Enhanced responses to inflammatory cytokine interleukin-6 in micropatterned networks of cultured cortical neurons.

Cortical neurons in dissociated cultures are an indispensable model system for pharmacological research that provides insights into chemical responses in well-defined environments. However, cortical neurons plated on homogeneous substrates develop an unstructured network that exhibits excessively synchronized activity, which occasionally masks the consequences induced by external substances. Here, we show that hyperactivity and excessive synchrony in cultured cortical networks can be effectively suppressed by growing neurons in microfluidic devices. These devices feature a hierarchically modular design that resembles the in vivo network. We focused on interleukin-6, a pro-inflammatory cytokine, and assessed its acute and chronic effects. Fluorescence calcium imaging of spontaneous neural activity for up to 20 days of culture showed detectable modulation of collective activity events and neural correlation in micropatterned neurons, which was not apparent in neurons cultured on homogeneous substrates. Our results indicate that engineered neuronal networks provide a unique platform for detecting and understanding the fundamental effects of biochemical compounds on neuronal networks.

Two-dimensional water-molecule-cluster layers at nanobubble interfaces.

HYPOTHESIS: Bulk nanobubbles (NBs) have high surface charge densities and long lifetimes. Despite several attempts to understand the lifetime of NBs, their interfacial layer structure remains unknown. It is hypothesized that a specific interfacial layer exists with a hydrogen bond network that stabilizes NBs. EXPERIMENTS: In situ infrared reflectance-absorption spectroscopy and density functional theory were used to determine the interfacial layer structure of NBs. Furthermore, nuclear magnetic resonance spectroscopy was used to examine the interfacial layer hardness of bubbles filled with N2, O2, and CO2, which was expected to depend on the encapsulated gas species. FINDINGS: The interfacial layer was composed of three-, four-, and five-membered ring clusters of water molecules. An interface model was proposed in which a two-dimensional layer of clusters with large electric dipole moments is oriented toward the endohedral gas, and the hydrophobic surface is adjacent to the free water. The interfacial layer hardness was dependent on the interaction with the gas (N2 > O2 > CO2), which supports the proposed interface model. These findings can be generalized to the structure of water at gas-water interfaces.

Modular architecture facilitates noise-driven control of synchrony in neuronal networks.

High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.

Model-free idealization: Adaptive integrated approach for idealization of ion-channel currents.

Single-channel electrophysiological recordings provide insights into transmembrane ion permeation and channel gating mechanisms. The first step in the analysis of the recorded currents involves an "idealization" process, in which noisy raw data are classified into two discrete levels corresponding to the open and closed states of channels. This provides valuable information on the gating kinetics of ion channels. However, the idealization step is often challenging in cases of currents with poor signal-to-noise ratios and baseline drifts, especially when the gating model of the target channel is not identified. We report herein on a highly robust model-free idealization method for achieving this goal. The algorithm, called adaptive integrated approach for idealization of ion-channel currents (AI2), is composed of Kalman filter and Gaussian mixture model clustering and functions without user input. AI2 automatically determines the noise reduction setting based on the degree of separation between the open and closed levels. We validated the method on pseudo-channel-current datasets that contain either computed or experimentally recorded noise. We also investigated the relationship between the noise reduction parameter of the Kalman filter and the cutoff frequency of the low-pass filter. The AI2 algorithm was then tested on actual experimental data for biological channels including gramicidin A, a voltage-gated sodium channel, and other unidentified channels. We compared the idealization results with those obtained by the conventional methods, including the 50%-threshold-crossing method.

Human mesenchymal stem-derived extracellular vesicles improve body growth and motor function following severe spinal cord injury in rat.

BACKGROUND: Spinal cord injury (SCI) in young adults leads to severe sensorimotor disabilities as well as slowing of growth. Systemic pro-inflammatory cytokines are associated with growth failure and muscle wasting. Here we investigated whether intravenous (IV) delivery of small extracellular vesicles (sEVs) derived from human mesenchymal stem/stromal cells (MSC) has therapeutic effects on body growth and motor recovery and can modulate inflammatory cytokines following severe SCI in young adult rats. METHODS: Contusional SCI rats were randomized into three different treatment groups (human and rat MSC-sEVs and a PBS group) on day 7 post-SCI. Functional motor recovery and body growth were assessed weekly until day 70 post-SCI. Trafficking of sEVs after IV infusions in vivo, the uptake of sEVs in vitro, macrophage phenotype at the lesion and cytokine levels at the lesion, liver and systemic circulation were also evaluated. RESULTS: An IV delivery of both human and rat MSC-sEVs improved functional motor recovery after SCI and restored normal body growth in young adult SCI rats, indicating a broad therapeutic benefit of MSC-sEVs and a lack of species specificity for these effects. Human MSC-sEVs were selectively taken up by M2 macrophages in vivo and in vitro, consistent with our previous observations of rat MSC-sEV uptake. Furthermore, the infusion of human or rat MSC-sEVs resulted in an increase in the proportion of M2 macrophages and a decrease in the production of the pro-inflammatory cytokines tumour necrosis factor-alpha (TNF-α) and interleukin (IL)-6 at the injury site, as well as a reduction in systemic serum levels of TNF-α and IL-6 and an increase in growth hormone receptors and IGF-1 levels in the liver. CONCLUSIONS: Both human and rat MSC-sEVs promote the recovery of body growth and motor function after SCI in young adult rats possibly via the cytokine modulation of growth-related hormonal pathways. Thus, MSC-sEVs affect both metabolic and neurological deficits in SCI.

Biological neurons act as generalization filters in reservoir computing.

Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data. Although the paradigm was initially proposed to model information processing in the mammalian cortex, it remains unclear how the nonrandom network architecture, such as the modular architecture, in the cortex integrates with the biophysics of living neurons to characterize the function of biological neuronal networks (BNNs). Here, we used optogenetics and calcium imaging to record the multicellular responses of cultured BNNs and employed the reservoir computing framework to decode their computational capabilities. Micropatterned substrates were used to embed the modular architecture in the BNNs. We first show that the dynamics of modular BNNs in response to static inputs can be classified with a linear decoder and that the modularity of the BNNs positively correlates with the classification accuracy. We then used a timer task to verify that BNNs possess a short-term memory of several 100 ms and finally show that this property can be exploited for spoken digit classification. Interestingly, BNN-based reservoirs allow categorical learning, wherein a network trained on one dataset can be used to classify separate datasets of the same category. Such classification was not possible when the inputs were directly decoded by a linear decoder, suggesting that BNNs act as a generalization filter to improve reservoir computing performance. Our findings pave the way toward a mechanistic understanding of information representation within BNNs and build future expectations toward the realization of physical reservoir computing systems based on BNNs.

Microfluidic cell engineering on high-density microelectrode arrays for assessing structure-function relationships in living neuronal networks.

Neuronal networks in dissociated culture combined with cell engineering technology offer a pivotal platform to constructively explore the relationship between structure and function in living neuronal networks. Here, we fabricated defined neuronal networks possessing a modular architecture on high-density microelectrode arrays (HD-MEAs), a state-of-the-art electrophysiological tool for recording neural activity with high spatial and temporal resolutions. We first established a surface coating protocol using a cell-permissive hydrogel to stably attach a polydimethylsiloxane microfluidic film on the HD-MEA. We then recorded the spontaneous neural activity of the engineered neuronal network, which revealed an important portrait of the engineered neuronal network-modular architecture enhances functional complexity by reducing the excessive neural correlation between spatially segregated modules. The results of this study highlight the impact of HD-MEA recordings combined with cell engineering technologies as a novel tool in neuroscience to constructively assess the structure-function relationships in neuronal networks.

Computational Efficiency of a Modular Reservoir Network for Image Recognition.

Liquid state machine (LSM) is a type of recurrent spiking network with a strong relationship to neurophysiology and has achieved great success in time series processing. However, the computational cost of simulations and complex dynamics with time dependency limit the size and functionality of LSMs. This paper presents a large-scale bioinspired LSM with modular topology. We integrate the findings on the visual cortex that specifically designed input synapses can fit the activation of the real cortex and perform the Hough transform, a feature extraction algorithm used in digital image processing, without additional cost. We experimentally verify that such a combination can significantly improve the network functionality. The network performance is evaluated using the MNIST dataset where the image data are encoded into spiking series by Poisson coding. We show that the proposed structure can not only significantly reduce the computational complexity but also achieve higher performance compared to the structure of previous reported networks of a similar size. We also show that the proposed structure has better robustness against system damage than the small-world and random structures. We believe that the proposed computationally efficient method can greatly contribute to future applications of reservoir computing.

Parallel Recordings of Transmembrane hERG Channel Currents Based on Solvent-Free Lipid Bilayer Microarray.

The reconstitution of ion-channel proteins in artificially formed bilayer lipid membranes (BLMs) forms a well-defined system for the functional analysis of ion channels and screening of the effects of drugs that act on these proteins. To improve the efficiency of the BLM reconstitution system, we report on a microarray of stable solvent-free BLMs formed in microfabricated silicon (Si) chips, where micro-apertures with well-defined nano- and micro-tapered edges were fabricated. Sixteen micro-wells were manufactured in a chamber made of Teflon®, and the Si chips were individually embedded in the respective wells as a recording site. Typically, 11 to 16 BLMs were simultaneously formed with an average BLM number of 13.1, which corresponded to a formation probability of 82%. Parallel recordings of ion-channel activities from multiple BLMs were successfully demonstrated using the human ether-a-go-go-related gene (hERG) potassium channel, of which the relation to arrhythmic side effects following drug treatment is well recognized.

Contribution of AMPA and NMDA receptors in the spontaneous firing patterns of single neurons in autaptic culture.

Single neurons in an autaptic culture exhibit various types of firing pattern with different firing durations and rhythms. However, a neuron with autapses has often been modeled as an oscillator providing a monotonic firing pattern with a constant periodicity because of the lack of a mathematical model. In the work described in this study, we use computational simulation and whole-cell patch-clamp recording to elucidate and model the mechanism by which such neurons generate various firing pattens. In the computational simulation, three types of spontaneous firing pattern, i.e., short, long-lasting, and periodic burst firing patterns are realized by changing the combination ratio of N-methyl-d-aspartate (NMDA) to α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) conductance. These three types of firing patterns are also observed in the experiments where neurons are cultured in isolation on micropatterned substrates. Using the AMPA and NMDA current models, we discuss that, in principle, autapses can regulate rhythmicity and information selection in neuronal networks.

NPC1L1 Facilitates Sphingomyelin Absorption and Regulates Diet-Induced Production of VLDL/LDL-associated S1P.

Niemann-Pick C1-Like 1 (NPC1L1) is a cholesterol importer and target of ezetimibe, a cholesterol absorption inhibitor used clinically for dyslipidemia. Recent studies demonstrated that NPC1L1 regulates the intestinal absorption of several fat-soluble nutrients, in addition to cholesterol. The study was conducted to reveal new physiological roles of NPC1L1 by identifying novel dietary substrate(s). Very low-density lipoprotein and low-density lipoprotein (VLDL/LDL) are increased in Western diet (WD)-fed mice in an NPC1L1-dependent manner, so we comprehensively analyzed the NPC1L1-dependent VLDL/LDL components. Apolipoprotein M (apoM), a binding protein of sphingosine-1-phosphate (S1P: a lipid mediator), and S1P were NPC1L1-dependently increased in VLDL/LDL by WD feeding. S1P is metabolized from sphingomyelin (SM) and SM is abundant in WD, so we focused on intestinal SM absorption. In vivo studies with Npc1l1 knockout mice and in vitro studies with NPC1L1-overexpressing cells revealed that SM is a physiological substrate of NPC1L1. These results suggest a scenario in which dietary SM is absorbed by NPC1L1 in the intestine, followed by SM conversion to S1P and, after several steps, S1P is exported into the blood as the apoM-bound form in VLDL/LDL. Our findings provide insight into the functions of NPC1L1 for a better understanding of sphingolipids and S1P homeostasis.

Characterization of Bulk Nanobubbles Formed by Using a Porous Alumina Film with Ordered Nanopores.

Nanobubbles (NBs), with their unique physicochemical properties and promising applications, have become an important research topic. Generation of monodispersed bulk NBs with specified gas content remains a challenge. We developed a simple method for generating bulk NBs, using porous alumina films with ordered straight nanoscaled holes. Different techniques, such as nanoparticle tracking analysis (NTA), atomic force microscopy (AFM), and infrared absorption spectroscopy (IRAS), are used to confirm NB formation. The NTA data demonstrate that the minimum size of the NBs formed is less than 100 nm, which is comparable to the diameter of nanoholes in the porous alumina film. By generating NBs with different gases, including CO2, O2, N2, Ar, and He, we discovered that the minimum size of NBs negatively correlated with the solubility of encapsulated gases in water. Due to the monodispersed size of NBs generated from the highly ordered porous alumina, we determined that NB size is distributed discretely with a uniform increment factor of [Formula: see text]. To explain the observed characteristic size distribution of NBs, we propose a simple model in which two NBs of the same size are assumed to preferentially coalesce. This characteristic bubble size distribution is useful for elucidating the basic characteristics of nanobubbles, such as the long-term stability of NBs. This distribution can also be used to develop new applications of NBs, for example, nanoscaled reaction fields through bubble coalescence.

Suppression of hypersynchronous network activity in cultured cortical neurons using an ultrasoft silicone scaffold.

The spontaneous activity pattern of cortical neurons in dissociated culture is characterized by burst firing that is highly synchronized among a wide population of cells. The degree of synchrony, however, is excessively higher than that in cortical tissues. Here, we employed polydimethylsiloxane (PDMS) elastomers to establish a novel system for culturing neurons on a scaffold with an elastic modulus resembling brain tissue, and investigated the effect of the scaffold's elasticity on network activity patterns in cultured rat cortical neurons. Using whole-cell patch clamp to assess the scaffold effect on the development of synaptic connections, we found that the amplitude of excitatory postsynaptic current, as well as the frequency of spontaneous transmissions, was reduced in neuronal networks grown on an ultrasoft PDMS with an elastic modulus of 0.5 kPa. Furthermore, the ultrasoft scaffold was found to suppress neural correlations in the spontaneous activity of the cultured neuronal network. The dose of GsMTx-4, an antagonist of stretch-activated cation channels (SACs), required to reduce the generation of the events below 1.0 event per min on PDMS substrates was lower than that for neurons on a glass substrate. This suggests that the difference in the baseline level of SAC activation is a molecular mechanism underlying the alteration in neuronal network activity depending on scaffold stiffness. Our results demonstrate the potential application of PDMS with biomimetic elasticity as cell-culture scaffold for bridging the in vivo-in vitro gap in neuronal systems.

Advances in Artificial Cell Membrane Systems as a Platform for Reconstituting Ion Channels.

An artificial cell membrane that is composed of bilayer lipid membranes (BLMs) with transmembrane proteins incorporated within them represents a well-defined system for the analysis of membrane proteins, especially ion channel proteins that are major targets for drug design. Because the BLM system has a high compatibility with recently developed cell-free expression systems, it has attracted attention as a next-generation drug screening system. However, three issues associated with BLM systems, i. e., their instability, the need for non-volatile organic solvents and a low efficiency of ion channel incorporation, have limited their use as a drug screening platform. In this personal account, we discuss our recent approaches to address these issues based on microfabrication. We also discuss the potential for using the BLM system combined with cell-free expression systems as a drug screening system for future personalized medicine.

Hepatic Expression of Niemann-Pick C1-Like 1, a Cholesterol Reabsorber from Bile, Exacerbates Western Diet-Induced Atherosclerosis in LDL Receptor Mutant Mice.

Westernization of dietary habits increases lipid intake and is responsible for increased numbers of patients with atherosclerotic diseases. Niemann-Pick C1-Like 1 (NPC1L1)-a cholesterol importer-plays a crucial role in dietary cholesterol absorption in the intestine and is closely associated with several lipid-related diseases, including atherosclerosis. NPC1L1 is highly expressed in the liver and intestine in humans, whereas NPC1L1 expression is low in the rodent liver. Due to species differences in the tissue distribution of NPC1L1, there are limited studies on the pathophysiological role of hepatic NPC1L1, a cholesterol reabsorber from bile. In the present study, to explore whether hepatic NPC1L1 is involved in the development/progression of atherosclerosis, we compared four kinds of atherosclerosis mouse models with different expression levels of NPC1L1 in the intestinal and liver tissues in a genetic background of dysfunctional low-density lipoprotein receptor mutation. Western diet (WD)-induced hyperlipidemia and atherosclerotic plaque formation were more severe in mice expressing NPC1L1 in both the liver and intestine (plasma cholesterol, 839.5 mg/dl; plaque area, 29.5% of total aorta), compared with mice expressing NPC1L1 only in the intestine (plasma cholesterol, 573.1 mg/dl; plaque area, 13.3% of total aorta). Such hepatic NPC1L1-mediated promotion of hyperlipidemia and atherosclerosis was not observed in mice not expressing intestinal NPC1L1 and mice treated with ezetimibe, an NPC1L1 inhibitor used clinically for dyslipidemia. These results suggested that hepatic NPC1L1 promotes WD-induced dyslipidemia and atherosclerosis in concert with intestinal NPC1L1. Our findings provide novel insights into the pathophysiological importance of hepatic NPC1L1 in development/progression of atherosclerosis. SIGNIFICANCE STATEMENT: Niemann-Pick C1-Like 1 (NPC1L1) protein, a cholesterol importer and a molecular target of ezetimibe clinically used for dyslipidemia, is highly expressed not only in the intestine, but also in the liver in humans, although the pathophysiological importance of hepatic NPC1L1 in atherosclerotic diseases remained unclear. By using novel mouse models to separately analyze the effects of hepatic and intestinal NPC1L1 on the development/progression of atherosclerosis, we first demonstrated that hepatic NPC1L1 accelerates Western diet-induced atherosclerotic plaque formation in an intestinal NPC1L1-dependent and an ezetimibe-sensitive manner.

Mean-field analysis of directed modular networks.

We considered a modular network with a binomial degree distribution and related the analytical relationships of the network properties (modularity, average clustering coefficient, and small-worldness) with structural parameters that define the network, i.e., number of nodes, number of modules, average node degree, and ratio of intra-modular to total connections. Even though modular networks are universally found in real-world systems and are consequently of broad interest in complex network science, the relationship between network properties and structural parameters has not yet been formulated. Here, we show that a series of equations for predicting the network properties can be related using a mean-field connectivity matrix that is defined on the basis of the structural parameters in the network generation algorithm. The theoretical results are then compared with values calculated numerically using the original connectivity matrix and are found to agree well, except when the connections between modules are sparse. Representation of the structure of the network using simple parameters is expected to be conducive for elucidating the structure-dynamics relationship.

Summary of temporal changes in air dose rates and radionuclide deposition densities in the 80 km zone over five years after the Fukushima Nuclear Power Plant accident.

We summarized temporal changes in air dose rates and radionuclide deposition densities over five years in the 80 km zone based on large-scale environmental monitoring data obtained continuously after the Fukushima Nuclear Power Plant (NPP) accident, including those already reported in the present and previous special issues. After the accident, multiple radionuclides deposited on the ground were detected over a wide area; radiocesium was found to be predominantly important from the viewpoint of long-term exposure. The relatively short physical half-life of 134Cs (2.06 y) has led to considerable reductions in air dose rates. The reduction in air dose rates owing to the radioactive decay of radiocesium was more than 60% over five years. Furthermore, the air dose rates in environments associated with human lives decreased at a considerably faster rate than expected for radioactive decay. The average air dose rate originating from the radiocesium deposited in the 80 km zone was lower than that predicted from radioactive decay by a factor of 2-3 at five years after the accident. Vertical penetration of radiocesium into the ground contributed greatly to the reduction in air dose rate because of an increase in the shielding of gamma rays; the estimated average reduction in air dose rate was approximately 25% with penetration compared to that without penetration. The average air dose rate measured in undisturbed fields in the 80 km zone was estimated to be reduced owing to decontamination by approximately 20% compared to that without decontamination. The average deposition density of radiocesium in undisturbed fields has decreased owing to radioactive decay, indicating that the migration of radiocesium in the horizontal direction has generally been slow. Nevertheless, in human living environments, horizontal radiocesium movement is considered to contribute significantly to the reduction in air dose rate. The contribution of horizontal radiocesium movement to the decrease in air dose rate was estimated to vary by up to 30% on average. Massive amounts of environmental data were used in extended analyses, such as the development of a predictive model or integrated air dose rate maps according to different measurement results, which facilitated clearer characterization of the contamination conditions. Ecological half-lives were evaluated in several studies by using a bi-exponential model. Short-term ecological half-lives were shorter than one year in most cases, while long-term ecological half-lives were different across the studies. Even though the general tendency of decrease in air dose rates and deposition densities in the 80 km zone were elucidated as summarized above, their trend was found to vary significantly according to location. Therefore, site-specific analysis is an important task in the future.

Ultrasoft Silicone Gel as a Biomimetic Passivation Layer in Inkjet-Printed 3D MEA Devices.

Multielectrode arrays (MEAs) are versatile tools that are used for chronic recording and stimulation of neural cells and tissues. Driven by the recent progress in understanding of how neuronal growth and function respond to scaffold stiffness, development of MEAs with a soft cell-to-device interface has gained importance not only for in vivo but also for in vitro applications. However, the passivation layer, which constitutes the majority of the cell-device interface, is typically prepared with stiff materials. Herein, a fabrication of an MEA device with an ultrasoft passivation layer is described, which takes advantage of inkjet printing and a polydimethylsiloxane (PDMS) gel with a stiffness comparable to that of the brain. The major challenge in using the PDMS gel is that it cannot be patterned to expose the sensing area of the electrode. This issue is resolved by printing 3D micropillars at the electrode tip. Primary cortical neurons are grown on the fabricated device, and effective stimulation of the culture confirms functional cell-device coupling. The 3D MEA device with an ultrasoft interface provides a novel platform for investigating evoked activity and drug responses of living neuronal networks cultured in a biomimetic environment for both fundamental research and pharmaceutical applications.

Measurement of ambient dose equivalent rates by walk survey around Fukushima Dai-ichi Nuclear Power Plant using KURAMA-II until 2016.

Ambient dose equivalent rates in various environments related to human lives were measured by walk surveys using the KURAMA-II systems from 2013 to 2016 within an 80-km radius of the Fukushima Dai-ichi Nuclear Power Plant. The dose rate of the locations where the walk survey was performed decreased to about 38% of its initial value in the 42 months from June 2013 to the December 2016, which was beyond that attributable to the physical decay of radiocaesium. The ecological half-life of the slow decreasing component was evaluated to be 4.1 ±â€¯0.2 y. The air dose rates decreased depending on the level of the evacuation areas, and the decrease in the dose rates was slightly larger in populated areas where humans are active. The dose rates as measured by walk surveys exhibited a good correlation with those by car-borne surveys, suggesting that car-borne survey data are reflecting the air dose rates in living environments surrounding roads. The comparison of walk survey data with car-borne survey data indicated that the air dose rate varies largely even within a 100 m square area, and the variation is enhanced by human activities. The dose rates measured by the walk surveys were estimated to be medial of those along roads and those of undisturbed flat ground, and they were found to be decreasing quickly compared with the air dose rate from the flat ground fixed-point measurements.

Impact of modular organization on dynamical richness in cortical networks.

As in many naturally formed networks, the brain exhibits an inherent modular architecture that is the basis of its rich operability, robustness, and integration-segregation capacity. However, the mechanisms that allow spatially segregated neuronal assemblies to swiftly change from localized to global activity remain unclear. Here, we integrate microfabrication technology with in vitro cortical networks to investigate the dynamical repertoire and functional traits of four interconnected neuronal modules. We show that the coupling among modules is central. The highest dynamical richness of the network emerges at a critical connectivity at the verge of physical disconnection. Stronger coupling leads to a persistently coherent activity among the modules, while weaker coupling precipitates the activity to be localized solely within the modules. An in silico modeling of the experiments reveals that the advent of coherence is mediated by a trade-off between connectivity and subquorum firing, a mechanism flexible enough to allow for the coexistence of both segregated and integrated activities. Our results unveil a new functional advantage of modular organization in complex networks of nonlinear units.