individual differences in current levels that the subjects received. Analysis of the cranial bone showed that the gender difference and the frontal parietal differences are due to differences in cranial bone. Males have more cancelous parietal bone and females more dense parietal bone (P < 0.01). These differences should be considered when planning tCS studies and call into question earlier reports of gender differences due to hormonal influences

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Modeling brain dynamics in brain tumor patients using the virtual brain

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance