Dynamic primitives of brain network interaction.

What dynamic processes underly functional brain networks? Functional connectivity (FC) and functional connectivity dynamics (FCD) are used to represent the patterns and dynamics of functional brain networks. FC(D) is related to the synchrony of brain activity: when brain areas oscillate in a coordinated manner this yields a high correlation between their signal time series. To explain the processes underlying FC(D) we review how synchronized oscillations emerge from coupled neural populations in brain network models (BNMs). From detailed spiking networks to more abstract population models, there is strong support for the idea that the brain operates near critical instabilities that give rise to multistable or metastable dynamics that in turn lead to the intermittently synchronized slow oscillations underlying FC(D). We explore further consequences from these fundamental mechanisms and how they fit with reality. We conclude by highlighting the need for integrative brain models that connect separate mechanisms across levels of description and spatiotemporal scales and link them with cognitive function.

Brain simulation as a cloud service: The Virtual Brain on EBRAINS.

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation.

Brain Network Simulations Indicate Effects of Neuregulin-1 Genotype on Excitation-Inhibition Balance in Cortical Dynamics.

Neuregulin-1 (NRG1) represents an important factor for multiple processes including neurodevelopment, brain functioning or cognitive functions. Evidence from animal research suggests an effect of NRG1 on the excitation-inhibition (E/I) balance in cortical circuits. However, direct evidence for the importance of NRG1 in E/I balance in humans is still lacking. In this work, we demonstrate the application of computational, biophysical network models to advance our understanding of the interaction between cortical activity observed in neuroimaging and the underlying neurobiology. We employed a biophysical neuronal model to simulate large-scale brain dynamics and to investigate the role of polymorphisms in the NRG1 gene (rs35753505, rs3924999) in n = 96 healthy adults. Our results show that G/G-carriers (rs3924999) exhibit a significant difference in global coupling (P = 0.048) and multiple parameters determining E/I-balance such as excitatory synaptic coupling (P = 0.047), local excitatory recurrence (P = 0.032) and inhibitory synaptic coupling (P = 0.028). This indicates that NRG1 may be related to excitatory recurrence or excitatory synaptic coupling potentially resulting in altered E/I-balance. Moreover, we suggest that computational modeling is a suitable tool to investigate specific biological mechanisms in health and disease.

Chemical Approaches to Synthetic Drug Delivery Systems for Systemic Applications.

Poor water solubility and low bioavailability of active pharmaceutical ingredients (APIs) are major causes of friction in the pharmaceutical industry and represent a formidable hurdle for pharmaceutical drug development. Drug delivery remains the major challenge for the application of new small-molecule drugs as well as biopharmaceuticals. The three challenges for synthetic delivery systems are: (i) controlling drug distribution and clearance in the blood; (ii) solubilizing poorly water-soluble agents, and (iii) selectively targeting specific tissues. Although several polymer-based systems have addressed the first two demands and have been translated into clinical practice, no targeted synthetic drug delivery system has reached the market. This Review is designed to provide a background on the challenges and requirements for the design and translation of new polymer-based delivery systems. This report will focus on chemical approaches to drug delivery for systemic applications.

Polyglycerol for Half-Life Extension of Proteins-Alternative to PEGylation?

Since several decades, PEGylation is known to be the clinical standard to enhance pharmacokinetics of biotherapeutics. In this study, we introduce polyglycerol (PG) of different lengths and architectures (linear and hyperbranched) as an alternative polymer platform to poly(ethylene glycol) (PEG) for half-life extension (HLE). We designed site-selective N-terminally modified PG-protein conjugates of the therapeutic protein anakinra (IL-1ra, Kineret) and compared them systematically with PEG analogues of similar molecular weights. Linear PG and PEG conjugates showed comparable hydrodynamic sizes and retained their secondary structure, whereas binding affinity to IL-1 receptor 1 decreased with increasing polymer length, yet remained in the low nanomolar range for all conjugates. The terminal half-life of a 40 kDa linear PG-modified anakinra was extended 4-fold compared to the unmodified protein, close to its PEG analogue. Our results demonstrate similar performances of PEG- and PG-anakinra conjugates and therefore highlight the outstanding potential of polyglycerol as a PEG alternative for half-life extension of biotherapeutics.

Toolbox of Biodegradable Dendritic (Poly glycerol sulfate)-SS-poly(ester) Micelles for Cancer Treatment: Stability, Drug Release, and Tumor Targeting.

In this paper, we present well-defined dPGS-SS-PCL/PLGA/PLA micellar systems demonstrating excellent capabilities as a drug delivery platform in light of high stability and precise in vitro and in vivo drug release combined with active targetability to tumors. These six amphiphilic block copolymers were each targeted in two different molecular weights (8 or 16 kDa) and characterized using 1H NMR, gel permeation chromatography (GPC), and elemental analysis. The block copolymer micelles showed monodispersed size distributions of 81-187 nm, strong negative charges between -52 and -41 mV, and low critical micelle concentrations (CMCs) of up to 1.13-3.58 mg/L (134-527 nM). The serum stability was determined as 94% after 24 h. The drug-loading efficiency for Sunitinib ranges from 38 to 83% (8-17 wt %). The release was selectively triggered by glutathione (GSH) and lipase, reaching 85% after 5 days, while only 20% leaching was observed under physiological conditions. Both the in vitro and in vivo studies showed sustained release of Sunitinib over 1 week. CCK-8 assays on HeLa lines demonstrated the high cell compatibility (1 mg/mL, 94% cell viability, 48 h) and the high cancer cell toxicity of Sunitinib-loaded micelles (IC50 2.5 µg/mL). By in vivo fluorescence imaging studies on HT-29 tumor-bearing mice, the targetability of dPGS7.8-SS-PCL7.8 enabled substantial accumulation in tumor tissue compared to nonsulfated dPG3.9-SS-PCL7.8. As a proof of concept, Sunitinib-loaded dPGS-SS-poly(ester) micelles improved the antitumor efficacy of the chemotherapeutic. A tenfold lower dosage of loaded Sunitinib led to an even higher tumor growth inhibition compared to the free drug, as demonstrated in a HeLa human cervical tumor-bearing mice model. No toxicity for the organism was observed, confirming the good biocompatibility of the system.

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.

How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?

Recent efforts to model human brain activity on the scale of the whole brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale brain models. It is still unclear on the one hand, which scale of representation of white matter fibers is optimal to describe brain activity on a large-scale such as recorded with magneto- or electroencephalography (M/EEG) or functional magnetic resonance imaging (fMRI), and on the other hand, to which extent short-range connections that are typically local should be taken into account. In this article we quantified the effect of connectivity upon large-scale brain network dynamics by (i) systematically varying the number of brain regions before computing the connectivity matrix, and by (ii) adding generic short-range connections. We used dMRI data from the Human Connectome Project. We developed a suite of preprocessing modules called SCRIPTS to prepare these imaging data for The Virtual Brain, a neuroinformatics platform for large-scale brain modeling and simulations. We performed simulations under different connectivity conditions and quantified the spatiotemporal dynamics in terms of Shannon Entropy, dwell time and Principal Component Analysis. For the reconstructed connectivity, our results show that the major white matter fiber bundles play an important role in shaping slow dynamics in large-scale brain networks (e.g. in fMRI). Faster dynamics such as gamma oscillations (around 40 Hz) are sensitive to the short-range connectivity if transmission delays are considered.

Structural architecture supports functional organization in the human aging brain at a regionwise and network level.

Functional interactions in the brain are constrained by the underlying anatomical architecture, and structural and functional networks share network features such as modularity. Accordingly, age-related changes of structural connectivity (SC) may be paralleled by changes in functional connectivity (FC). We provide a detailed qualitative and quantitative characterization of the SC-FC coupling in human aging as inferred from resting-state blood oxygen-level dependent functional magnetic resonance imaging and diffusion-weighted imaging in a sample of 47 adults with an age range of 18-82. We revealed that SC and FC decrease with age across most parts of the brain and there is a distinct age-dependency of regionwise SC-FC coupling and network-level SC-FC relations. A specific pattern of SC-FC coupling predicts age more reliably than does regionwise SC or FC alone (r = 0.73, 95% CI = [0.7093, 0.8522]). Hence, our data propose that regionwise SC-FC coupling can be used to characterize brain changes in aging. Hum Brain Mapp 37:2645-2661, 2016. © 2016 Wiley Periodicals, Inc.

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data.

Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface.

Human connectome topology directs cortical traveling waves and shapes frequency gradients.

Traveling waves and neural oscillation frequency gradients are pervasive in the human cortex. While the direction of traveling waves has been linked to brain function and dysfunction, the factors that determine this direction remain elusive. We hypothesized that structural connectivity instrength gradients - defined as the gradually varying sum of incoming connection strengths across the cortex - could shape both traveling wave direction and frequency gradients. We confirm the presence of instrength gradients in the human connectome across diverse cohorts and parcellations. Using a cortical network model, we demonstrate how these instrength gradients direct traveling waves and shape frequency gradients. Our model fits resting-state MEG functional connectivity best in a regime where instrength-directed traveling waves and frequency gradients emerge. We further show how structural subnetworks of the human connectome generate opposing wave directions and frequency gradients observed in the alpha and beta bands. Our findings suggest that structural connectivity instrength gradients affect both traveling wave direction and frequency gradients.

Topical Delivery of Tofacitinib in Dermatology: The Promise of a Novel Therapeutic Class Using Biodegradable Dendritic Polyglycerol Sulfates.

Inflammatory skin diseases, such as psoriasis, atopic dermatitis, and alopecia areata, occur when the regulatory tolerance of the innate immune system is disrupted, resulting in the activation of the Janus kinase-signal transducer and activator of transcription (JAK-STAT) inflammatory signaling pathway by interleukin 6 (IL-6) and other key inflammatory cytokines. JAK inhibitors, such as tofacitinib, bind to these enzymes which are coupled to receptors on cell surfaces and block the transcription of inflammatory cytokine-induced genes. The first topical applications are being marketed, yet insufficient effects regarding indications, such as alopecia areata, suggest that improved delivery technologies could help increase the efficacy. In this study, we used sulfated dendritic polyglycerol with caprolactone segments integrated in its backbone (dPGS-PCL), with a molecular weight of 54 kDa, as a degradable carrier to load and solubilize the hydrophobic drug tofacitinib (TFB). TFB loaded in dPGS-PCL (dPGS-PCL@TFB), at a 11 w/w% loading capacity in aqueous solution, showed in an ex-vivo human skin model better penetration than free TFB in a 30:70 (v/v) ethanol/water mixture. We also investigated the anti-inflammatory efficacy of dPGS-PCL@TFB (0.5 w/w%), dPGS-PCL, and free TFB in the water/ethanol mixture by measuring their effects on IL-6 and IL-8 release, and STAT3 and STAT5 activation in ex vivo skin models of simulated inflamed human skin. Our results suggest that dPGS-PCL@TFB reduces the activation of STAT3 and STAT5 by increasing the penetration of the tofacitinib. However, no statistically significant differences with respect to the inhibition of IL-6 and IL-8 were observed in this short incubation time.

Dendritic polyglycerol sulfates as multivalent inhibitors of inflammation.

Adhesive interactions of leukocytes and endothelial cells initiate leukocyte migration to inflamed tissue and are important for immune surveillance. Acute and chronic inflammatory diseases show a dysregulated immune response and result in a massive efflux of leukocytes that contributes to further tissue damage. Therefore, targeting leukocyte trafficking may provide a potent form of anti-inflammatory therapy. Leukocyte migration is initiated by interactions of the cell adhesion molecules E-, L-, and P-selectin and their corresponding carbohydrate ligands. Compounds that efficiently address these interactions are therefore of high therapeutic interest. Based on this rationale we investigated synthetic dendritic polyglycerol sulfates (dPGS) as macromolecular inhibitors that operate via a multivalent binding mechanism mimicking naturally occurring ligands. dPGS inhibited both leukocytic L-selectin and endothelial P-selectin with high efficacy. Size and degree of sulfation of the polymer core determined selectin binding affinity. Administration of dPGS in a contact dermatitis mouse model dampened leukocyte extravasation as effectively as glucocorticoids did and edema formation was significantly reduced. In addition, dPGS interacted with the complement factors C3 and C5 as was shown in vitro and reduced C5a levels in a mouse model of complement activation. Thus, dPGS represent an innovative class of a fully synthetic polymer therapeutics that may be used for the treatment of inflammatory diseases.

Learning how network structure shapes decision-making for bio-inspired computing.

To better understand how network structure shapes intelligent behavior, we developed a learning algorithm that we used to build personalized brain network models for 650 Human Connectome Project participants. We found that participants with higher intelligence scores took more time to solve difficult problems, and that slower solvers had higher average functional connectivity. With simulations we identified a mechanistic link between functional connectivity, intelligence, processing speed and brain synchrony for trading accuracy with speed in dependence of excitation-inhibition balance. Reduced synchrony led decision-making circuits to quickly jump to conclusions, while higher synchrony allowed for better integration of evidence and more robust working memory. Strict tests were applied to ensure reproducibility and generality of the obtained results. Here, we identify links between brain structure and function that enable to learn connectome topology from noninvasive recordings and map it to inter-individual differences in behavior, suggesting broad utility for research and clinical applications.

Dendritic polyglycerolsulfate-SS-poly(ester amide) micelles for the systemic delivery of docetaxel: pushing the limits of stability through the insertion of π-π interactions.

Insufficient stability of micellar drug delivery systems is still the major limitation to their systematic application in chemotherapy. This work demonstrates novel π-electron stabilized polyelectrolyte block copolymer micelles based on dendritic polyglycerolsulfate-cystamine-block-poly(4-benzoyl-1,4-oxazepan-7-one)-pyrene (dPGS-SS-POxPPh-Py) presenting a very low critical micelle concentration (CMC) of 0.3 mg L-1 (18 nM), 55-fold lower than that of conventional amphiphilic block copolymer micelles. The drug loading capacities of up to 13 wt% allow the efficient encapsulation of the chemotherapeutic Docetaxel (DTX). The spherical morphology of the micelles was proven by cryogenic electron microscopy (cryo-EM). Gaussian Analysis revealed well-defined sizes of 57 nm and 80 nm in the unloaded/loaded state, respectively. Experiments by dynamic light scattering (DLS), ultraviolet-visible spectroscopy (UV-VIS), fluorescence spectroscopy, and cross-polarization solid-state 13C NMR studied the π-π interactions between the core-forming block segment of dPGS-SS-POxPPh-Py and DTX. The findings point to a substantial contribution of these noncovalent interactions to the system's high stability. By confocal laser scanning microscopy (CLSM), the cellular uptake of fluorescein-labelled FITC-dPGS-SS-POxPPh-Py micelles was monitored after one day displaying the successful cell insertion of the cargo-loaded systems. To ensure the drug release in cancerous cells, the disassembly of the micellar DTX-formulations was achieved by reductive and enzymatic degradation studied by light scattering and GPC experiments. Further, no size increase nor disassembly in the presence of human serum proteins after four days was detected. The precise in vitro drug release was also given by the high potency of inhibiting cancer cell growth, finding half-maximal inhibitory concentrations (IC50) efficiently reduced to 68 nM coming along with high viabilities of the empty polymer materials tested on tumor-derived HeLa, A549, and McF-7 cell lines after two days. This study highlights the substantial potential of micelles tailored through the combination of π-electron stabilization with dendritic polyglycerolsulfate for targeted drug delivery systems, enabling them to have a significant foothold in the clinical treatment of cancer.

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias.

Introduction: Neural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. The Virtual Brain (TVB) modeling, by exploiting structural and functional magnetic resonance imaging (MRI), yields mesoscopic parameters of connectivity and synaptic transmission. Methods: We used TVB to simulate brain networks, which are key for human brain function, in Alzheimer's disease (AD) and frontotemporal dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks. Results: The pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles. Discussion: These TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

Inflammation assessment in patients with arthritis using a novel in vivo fluorescence optical imaging technology.

BACKGROUND: Indocyanine green (ICG)-enhanced fluorescence optical imaging (FOI) is an established technology for imaging of inflammation in animal models. In experimental models of arthritis, FOI findings corresponded to histologically proven synovitis. This is the first comparative study of FOI with other imaging modalities in humans with arthritis. METHODS: 252 FOI examinations (Xiralite system, mivenion GmbH, Berlin, Germany; ICG bolus of 0.1 mg/kg/body weight, sequence of 360 images, one image per second) were compared with clinical examination (CE), ultrasonography (US) and MRI of patients with arthritis of the hands. RESULTS: In an FOI sequence, three phases could be distinguished (P1-P3). With MRI as reference, FOI had a sensitivity of 76% and a specificity of 54%, while the specificity of phase 1 was 94%. FOI had agreement rates up to 88% versus CE, 64% versus greyscale US, 88% versus power Doppler US and 83% versus MRI, depending on the compared phase and parameter. FOI showed a higher rate of positive results compared to CE, US and MRI. In individual patients, FOI correlated significantly (p<0.05) with disease activity (Disease Activity Score 28, r=0.41), US (r=0.40) and RAMRIS (Rheumatoid Arthritis MRI Score) (r=0.56). FOI was normal in 97.8% of joints of controls. CONCLUSION: ICG-enhanced FOI is a new technology offering sensitive imaging detection of inflammatory changes in subjects with arthritis. FOI was more sensitive than CE and had good agreement with CE, US in power Doppler mode and MRI, while showing more positive results than these. An adequate interpretation of an FOI sequence requires a separate evaluation of all phases. For the detection of synovitis and tenosynovitis, FOI appears to be as informative as 1.5 T MRI and US.

Personalized Connectome-Based Modeling in Patients with Semi-Acute Phase TBI: Relationship to Acute Neuroimaging and 6 Month Follow-Up.

Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the microscale, a neurometabolic cascade alters neurotransmission, while on the macroscale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of 44 adult TBI patients recruited from a regional trauma center, scanned at 1-2 weeks postinjury, and with follow-up behavioral outcome assessed 6 months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain, we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semiacute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6 month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory connection strengths even when traumatic intracranial lesions were not present. Our results suggest that large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes.

Design of therapeutic biomaterials to control inflammation.

Inflammation plays an important role in the response to danger signals arising from damage to our body and in restoring homeostasis. Dysregulated inflammatory responses occur in many diseases, including cancer, sepsis and autoimmunity. The efficacy of anti-inflammatory drugs, developed for the treatment of dysregulated inflammation, can be potentiated using biomaterials, by improving the bioavailability of drugs and by reducing side effects. In this Review, we first outline key elements and stages of the inflammatory environment and then discuss the design of biomaterials for different anti-inflammatory therapeutic strategies. Biomaterials can be engineered to scavenge danger signals, such as reactive oxygen and nitrogen species and cell-free DNA, in the early stages of inflammation. Materials can also be designed to prevent adhesive interactions of leukocytes and endothelial cells that initiate inflammatory responses. Furthermore, nanoscale platforms can deliver anti-inflammatory agents to inflammation sites. We conclude by discussing the challenges and opportunities for biomaterial innovations in addressing inflammation.

Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling.

We present a dataset of magnetic resonance imaging (MRI) data (T1, diffusion, BOLD) acquired in 25 brain tumor patients before the tumor resection surgery, and six months after the surgery, together with the tumor masks, and in 11 controls (recruited among the patients' caregivers). The dataset also contains behavioral and emotional scores obtained with standardized questionnaires. To simulate personalized computational models of the brain, we also provide structural connectivity matrices, necessary to perform whole-brain modelling with tools such as The Virtual Brain. In addition, we provide blood-oxygen-level-dependent imaging time series averaged across regions of interest for comparison with simulation results. An average resting state hemodynamic response function for each region of interest, as well as shape maps for each voxel, are also contributed.