Investigating Communication Dynamics in Neuronal Network using 3D Gold Microelectrode Arrays.

Although in vitro neuronal network models hold great potential for advancing neuroscience research, with the capacity to provide fundamental insights into mechanisms underlying neuronal functions, the dynamics of cell communication within such networks remain poorly understood. Here, we develop a customizable, polymer modified three-dimensional gold microelectrode array with sufficient stability for high signal-to-noise, long-term, neuronal recording of cultured networks. By using directed spatial and temporal patterns of electrical stimulation of cells to explore synaptic-based communication, we monitored cell network dynamics over 3 weeks, quantifying communication capability using correlation heatmaps and mutual information networks. Analysis of synaptic delay and signal speed between cells enabled us to establish a communication connectivity model. We anticipate that our discoveries of the dynamic changes in communication across the neuronal network will provide a valuable tool for future studies in understanding health and disease as well as in developing effective platforms for evaluating therapies.

Structural-functional brain network coupling predicts human cognitive ability.

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

Whole-brain structural connectome asymmetry in autism.

Autism spectrum disorder is a common neurodevelopmental condition that manifests as a disruption in sensory and social skills. Although it has been shown that the brain morphology of individuals with autism is asymmetric, how this differentially affects the structural connectome organization of each hemisphere remains under-investigated. We studied whole-brain structural connectivity-based brain asymmetry in individuals with autism using diffusion magnetic resonance imaging obtained from the Autism Brain Imaging Data Exchange initiative. By leveraging dimensionality reduction techniques, we constructed low-dimensional representations of structural connectivity and calculated their asymmetry index. Comparing the asymmetry index between individuals with autism and neurotypical controls, we found atypical structural connectome asymmetry in the sensory and default-mode regions, particularly showing weaker asymmetry towards the right hemisphere in autism. Network communication provided topological underpinnings by demonstrating that the inferior temporal cortex and limbic and frontoparietal regions showed reduced global network communication efficiency and decreased send-receive network navigation in the inferior temporal and lateral visual cortices in individuals with autism. Finally, supervised machine learning revealed that structural connectome asymmetry could be used as a measure for predicting communication-related autistic symptoms and nonverbal intelligence. Our findings provide insights into macroscale structural connectome alterations in autism and their topological underpinnings.

State-of-the-art survey of in-vehicle protocols and automotive Ethernet security and vulnerabilities.

With the help of advanced technology, the automotive industry is in continuous evolution. Modern vehicles are not only comprised of mechanical components but also contain highly complex electronic devices and connections to the outside world. Today's vehicle usually has between 30 and 70 ECUs (Electronic Control Units), which communicate with each other over standard communication protocols. There are different types of in-vehicle network protocols and bus systems, including the Controlled Area Network (CAN), Local Interconnected Network (LIN), FlexRay, Media Oriented System Transport (MOST), and Automotive Ethernet (AE). Modern cars are also able to communicate with other devices through wired or wireless interfaces such as USB, Bluetooth, Wi-Fi or even 5G. Such interfaces may expose the internal network to the outside world and can be seen as entry points for cyber-attacks. In this paper, the main interest is in the AE network protocol. AE is a special Ethernet design that provides the bandwidth needed for today's applications, and the potential for even greater performance in the future. However, AE is a "best effort" protocol, which cannot be considered reliable. This implies that it is not trustworthy in terms of reliability and timely deliveries. The focus of this paper is to present a state-of-the-art survey of security threats and protection mechanisms relating to AE. After introducing and comparing the different protocols being used in the embedded networks of current vehicles, we analyze the potential threats targeting the AE network and describe how attackers' opportunities can be enhanced by the new communication abilities of modern cars. Finally, we present and compare the AE security solutions currently being devised to address these problems and propose some recommendations and challenges to deal with security issue in AE protocol.

Incorporating Network Communication and Sports Psychology: An In-depth Quantitative Analysis of Interdisciplinary Knowledge Graphs and Association Patterns in Chinese Sports News Communication / Incorporación de la comunicación en red y la psicología del deporte: un análisis cuantitativo en profundidad de gráficos de conocimiento interdisciplinarios y patrones de asociación en la comunicación de noticias deportivas chinas

In the rapidly evolving landscape of sports news and sports psychology, driven by the proliferation of mobile Internet and Internet media in China, it is essential to recognize two pivotal factors shaping the discourse. Firstly, there is the imperative 'construction of new liberal arts' within professional knowledge education. Secondly, the practice of news communication is undergoing transformative changes due to media integration, particularly in the context of sports news concerning Chinese athletes. These two dynamics together create a favorable environment, underpinned by robust policy support, for interdisciplinary research at the intersection of sports psychology and sports news communication. Within the academic realm, our efforts must be attuned to the evolving curriculum and the unique challenges posed by this dynamic landscape. Such an approach is instrumental in advancing in-depth interdisciplinary research at the confluence of sports psychology and the communication of sports news. Recent years have witnessed an upsurge in research interest in media communication, with a pronounced emphasis on sports. In light of this, we have a unique opportunity to conduct a comprehensive analysis of interdisciplinary interactions within the domain of sports news and sports psychology. To facilitate this, we propose employing advanced knowledge graph techniques and correlation analysis. In this specialized context, the interdisciplinary study of sports psychology and sports news communication can be envisaged as a dynamic process that involves the assimilation and dissemination of specialized knowledge. We advocate for the application of a network environment correlation analysis method rooted in knowledge mapping principles. This approach entails the development of a tailored knowledge map specific to the realm of sports news concerning Chinese athletes, within the framework of sports psychology. Furthermore, we will explore the creation of efficient storage and retrieval mechanisms to optimize the effectiveness of our research pursuits in this exciting and evolving field.(AU)

Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks.

The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.

Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication.

Sleep inertia is the brief period of impaired alertness and performance experienced immediately after waking. Little is known about the neural mechanisms underlying this phenomenon. A better understanding of the neural processes during sleep inertia may offer insight into the awakening process. We observed brain activity every 15 min for 1 hr following abrupt awakening from slow wave sleep during the biological night. Using 32-channel electroencephalography, a network science approach, and a within-subject design, we evaluated power, clustering coefficient, and path length across frequency bands under both a control and a polychromatic short-wavelength-enriched light intervention condition. We found that under control conditions, the awakening brain is typified by an immediate reduction in global theta, alpha, and beta power. Simultaneously, we observed a decrease in the clustering coefficient and an increase in path length within the delta band. Exposure to light immediately after awakening ameliorated changes in clustering. Our results suggest that long-range network communication within the brain is crucial to the awakening process and that the brain may prioritize these long-range connections during this transitional state. Our study highlights a novel neurophysiological signature of the awakening brain and provides a potential mechanism by which light improves performance after waking.

Temporary alteration of neuronal network communication is a protective response to redox imbalance that requires GPI-anchored prion protein.

Cellular prion protein (PrPC) protects neurons against oxidative stress damage. This role is lost upon its misfolding into insoluble prions in prion diseases, and correlated with cytoskeletal breakdown and neurophysiological deficits. Here we used mouse neuronal models to assess how PrPC protects the neuronal cytoskeleton, and its role in network communication, from oxidative stress damage. Oxidative stress was induced extrinsically by potassium superoxide (KO2) or intrinsically by Mito-Paraquat (MtPQ), targeting the mitochondria. In mouse neural lineage cells, KO2 was damaging to the cytoskeleton, with cells lacking PrPC (PrP-/-) damaged more than wild-type (WT) cells. In hippocampal slices, KO2 acutely inhibited neuronal communication in WT controls without damaging the cytoskeleton. This inhibition was not observed in PrP-/- slices. Neuronal communication and the cytoskeleton of PrP-/- slices became progressively disrupted and degenerated post-recovery, whereas the dysfunction in WT slices recovered in 5 days. This suggests that the acute inhibition of neuronal activity in WT slices in response to KO2 was a neuroprotective role of PrPC, which PrP-/- slices lacked. Heterozygous expression of PrPC was sufficient for this neuroprotection. Further, hippocampal slices from mice expressing PrPC without its GPI anchor (PrPGPI-/-) displayed acute inhibition of neuronal activity by KO2. However, they failed to restore normal activity and cytoskeletal formation post-recovery. This suggests that PrPC facilitates the depressive response to KO2 and its GPI anchoring is required to restore KO2-induced damages. Immuno spin-trapping showed increased radicals formed on the filamentous actin of PrP-/- and PrPGPI-/- slices, but not WT and PrP+/- slices, post-recovery suggesting ongoing dysregulation of redox balance in the slices lacking GPI-anchored PrPC. The MtPQ treatment of hippocampal slices temporarily inhibited neuronal communication independent of PrPC expression. Overall, GPI-anchored PrPC alters synapses and neurotransmission to protect and repair the neuronal cytoskeleton, and neuronal communication, from extrinsically induced oxidative stress damages.

Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation.

Communication between gray matter regions underpins all facets of brain function. We study inter-areal communication in the human brain using intracranial EEG recordings, acquired following 29,055 single-pulse direct electrical stimulations in a total of 550 individuals across 20 medical centers (average of 87 ± 37 electrode contacts per subject). We found that network communication models-computed on structural connectivity inferred from diffusion MRI-can explain the causal propagation of focal stimuli, measured at millisecond timescales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional, and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (R2=46% in data from held-out medical centers). Our work contributes toward the biological validation of concepts in network neuroscience and provides insight into how connectome topology shapes polysynaptic inter-areal signaling. We anticipate that our findings will have implications for research on neural communication and the design of brain stimulation paradigms.

Edges in brain networks: Contributions to models of structure and function.

Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.

Efficient coding in the economics of human brain connectomics.

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion-weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior-beyond the conventional network efficiency metric-for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.

Will executive equity incentives affect investor relations management: Empirical evidence from Chinese listed companies.

How to improve the level of investor relationship management of listed companies and establish trust relationship with investors is an important research issue for enterprises in the capital market. From the perspective of optimal contracting theory, we construct a theoretical model to assess how executive equity incentive plans (EEIPs) affect enterprises' investor relationship management. For the analysis purpose, this study looks into panel data issues in depth by using approaches the fixed effect (FE) method, and the study employs the propensity score matching (PSM), instrumental variable method, and core indicator substitution method to test the robustness of the conclusions. Based on the panel data of Chinese A-share listed companies from 2014 to 2019, our baseline results indicate that EEIPs improves investor relations. This positive effect mainly exists in stock options, rather than restricted stocks. In the sample of enterprises implementing EEIPs, the intensity of executive equity incentive is positively correlated with investor relationship management. Further research shows that EEIPs mainly through telephone communication, network communication and on-site communication to achieve the impact of listed companies investor relationship management. These findings enriches the economics of executive equity incentives from the perspective of investor relations management. At the same time, it has certain guiding significance for improving the design of the incentive system for corporate executives and improving the information efficiency of the capital market.

Persistence of information flow: A multiscale characterization of human brain.

Information exchange in the human brain is crucial for vital tasks and to drive diseases. Neuroimaging techniques allow for the indirect measurement of information flows among brain areas and, consequently, for reconstructing connectomes analyzed through the lens of network science. However, standard analyses usually focus on a small set of network indicators and their joint probability distribution. Here, we propose an information-theoretic approach for the analysis of synthetic brain networks (based on generative models) and empirical brain networks, and to assess connectome's information capacity at different stages of dementia. Remarkably, our framework accounts for the whole network state, overcoming limitations due to limited sets of descriptors, and is used to probe human connectomes at different scales. We find that the spectral entropy of empirical data lies between two generative models, indicating an interpolation between modular and geometry-driven structural features. In fact, we show that the mesoscale is suitable for characterizing the differences between brain networks and their generative models. Finally, from the analysis of connectomes obtained from healthy and unhealthy subjects, we demonstrate that significant differences between healthy individuals and the ones affected by Alzheimer's disease arise at the microscale (max. posterior probability smaller than 1%) and at the mesoscale (max. posterior probability smaller than 10%).

Neuronal excitatory-to-inhibitory balance is altered in cerebral organoid models of genetic neurological diseases.

The neuro-physiological properties of individuals with genetic pre-disposition to neurological disorders are largely unknown. Here we aimed to explore these properties using cerebral organoids (COs) derived from fibroblasts of individuals with confirmed genetic mutations including PRNPE200K, trisomy 21 (T21), and LRRK2G2019S, which are associated with Creutzfeldt Jakob disease, Down Syndrome, and Parkinson's disease. We utilized no known disease/healthy COs (HC) as normal function controls. At 3-4 and 6-10 months post-differentiation, COs with mutations showed no evidence of disease-related pathology. Electrophysiology assessment showed that all COs exhibited mature neuronal firing at 6-10 months old. At this age, we observed significant changes in the electrophysiology of the COs with disease-associated mutations (dCOs) as compared with the HC, including reduced neuronal network communication, slowing neuronal oscillations, and increased coupling of delta and theta phases to the amplitudes of gamma oscillations. Such changes were linked with the detection of hypersynchronous events like spike-and-wave discharges. These dysfunctions were associated with altered production and release of neurotransmitters, compromised activity of excitatory ionotropic receptors including receptors of kainate, AMPA, and NMDA, and changed levels and function of excitatory glutamatergic synapses and inhibitory GABAergic synapses. Neuronal properties that modulate GABAergic inhibition including the activity of Na-K-Cl cotransport 1 (NKCC1) in Cl- homeostasis and the levels of synaptic and extra-synaptic localization of GABA receptors (GABARs) were altered in the T21 COs only. The neurosteroid allopregnanolone, a positive modulator of GABARs, was downregulated in all the dCOs. Treatment with this neurosteroid significantly improved the neuronal communication in the dCOs, possibly through improving the GABAergic inhibition. Overall, without the manifestation of any disease-related pathology, the genetic mutations PRNPE200K, T21, and LRRK2G2019S significantly altered the neuronal network communication in dCOs by disrupting the excitatory-to-inhibitory balance.

Multiscale communication in cortico-cortical networks.

Signaling in brain networks unfolds over multiple topological scales. Areas may exchange information over local circuits, encompassing direct neighbours and areas with similar functions, or over global circuits, encompassing distant neighbours with dissimilar functions. Here we study how the organization of cortico-cortical networks mediate localized and global communication by parametrically tuning the range at which signals are transmitted on the white matter connectome. We show that brain regions vary in their preferred communication scale. By investigating the propensity for brain areas to communicate with their neighbors across multiple scales, we naturally reveal their functional diversity: unimodal regions show preference for local communication and multimodal regions show preferences for global communication. We show that these preferences manifest as region- and scale-specific structure-function coupling. Namely, the functional connectivity of unimodal regions emerges from monosynaptic communication in small-scale circuits, while the functional connectivity of transmodal regions emerges from polysynaptic communication in large-scale circuits. Altogether, the present findings reveal that communication preferences are highly heterogeneous across the cortex, shaping regional differences in structure-function coupling.

Using social network tools to facilitate cultural adjustment of self-initiated Malaysian female expatriate nurses in Saudi Arabia.

BACKGROUND: This research study aims to:(1) identify and explore the social network communication tools used to facilitate the adjustment process of Malaysian female expatriate nurses working in the Kingdom of Saudi Arabia (hereafter "the Kingdom" or "SA") who are accompanied by neither their spouses nor families and (2) determine how these tools facilitate their adjustment to their new environment. Emphasis is placed on this particular group of respondents primarily due to the dearth of research conducted on female expatriate nurses. METHOD: We conducted a preliminary study using semi-structured interviews with sixteen (16) Malaysian female expatriate nurses working in SA to obtain a broader understanding of their experiences with cross-cultural adaptation and their use of social media tools to connect with their families and friends in their home country. RESULTS: This study uncovers numerous social media communication tools being used by female expatriate nurses to help curb their loneliness and lessen the culture shock of living and working in a foreign country. Continuous engagement with these tools helps Malaysian female expatriate nurses maintain their emotional stability, thereby enabling them to remain mentally strong and ultimately prolonging their stay in SA. CONCLUSIONS: This study's outcomes contribute significantly to the knowledge of the government, various organizations, and aspiring female expatriate nurses in the healthcare industry because the results can assist female expatriate nurses during the adjustment period, enabling them to work efficiently and successfully in the host country.

Network communication models improve the behavioral and functional predictive utility of the human structural connectome.

The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35-65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.

Neuron-specific knockouts indicate the importance of network communication to Drosophila rhythmicity.

Animal circadian rhythms persist in constant darkness and are driven by intracellular transcription-translation feedback loops. Although these cellular oscillators communicate, isolated mammalian cellular clocks continue to tick away in darkness without intercellular communication. To investigate these issues in Drosophila, we assayed behavior as well as molecular rhythms within individual brain clock neurons while blocking communication within the ca. 150 neuron clock network. We also generated CRISPR-mediated neuron-specific circadian clock knockouts. The results point to two key clock neuron groups: loss of the clock within both regions but neither one alone has a strong behavioral phenotype in darkness; communication between these regions also contributes to circadian period determination. Under these dark conditions, the clock within one region persists without network communication. The clock within the famous PDF-expressing s-LNv neurons however was strongly dependent on network communication, likely because clock gene expression within these vulnerable sLNvs depends on neuronal firing or light.

Cross-Network Directory Service: Infrastructure to enable collaborations across distributed research networks.

INTRODUCTION: Existing large-scale distributed health data networks are disconnected even as they address related questions of healthcare research and public policy. This paper describes the design and implementation of a fully functional prototype open-source tool, the Cross-Network Directory Service (CNDS), which addresses much of what keeps distributed networks disconnected from each other. METHODS: The set of services needed to implement a Cross-Directory Service was identified through engagement with stakeholders and workgroup members. CNDS was implemented using PCORnet and Sentinel network instances and tested by participating data partners. RESULTS: Web services that enable the four major functional features of the service (registration, discovery, communication, and governance) were developed and placed into an open-source repository. The services include a robust metadata model that is extensible to accommodate a virtually unlimited inventory of metadata fields, without requiring any further software development. The user interfaces are programmatically generated based on the contents of the metadata model. CONCLUSION: The CNDS pilot project gathered functional requirements from stakeholders and collaborating partners to build a software application to enable cross-network data and resource sharing. The two partners-one from Sentinel and one from PCORnet-tested the software. They successfully entered metadata about their organizations and data sources and then used the Discovery and Communication functionality to find data sources of interest and send a cross-network query. The CNDS software can help integrate disparate health data networks by providing a mechanism for data partners to participate in multiple networks, share resources, and seamlessly send queries across those networks.

Real-time experimental control using network-based parallel processing.

Modern neuroscience research often requires the coordination of multiple processes such as stimulus generation, real-time experimental control, as well as behavioral and neural measurements. The technical demands required to simultaneously manage these processes with high temporal fidelity is a barrier that limits the number of labs performing such work. Here we present an open-source, network-based parallel processing framework that lowers this barrier. The Real-Time Experimental Control with Graphical User Interface (REC-GUI) framework offers multiple advantages: (i) a modular design that is agnostic to coding language(s) and operating system(s) to maximize experimental flexibility and minimize researcher effort, (ii) simple interfacing to connect multiple measurement and recording devices, (iii) high temporal fidelity by dividing task demands across CPUs, and (iv) real-time control using a fully customizable and intuitive GUI. We present applications for human, non-human primate, and rodent studies which collectively demonstrate that the REC-GUI framework facilitates technically demanding, behavior-contingent neuroscience research. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).