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

Isotropic non-white matter partial volume effects in constrained spherical deconvolution

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DVV signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM G M interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm(2), reasonable SNR (similar to 30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD

Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers Using Constrained Spherical Deconvolution

This research was funded by Fonds Wetenschappelijk Onderzoek (FWO) with grant numbers G090020N, G094320N and 1S46122N. Ben Jeurissen is supported by BELSPO/Prodex and ESA Grant ISLRA 2009-1062

The effect of spaceflight and microgravity on the human brain

peer reviewedMicrogravity, confinement, isolation, and immobilization are just some of the features astronauts have to cope with during space missions. Consequently, long-duration space travel can have detrimental effects on human physiology. Although research has focused on the cardiovascular and musculoskeletal system in particular, the exact impact of spaceflight on the human central nervous system remains to be determined. Previous studies have reported psychological problems, cephalic fluid shifts, neurovestibular problems, and cognitive alterations, but there is paucity in the knowledge of the underlying neural substrates. Previous space analogue studies and preliminary spaceflight studies have shown an involvement of the cerebellum, cortical sensorimotor, and somatosensory areas and the vestibular pathways. Extending this knowledge is crucial, especially in view of long-duration interplanetary missions (e.g., Mars missions) and space tourism. In addition, the acquired insight could be relevant for vestibular patients, patients with neurodegenerative disorders, as well as the elderly population, coping with multisensory deficit syndromes, immobilization, and inactivity

Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom.

International audienceAs it provides the only method for mapping white matter fibers in vivo, diffusion MRI tractography is gaining importance in clinical and neuroscience research. However, despite the increasing availability of different diffusion models and tractography algorithms, it remains unclear how to select the optimal fiber reconstruction method, given certain imaging parameters. Consequently, it is of utmost importance to have a quantitative comparison of these models and algorithms and a deeper understanding of the corresponding strengths and weaknesses. In this work, we use a common dataset with known ground truth and a reproducible methodology to quantitatively evaluate the performance of various diffusion models and tractography algorithms. To examine a wide range of methods, the dataset, but not the ground truth, was released to the public for evaluation in a contest, the "Fiber Cup". 10 fiber reconstruction methods were evaluated. The results provide evidence that: 1. For high SNR datasets, diffusion models such as (fiber) orientation distribution functions correctly model the underlying fiber distribution and can be used in conjunction with streamline tractography, and 2. For medium or low SNR datasets, a prior on the spatial smoothness of either the diffusion model or the fibers is recommended for correct modelling of the fiber distribution and proper tractography results. The phantom dataset, the ground truth fibers, the evaluation methodology and the results obtained so far will remain publicly available on: http://www.lnao.fr/spip.php?rubrique79 to serve as a comparison basis for existing or new tractography methods. New results can be submitted to [email protected] and updates will be published on the webpage

Isabelle Modelchecking for insider threats

The Isabelle Insider framework formalises the technique of social explanation for modeling and analysing Insider threats in infrastructures including physical and logical aspects. However, the abstract Isabelle models need some refinement to provide sufficient detail to explore attacks constructively and understand how the attacker proceeds. The introduction of mutable states into the model leads us to use the concepts of Modelchecking within Isabelle. Isabelle can simply accommodate classical CTL type Modelchecking. We integrate CTL Modelchecking into the Isabelle Insider framework. A running example of an IoT attack on privacy motivates the method throughout and illustrates how the enhanced framework fully supports realistic modeling and analysis of IoT Insiders

A novel method for realistic DWI data generation

Diffusion Weighted Imaging (DWI) was introduced to explore the human connectome in vivo; although many fiber tractography (FT) algorithms exist, proving the effectiveness of their estimates is challenging. We present a biologically and physically realistic software phantom, with brain-like fibres configuration and images, fully tuneable in terms of ‘simulated acquisition’ parameters: a realistic bench test for quantitative analyses of every DWI-related algorith

Modeling brain dynamics after tumor resection using The Virtual Brain

Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first "virtual neurosurgery", mimicking patient's actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome. We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation

Improved diffusion parameter estimation by incorporating T-2 relaxation properties into the DKI-FWE model

This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 764513. Research was partially performed as part of the Center of Advanced Imaging Innovation and Research (CAI2R, www.cai2r.net) , an NIBIB Biomedical Technology Resource Center (NIH P41 EB017183) and was partially supported by the NINDS (R01 NS088040) and NIBIB (R01 EB027075) of the NIH. The work was also supported by the Research Foundation Flanders (FWO Bel-gium) through project funding (G084217N and 12M3119N) . The authors gratefully acknowledge support of the European Space Agency and BELSPO Prodex (BrainDTI)