Postdoctoral Fellow, University of Sydney/Complex Systems Group
Large scale simulations of brain dynamics can help us treat brain disorders
We are driven by our brains and how they interact with the world. When brains malfunction it normally leads to behaviour alteration that can affect various aspects of our lives including personal relationships, learning and self-sufficiency. There are certain disorders like epilepsy and insomnia that are not fully understood and remain subject of intense multidisciplinary research worldwide. Moreover, the treatment of these disorders is either invasive or done by trial and error.
Have you ever heard of flight simulators? Pilots use them to learn how to fly an aircraft. What I do is very similar, only that instead of flying, I practice how to treat brains that misbehave. My research focuses on modelling and simulation of brain activity using the shape and anatomy of real brains. The advantage of incorporating realistic shape in the model is that I can perform an unlimited number of 'virtual experiments' and predict the outcome of different treatments without actually performing the procedure in real life.
The software I contributed developing can be used to assist in the refinement of current classification of brain disorders; and, in the not-too-distant future, it may become a computer assistant that will provide subject-specific guidance to clinical staff during invasive treatments of brain disorders.
Abstract: An exciting advance in the field of neuroimaging is the acquisition and processing of very large data sets (so called 'big data'), permitting large-scale inferences that foster a greater understanding of brain function in health and disease. Yet what we are clearly lacking are quantitative integrative tools to translate this understanding to the individual level to lay the basis for personalized medicine.Here we address this challenge through a review on how the relatively new field of neuroinformatics modeling has the capacity to track brain network function at different levels of inquiry, from microscopic to macroscopic and from the localized to the distributed. In this context, we introduce a new and unique multiscale approach, The Virtual Brain (TVB), that effectively models individualized brain activity, linking large-scale (macroscopic) brain dynamics with biophysical parameters at the microscopic level. We also show how TVB modeling provides unique biological interpretable data in epilepsy and stroke.These results establish the basis for a deliberate integration of computational biology and neuroscience into clinical approaches for elucidating cellular mechanisms of disease. In the future, this can provide the means to create a collection of disease-specific models that can be applied on the individual level to personalize therapeutic interventions. VIDEO ABSTRACT.
Pub.: 26 May '16, Pinned: 29 Sep '17
Abstract: Modern systems neuroscience increasingly leans on large-scale multi-lab neuroinformatics initiatives to provide necessary capacity for biologically realistic modeling of primate whole-brain activity. Here, we present a framework to assemble primate brain's biologically plausible anatomical backbone for such modeling initiatives. In this framework, structural connectivity is determined by adding complementary information from invasive macaque axonal tract tracing and non-invasive human diffusion tensor imaging. Both modalities are combined by means of available interspecies registration tools and a newly developed Bayesian probabilistic modeling approach to extract common connectivity evidence. We demonstrate how this novel framework is embedded in the whole-brain simulation platform called The Virtual Brain (TVB). Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
Pub.: 06 Jan '17, Pinned: 29 Sep '17
Abstract: We describe five patients with frontal lobe epilepsy who underwent electrocortical stimulation (ES) for language localization and language functional magnetic resonance imaging (fMRI) prior to epilepsy surgery. Six months after surgery, three patients suffered from a drop of verbal fluency. In all of them, frontal areas with presurgical language fMRI activity were resected. Our results suggest that resection in regions of areas with presurgical fMRI activation is not without risk for a postsurgical loss of function, even when ES results were negative for language function in these areas. Using fMRI activations might be specifically helpful to plan the resection when ES delivered inconclusive results.
Pub.: 28 Sep '17, Pinned: 29 Sep '17
Abstract: Neural Field models (NFM) play an important role in the understanding of neural population dynamics on a mesoscopic spatial and temporal scale. Their numerical simulation is an essential element in the analysis of their spatio-temporal dynamics. The simulation tool described in this work considers scalar spatially homogeneous neural fields taking into account a finite axonal transmission speed and synaptic temporal derivatives of first and second order. A text-based interface offers complete control of field parameters and several approaches are used to accelerate simulations. A graphical output utilizes video hardware acceleration to display running output with reduced computational hindrance compared to simulators that are exclusively software-based. Diverse applications of the tool demonstrate breather oscillations, static and dynamic Turing patterns and activity spreading with finite propagation speed. The simulator is open source to allow tailoring of code and this is presented with an extension use case.
Pub.: 06 Nov '15, Pinned: 29 Sep '17
Abstract: 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.
Pub.: 20 Jun '13, Pinned: 29 Sep '17
Abstract: In this article, we describe the mathematical framework of the computational model at the core of the tool The Virtual Brain (TVB), designed to simulate collective whole brain dynamics by virtualizing brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. Here, a consistent notation for the generalized BNM is given, so that in this form the equations represent a direct link between the mathematical description of BNMs and the components of the numerical implementation in TVB. Finally, we made a summary of the forward models implemented for mapping simulated neural activity (EEG, MEG, sterotactic electroencephalogram (sEEG), fMRI), identifying their advantages and limitations.
Pub.: 17 Jan '15, Pinned: 29 Sep '17