We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.

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BackgroundGiven the large burden of non-communicable diseases (NCDs) among both Syrian refugees and the host communities within which they are settled, humanitarian actors and the government of Lebanon face immense challenges in addressing health needs. This study assessed health status, unmet needs, and utilization of health services among Syrian refugees and host communities in Lebanon.MethodsA cross-sectional survey of Syrian refugees and host communities in Lebanon was conducted using a two-stage cluster survey design with probability proportional to size sampling. To obtain information on chronic NCDs, respondents were asked a series of questions about hypertension, cardiovascular disease, diabetes, chronic respiratory disease, and arthritis. Differences in household characteristics by care-seeking for these conditions were examined using chi-square, t-test, and adjusted logistic regression methods.ResultsOver half (50.4 %) of refugee and host community households (60.2 %) reported a member with one of the five NCDs. Host community prevalence rates were significantly higher than refugees for all conditions except chronic respiratory diseases (p = 0.08). Care-seeking for NCDs among refugees and host community households was high across all conditions with 82.9 and 97.8 %, respectively, having sought care in Lebanon for their condition. Refugees utilized primary health care centers (PHCC) (57.7 %) most often while host communities sought care most in private clinics (62.4 %). Overall, 69.7 % of refugees and 82.7 % of host community members reported an out-of-pocket consultation payment (p = 0.041) with an average payment of US$15 among refugees and US$42 for the host community (p <0.001).ConclusionsGiven the protracted nature of the Syrian crisis and the burden on the Lebanese health system, implications for both individuals with NCDs and Lebanon’s health system are immense. The burden of out of pocket expenses on persons with NCDs are also substantial, especially given the tenuous economic status of many refugees and the less affluent segments of the Lebanese population. Greater investment in the public sector health system could benefit all parties. Efforts to improve quality of care for NCDs at the primary care level are also a critical component of preventing adverse outcomes and lowering the overall cost of care for NCDs.