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.

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.

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.

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.

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.

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.

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.

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.

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.

Biodegradable Dendritic Polyglycerol Sulfate for the Delivery and Tumor Accumulation of Cytostatic Anticancer Drugs.

Targeted delivery and extended blood circulation of anticancer drugs have been the challenges for decreasing the adverse side effects and improving the therapeutic efficiency in cancer chemotherapy. Herein, we present a drug delivery system (DDS) based on biodegradable dendritic polyglycerol sulfate-bearing poly(caprolactone) (dPGS-PCL) chains, which has been synthesized on 20 g scale using a straightforward two-step protocol. In vivo fluorescence imaging demonstrated a significant accumulation of the DDS in the tumor environment. Sunitinib, an anticancer drug, was loaded into the DDS and the drug-induced toxicity was investigated in vitro and in vivo. The drug encapsulated in dPGS-PCL and the free drug showed similar toxicities in A431 and HT-29 cells, and the cellular uptake was comparable. The straightforward and large-scale synthesis, the organic solvent-free drug-loading approach, together with the tumor targetability of the biodegradable dendritic polyglycerols, render this copolymer a promising candidate for targeted cancer nanomedicine drug delivery systems.

Prolonged activity of exenatide: Detailed comparison of Site-specific linear polyglycerol- and poly(ethylene glycol)-conjugates.

Exenatide is a small therapeutic peptide being currently used in clinic for the treatment of diabetes mellitus type II, however, displaying a short blood circulation time which makes two daily injections necessary. Covalent polymer modification of a protein is a well-known approach to overcome this limitation, resulting in steric shielding, an increased size and therefore a longer circulation half-life. In this study, we employed site-selective C-terminal polymer ligation of exenatide via copper-catalyzed azide-alkyne-cycloaddition (CuAAC) to yield 1:1-conjugates of either poly(ethylene glycol) (PEG) or linear polyglycerol (LPG) of different molecular weights. Our goal was to compare the impact of the two polymers on size, structure and activity of exenatide on the in vitro and in vivo level. Both polymers did not alter the secondary structure of exenatide and expectedly increased its hydrodynamic size, where the LPG-versions of exenatide showed slightly smaller values than their PEG-analogs of same molecular weight. Upon conjugation, GLP-1 receptor activation was diminished, however, still enabled maximum receptor response at slightly higher concentrations. Exenatide modified with a 40 kDa LPG (Ex-40-LPG) showed significant reduction of the blood glucose level in diabetic mice for up to 72 h, which was comparable to its PEG-analog, but 9-fold longer than native exenatide (8 h).

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.

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.

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.

A macaque connectome for large-scale network simulations in TheVirtualBrain.

Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.

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.

Inferring multi-scale neural mechanisms with brain network modelling.

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.