Implementation strategies to build mental health-care capacity in Malawi: a health-economic evaluation.

BACKGROUND: Depression is a major contributor to morbidity and mortality in sub-Saharan Africa. Due to low system capacity, three in four patients with depression in sub-Saharan Africa go untreated. Despite this, little attention has been paid to the cost-effectiveness of implementation strategies to scale up evidence-based depression treatment in the region. In this study, we investigate the cost-effectiveness of two different implementation strategies to integrate the Friendship Bench approach and measurement-based care in non-communicable disease clinics in Malawi. METHODS: The two implementation strategies tested in this study are part of a trial, in which ten clinics were randomly assigned (1:1) to a basic implementation package consisting of an internal coordinator acting as a champion (IC-only group) or to an enhanced package that complemented the basic package with quarterly external supervision, and audit and feedback of intervention delivery (IC + ES group). We included material costs, training costs, costs related to project-wide meetings, transportation and medication costs, time costs related to internal champion activities and depression screening or treatment, and costs of external supervision visits if applicable. Outcomes included the number of patients screened with the patient health questionnaire 2 (PHQ-2), cases of remitted depression at 3 and 12 months, and disability-adjusted life-years (DALYs) averted. We compared the cost-effectiveness of both packages to the status quo (ie, no intervention) using a micro-costing-informed decision-tree model. FINDINGS: Relative to the status quo, IC + ES would be on average US$10 387 ($1349-$17 365) more expensive than IC-only but more effective in achieving remission and averting DALYs. The cost per additional remission would also be lower with IC + ES than IC-only at 3 months ($119 vs $223) and 12 months ($210 for IC + ES; IC-only dominated by the status quo at 12 months). Neither package would be cost-effective under the willingness-to-pay threshold of $65 per DALY averted currently used by the Malawian Ministry of Health. However, the IC + ES package would be cost-effective in relation to the commonly used threshold of three times per-capita gross domestic product per DALY averted. INTERPRETATION: Investing in supporting champions might be an appropriate use of resources. Although not currently cost-effective by Malawian willingness-to-pay standards compared with the status quo, the IC + ES package would probably be a cost-effective way to build mental health-care capacity in resource-constrained settings in which decision makers use higher willingness-to-pay thresholds. FUNDING: National Institute of Mental Health.

Virtual quality improvement collaborative with primary care practices during COVID-19: a case study within a clinically integrated network.

INTRODUCTION: Quality improvement collaboratives (QICs) are a common approach to facilitate practice change and improve care delivery. Attention to QIC implementation processes and outcomes can inform best practices for designing and delivering collaborative content. In partnership with a clinically integrated network, we evaluated implementation outcomes for a virtual QIC with independent primary care practices delivered during COVID-19. METHODS: We conducted a longitudinal case study evaluation of a virtual QIC in which practices participated in bimonthly online meetings and monthly tailored QI coaching sessions from July 2020 to June 2021. Implementation outcomes included: (1) level of engagement (meeting attendance and poll questions), (2) QI capacity (assessments completed by QI coaches), (3) use of QI tools (plan-do-check-act (PDCA) cycles started and completed) and (4) participant perceptions of acceptability (interviews and surveys). RESULTS: Seven clinics from five primary care practices participated in the virtual QIC. Of the seven sites, five were community health centres, three were in rural counties and clinic size ranged from 1 to 7 physicians. For engagement, all practices had at least one member attend all online QIC meetings and most (9/11 (82%)) poll respondents reported meeting with their QI coach at least once per month. For QI capacity, practice-level scores showed improvements in foundational, intermediate and advanced QI work. For QI tools used, 26 PDCA cycles were initiated with 9 completed. Most (10/11 (91%)) survey respondents were satisfied with their virtual QIC experience. Twelve interviews revealed additional themes such as challenges in obtaining real-time data and working with multiple electronic medical record systems. DISCUSSION: A virtual QIC conducted with independent primary care practices during COVID-19 resulted in high participation and satisfaction. QI capacity and use of QI tools increased over 1 year. These implementation outcomes suggest that virtual QICs may be an attractive alternative to engage independent practices in QI work.

Multiscale co-simulation design pattern for neuroscience applications.

Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models.

Multiplexed Live-Cell Imaging for Drug Responses in Patient-Derived Organoid Models of Cancer.

Patient-derived organoid (PDO) models of cancer are a multifunctional research system that better recapitulates human disease as compared to cancer cell lines. PDO models can be generated by culturing patient tumor cells in extracellular basement membrane extracts (BME) and plating them as three-dimensional domes. However, commercially available reagents that have been optimized for phenotypic assays in monolayer cultures often are not compatible with BME. Herein, we describe a method to plate PDO models and assess drug effects using an automated live-cell imaging system. In addition, we apply fluorescent dyes that are compatible with kinetic measurements to quantify cell health and apoptosis simultaneously. Image capture can be customized to occur at regular time intervals over several days. Users can analyze drug effects in individual Z-plane images or a Z Projection of serial images from multiple focal planes. Using masking, specific parameters of interest are calculated, such as PDO number, area, and fluorescence intensity. We provide proof-of-concept data demonstrating the effect of cytotoxic agents on cell health, apoptosis, and viability. This automated kinetic imaging platform can be expanded to other phenotypic readouts to understand diverse therapeutic effects in PDO models of cancer.

Intimate Partner Violence and Immigration in the United States: A Systematic Review.

This systematic review sought to describe the prevalence of intimate partner violence (IPV) victimization among immigrants in the United States (U.S.) and the prevalence of IPV perpetration among immigrants in the U.S. PsycInfo, PubMed, Global Health and Scopus databases were searched for peer-reviewed literature that quantitatively examined IPV in relation to immigration. Twenty-four articles were included in the final review. Past-year IPV victimization rates among immigrants ranged from 3.8% to 46.9% and lifetime IPV victimization rates ranged from 13.9% to 93%; past-year IPV perpetration rates ranged from 3.0% to 24.8% and the one lifetime IPV perpetration rate was 12.8%. Estimates varied widely by country of origin, type of violence measured, and measure used to quantify IPV. Reliance on small convenience samples is problematic when trying to determine the true prevalence of IPV among immigrants. Epidemiological research is needed to improve the accuracy and representativeness of findings.

Improving integrated depression and non-communicable disease care in Malawi through engaged leadership and supportive implementation climate.

BACKGROUND: Low- and middle-income countries often lack access to mental health services, leading to calls for integration within other primary care systems. In sub-Saharan Africa, integration of depression treatment in non-communicable disease (NCD) settings is feasible, acceptable, and effective. However, leadership and implementation climate challenges often hinder effective integration and quality of services. The aim of this study was to identify discrete leadership strategies that facilitate overcoming barriers to the integration of depression care in NCD clinics in Malawi and to understand how clinic leadership shapes the implementation climate. METHODS: We conducted 39 in-depth interviews with the District Medical Officer, the NCD coordinator, one NCD provider, and the research assistant from each of the ten Malawian NCD clinics (note one District Medical Officer served two clinics). Based on semi-structured interview guides, participants were asked their perspectives on the impact of leadership and implementation climate on overcoming barriers to integrating depression care into existing NCD services. Thematic analysis used both inductive and deductive approaches to identify emerging themes and compare among participant type. RESULTS: The results revealed how engaged leadership can fuel a positive implementation climate where clinics had heightened capacity to overcome implementation barriers. Effective leaders were approachable and engaged in daily operations of the clinic and problem-solving. They held direct involvement with and mentorship during the intervention, providing assistance in patient screening and consultation with treatment plans. Different levels of leadership utilized their respective standings and power dynamics to influence provider attitudes and perceptions surrounding the intervention. Leaders acted by informing providers about the intervention source and educating them on the importance of mental healthcare, as it was often undervalued. Lastly, they prioritized teamwork and collective ownership for the intervention, increasing provider responsibility. CONCLUSION: Training that prioritizes leadership visibility and open communication will facilitate ongoing Malawi Ministry of Health efforts to scale up evidence-based depression treatment within NCD clinics. This proves useful where extensive and external monitoring may be limited. Ultimately, these results can inform successful strategies to close implementation gaps to achieve integration of mental health services in low-resource settings through improved leadership and implementation climate. TRIAL REGISTRATION: These findings are reported from ClinicalTrials.gov, NCT03711786. Registered on 18/10/2018. https://clinicaltrials.gov/ct2/show/NCT03711786 .

Multiplexed live-cell imaging for drug responses in patient-derived organoid models of cancer.

Patient-derived organoid (PDO) models of cancer are a multifunctional research system that better recapitulates human disease as compared to cancer cell lines. PDO models can be generated by culturing patient tumor cells in extracellular basement membrane extracts (BME) and plating as three-dimensional domes. However, commercially available reagents that have been optimized for phenotypic assays in monolayer cultures often are not compatible with BME. Herein we describe a method to plate PDO models and assess drug effects using an automated live-cell imaging system. In addition, we apply fluorescent dyes that are compatible with kinetic measurements to simultaneously quantitate cell health and apoptosis. Image capture can be customized to occur at regular time intervals over several days. Users can analyze drug effects in individual Z-plane images or a Z Projection of serial images from multiple focal planes. Using masking, specific parameters of interest are calculated, such as PDO number, area, and fluorescence intensity. We provide proof-of-concept data demonstrating the effect of cytotoxic agents on cell health, apoptosis and viability. This automated kinetic imaging platform can be expanded to other phenotypic readouts to understand diverse therapeutic effects in PDO models of cancer.

Toward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning.

To acquire statistical regularities from the world, the brain must reliably process, and learn from, spatio-temporally structured information. Although an increasing number of computational models have attempted to explain how such sequence learning may be implemented in the neural hardware, many remain limited in functionality or lack biophysical plausibility. If we are to harvest the knowledge within these models and arrive at a deeper mechanistic understanding of sequential processing in cortical circuits, it is critical that the models and their findings are accessible, reproducible, and quantitatively comparable. Here we illustrate the importance of these aspects by providing a thorough investigation of a recently proposed sequence learning model. We re-implement the modular columnar architecture and reward-based learning rule in the open-source NEST simulator, and successfully replicate the main findings of the original study. Building on these, we perform an in-depth analysis of the model's robustness to parameter settings and underlying assumptions, highlighting its strengths and weaknesses. We demonstrate a limitation of the model consisting in the hard-wiring of the sequence order in the connectivity patterns, and suggest possible solutions. Finally, we show that the core functionality of the model is retained under more biologically-plausible constraints.

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways.

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we demonstrate that dendritic N-Methyl-D-Aspartate spikes can, within physiological constraints, implement contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to dendritic branches can solve linearly nonseparable learning problems with a Hebbian, error-modulated learning rule. We also demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.

A refined information processing capacity metric allows an in-depth analysis of memory and nonlinearity trade-offs in neurocomputational systems.

Since dynamical systems are an integral part of many scientific domains and can be inherently computational, analyses that reveal in detail the functions they compute can provide the basis for far-reaching advances in various disciplines. One metric that enables such analysis is the information processing capacity. This method not only provides us with information about the complexity of a system's computations in an interpretable form, but also indicates its different processing modes with different requirements on memory and nonlinearity. In this paper, we provide a guideline for adapting the application of this metric to continuous-time systems in general and spiking neural networks in particular. We investigate ways to operate the networks deterministically to prevent the negative effects of randomness on their capacity. Finally, we present a method to remove the restriction to linearly encoded input signals. This allows the separate analysis of components within complex systems, such as areas within large brain models, without the need to adapt their naturally occurring inputs.

UV irradiation remodels the specificity landscape of transcription factors.

Somatic mutations are highly enriched at transcription factor (TF) binding sites, with the strongest trend being observed for ultraviolet light (UV)-induced mutations in melanomas. One of the main mechanisms proposed for this hypermutation pattern is the inefficient repair of UV lesions within TF-binding sites, caused by competition between TFs bound to these lesions and the DNA repair proteins that must recognize the lesions to initiate repair. However, TF binding to UV-irradiated DNA is poorly characterized, and it is unclear whether TFs maintain specificity for their DNA sites after UV exposure. We developed UV-Bind, a high-throughput approach to investigate the impact of UV irradiation on protein-DNA binding specificity. We applied UV-Bind to ten TFs from eight structural families, and found that UV lesions significantly altered the DNA-binding preferences of all the TFs tested. The main effect was a decrease in binding specificity, but the precise effects and their magnitude differ across factors. Importantly, we found that despite the overall reduction in DNA-binding specificity in the presence of UV lesions, TFs can still compete with repair proteins for lesion recognition, in a manner consistent with their specificity for UV-irradiated DNA. In addition, for a subset of TFs, we identified a surprising but reproducible effect at certain nonconsensus DNA sequences, where UV irradiation leads to a high increase in the level of TF binding. These changes in DNA-binding specificity after UV irradiation, at both consensus and nonconsensus sites, have important implications for the regulatory and mutagenic roles of TFs in the cell.

Signal denoising through topographic modularity of neural circuits.

Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout the neocortex. However, the extent to which it influences cortical processing is unclear. In this study, we combine cortical circuit modeling with network theory to demonstrate that the sharpness of topographic projections acts as a bifurcation parameter, controlling the macroscopic dynamics and representational precision across a modular network. By shifting the balance of excitation and inhibition, topographic modularity gradually increases task performance and improves the signal-to-noise ratio across the system. We demonstrate that in biologically constrained networks, such a denoising behavior is contingent on recurrent inhibition. We show that this is a robust and generic structural feature that enables a broad range of behaviorally relevant operating regimes, and provide an in-depth theoretical analysis unraveling the dynamical principles underlying the mechanism.

Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model.

The neocortex, and with it the mammalian brain, achieves a level of computational efficiency like no other existing computational engine. A deeper understanding of its building blocks (cortical microcircuits), and their underlying computational principles is thus of paramount interest. To this end, we need reproducible computational models that can be analyzed, modified, extended and quantitatively compared. In this study, we further that aim by providing a replication of a seminal cortical column model. This model consists of noisy Hodgkin-Huxley neurons connected by dynamic synapses, whose connectivity scheme is based on empirical findings from intracellular recordings. Our analysis confirms the key original finding that the specific, data-based connectivity structure enhances the computational performance compared to a variety of alternatively structured control circuits. For this comparison, we use tasks based on spike patterns and rates that require the systems not only to have simple classification capabilities, but also to retain information over time and to be able to compute nonlinear functions. Going beyond the scope of the original study, we demonstrate that this finding is independent of the complexity of the neuron model, which further strengthens the argument that it is the connectivity which is crucial. Finally, a detailed analysis of the memory capabilities of the circuits reveals a stereotypical memory profile common across all circuit variants. Notably, the circuit with laminar structure does not retain stimulus any longer than any other circuit type. We therefore conclude that the model's computational advantage lies in a sharper representation of the stimuli.

Learning and replaying spatiotemporal sequences: A replication study.

Learning and replaying spatiotemporal sequences are fundamental computations performed by the brain and specifically the neocortex. These features are critical for a wide variety of cognitive functions, including sensory perception and the execution of motor and language skills. Although several computational models demonstrate this capability, many are either hard to reconcile with biological findings or have limited functionality. To address this gap, a recent study proposed a biologically plausible model based on a spiking recurrent neural network supplemented with read-out neurons. After learning, the recurrent network develops precise switching dynamics by successively activating and deactivating small groups of neurons. The read-out neurons are trained to respond to particular groups and can thereby reproduce the learned sequence. For the model to serve as the basis for further research, it is important to determine its replicability. In this Brief Report, we give a detailed description of the model and identify missing details, inconsistencies or errors in or between the original paper and its reference implementation. We re-implement the full model in the neural simulator NEST in conjunction with the NESTML modeling language and confirm the main findings of the original work.

Brain signal predictions from multi-scale networks using a linearized framework.

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 106 neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials ('spikes') or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin. Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for. The work also constitutes a major advance in computational efficiency of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.

A System-on-Chip Based Hybrid Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks.

Despite the great strides neuroscience has made in recent decades, the underlying principles of brain function remain largely unknown. Advancing the field strongly depends on the ability to study large-scale neural networks and perform complex simulations. In this context, simulations in hyper-real-time are of high interest, as they would enable both comprehensive parameter scans and the study of slow processes, such as learning and long-term memory. Not even the fastest supercomputer available today is able to meet the challenge of accurate and reproducible simulation with hyper-real acceleration. The development of novel neuromorphic computer architectures holds out promise, but the high costs and long development cycles for application-specific hardware solutions makes it difficult to keep pace with the rapid developments in neuroscience. However, advances in System-on-Chip (SoC) device technology and tools are now providing interesting new design possibilities for application-specific implementations. Here, we present a novel hybrid software-hardware architecture approach for a neuromorphic compute node intended to work in a multi-node cluster configuration. The node design builds on the Xilinx Zynq-7000 SoC device architecture that combines a powerful programmable logic gate array (FPGA) and a dual-core ARM Cortex-A9 processor extension on a single chip. Our proposed architecture makes use of both and takes advantage of their tight coupling. We show that available SoC device technology can be used to build smaller neuromorphic computing clusters that enable hyper-real-time simulation of networks consisting of tens of thousands of neurons, and are thus capable of meeting the high demands for modeling and simulation in neuroscience.

Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure.

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.

Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.

Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. In this work, we present L2L as an easy to use and flexible framework to perform parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper, we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor, TVB, OpenAIGym, and NetLogo.

RateML: A Code Generation Tool for Brain Network Models.

Whole brain network models are now an established tool in scientific and clinical research, however their use in a larger workflow still adds significant informatics complexity. We propose a tool, RateML, that enables users to generate such models from a succinct declarative description, in which the mathematics of the model are described without specifying how their simulation should be implemented. RateML builds on NeuroML's Low Entropy Model Specification (LEMS), an XML based language for specifying models of dynamical systems, allowing descriptions of neural mass and discretized neural field models, as implemented by the Virtual Brain (TVB) simulator: the end user describes their model's mathematics once and generates and runs code for different languages, targeting both CPUs for fast single simulations and GPUs for parallel ensemble simulations. High performance parallel simulations are crucial for tuning many parameters of a model to empirical data such as functional magnetic resonance imaging (fMRI), with reasonable execution times on small or modest hardware resources. Specifically, while RateML can generate Python model code, it enables generation of Compute Unified Device Architecture C++ code for NVIDIA GPUs. When a CUDA implementation of a model is generated, a tailored model driver class is produced, enabling the user to tweak the driver by hand and perform the parameter sweep. The model and driver can be executed on any compute capable NVIDIA GPU with a high degree of parallelization, either locally or in a compute cluster environment. The results reported in this manuscript show that with the CUDA code generated by RateML, it is possible to explore thousands of parameter combinations with a single Graphics Processing Unit for different models, substantially reducing parameter exploration times and resource usage for the brain network models, in turn accelerating the research workflow itself. This provides a new tool to create efficient and broader parameter fitting workflows, support studies on larger cohorts, and derive more robust and statistically relevant conclusions about brain dynamics.

DigiKnowIt News: Educating youth about pediatric clinical trials using an interactive, multimedia educational website.

Pediatric clinical trials allow for the testing of appropriate and effective treatments for children. However, some challenges exist with recruitment. This study examined the effectiveness of DigiKnowIt News, an interactive, multimedia website (which includes activities, videos, and comic books) designed to educate children about clinical trials. A randomized controlled trial was conducted in 2018 with 91 participants (M age = 10.92 years; SD = 2.06). Participants were randomly assigned to intervention or wait-list control groups and completed questionnaires at pretest and posttest (1 week later) about their knowledge, attitudes, beliefs about clinical trials, and self-efficacy for participating in clinical trials. Participants in the intervention group received access to DigiKnowIt News between pretest and posttest and completed a satisfaction questionnaire at posttest. At the end of the study, participants in the wait-list control group were offered the option to use the website and complete a satisfaction questionnaire. At posttest, participants in the intervention group, compared to participants in the wait-list control group, had more knowledge about clinical trials and more reported confidence for participating in clinical trials. Participants reported high levels of satisfaction with DigiKnowIt News. The findings suggest that an educational website can improve factors related to increasing rates of participation in clinical trials.