Neuronal Network Connectivity Research Articles

Tonic and burst-like locus coeruleus stimulation distinctly shift network activity across the cortical hierarchy

Noradrenaline (NA) release from the locus coeruleus (LC) changes activity and connectivity in neuronal networks across the brain, modulating multiple behavioral states. NA release is mediated by both tonic and burst-like LC activity. However, it is unknown whether the functional changes in target areas depend on these firing patterns. Using optogenetics, photometry, electrophysiology and functional magnetic resonance imaging in mice, we show that tonic and burst-like LC firing patterns elicit brain responses that hinge on their distinct NA release dynamics. During moderate tonic LC activation, NA release engages regions associated with associative processing, while burst-like stimulation biases the brain toward sensory processing. These activation patterns locally couple with increased astrocytic and inhibitory activity and change the brain’s topological configuration in line with the hierarchical organization of the cerebral cortex. Together, these findings reveal how the LC–NA system achieves a nuanced regulation of global circuit operations.

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain.

Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Effects of maternal supplementation with DHA and/or egg yolk powder on monoamine homeostasis in the perinatal piglet brain

ABSTRACT Objectives Reports indicate that children of mothers who received docosahexaenoic acid (DHA) or egg yolk supplements during pregnancy have improved performance on cognitive tasks and brain growth; their combination has recently been demonstrated to modulate functional neuronal network connectivity in the human-relevant piglet brain. To expand upon this functional connectivity analysis, neurochemical evaluation to determine how dietary supplementation with one or both of these nutrients during the last trimester of pregnancy alters monoamine homeostasis in selected brain regions of piglets was done. Methods Beginning gestation days 60–69 through weaning, pregnant sows were fed either control diet or diets supplemented with egg yolk powder, DHA, or both. Brains were then collected, and monoamine neurotransmitters and their metabolites were quantified from various brain regions with HPLC-ECD. Results Relative to controls, egg yolk supplementation increased serotonin metabolite (5-HIAA) levels in the cerebellum, while DHA supplementation decreased serotonin (5-HT) levels in the prefrontal cortex; combined supplementation increased norepinephrine metabolite (MHPG) levels in the prefrontal cortex and cerebellum, but decreased 5-HT levels in the posterior hippocampus. Notably, all diets increased serotonin, dopamine, and their respective metabolite levels in the substantia nigra. Disscussion This suggests both overlapping and specific effects of DHA and components of egg yolk in the context of maternal supplementation during pregnancy and lactation that might facilitate optimal neurodevelopment, with the nigrostriatal pathway being particularly sensitive. Such supplementations might impact brain function and facilitate development later in life through modulating monoamine homeostasis.

Microglia Mediate Contact-Independent Neuronal Network Remodeling via Secreted Neuraminidase-3 Associated with Extracellular Vesicles.

Neurons communicate with each other through electrochemical transmission at synapses. Microglia, the resident immune cells of the central nervous system, modulate this communication through a variety of contact-dependent and -independent means. Microglial secretion of active sialidase enzymes upon exposure to inflammatory stimuli is one unexplored mechanism of modulation. Recent work from our lab showed that treatment of neurons with bacterial sialidases disrupts neuronal network connectivity. Here, we find that activated microglia secrete neuraminidase-3 (Neu3) associated with fusogenic extracellular vesicles. Furthermore, we show that Neu3 mediates contact-independent disruption of neuronal network synchronicity through neuronal glycocalyx remodeling. We observe that NEU3 is transcriptionally upregulated upon exposure to inflammatory stimuli and that a genetic knockout of NEU3 abrogates the sialidase activity of inflammatory microglial secretions. Moreover, we demonstrate that Neu3 is associated with a subpopulation of extracellular vesicles, possibly exosomes, that are secreted by microglia upon inflammatory insult. Finally, we demonstrate that Neu3 is necessary and sufficient to both desialylate neurons and decrease neuronal network connectivity. These results implicate Neu3 in remodeling of the glycocalyx leading to aberrant network-level activity of neurons, with implications in neuroinflammatory diseases such as Parkinson's disease and Alzheimer's disease.

EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification

Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios.

Quantitative electroencephalography predicts postoperative delirium in cardiac surgical patients after cardiopulmonary bypass: a prospective observational study.

Despite its frequency and associated negative effect, delirium remains poorly recognized in postoperative patients after ICU admission, especially among those who have undergone cardiac surgery with cardiopulmonary bypass. Postoperative delirium is triggered by a wide variety of acute medical conditions associated with impaired neuronal network connectivity. The lack of objective biomarkers primarily hinders the early detection of delirium. Seeking early biomarkers for tracking POD could potentially assist in predicting the onset of delirium and assessing the severity of delirium and response to interventions. QEEGs were taken from 46 sedated postoperative patients, with 24 of them having undergone cardiac surgery. The assessment of delirium was performed twice daily using the Confusion Assessment Method for the ICU (CAM-ICU) to screen for postoperative delirium (POD). QEEG data were interpreted clinically by neurophysiologists and processed by open-source EEGLAB to identify features in patients who had or did not have POD after cardiac or non-cardiac surgery. The incidence of delirium in patients after undergoing cardiac surgery was nine times greater than in those after non-cardiac surgeries (41.7% vs. 4.5%; p = 0.0046). Patients with delirium experienced longer use of mechanical ventilation (118 h (78,323) compared to 20 h (18,23); p < 0.0001) and an extended ICU length of stay (7 days (6, 20) vs. 2 days (2, 4); p < 0.0001). The depth of anesthesia, as measured by RASS scores (p = 0.3114) and spectral entropy (p = 0.1504), showed no significant difference. However, notable differences were observed between delirious and non-delirious patients in terms of the amplitude-integrated EEG (aEEG) upper limit, the relative power of the delta band, and spectral edge frequency 95 (SEF95) (p = 0.0464, p = 0.0417, p = 0.0337, respectively). In a homogenous population of sedated postoperative patients, robust qEEG parameters strongly correlate with delirium and could serve as valuable biomarkers for early detection of delirium and assist in clinical decision-making.

Electrical stimulation induced structural 3D human engineered neural tissue with well-developed neuronal network and functional connectivity

Objective. Three-dimensional (3D) neural tissue engineering is expected to provide new stride in developing neural disease models and functional substitutes to aid in the treatment of central nervous system injury. We have previously detailed an electrical stimulation (ES) system to generate 3D mouse engineered neural tissue (mENT) in vitro. However, ES-induced human ENT (hENT) has not previously been either investigated or identified in structural and functional manner. Here, we applied ES as a stimulator to regulate human neural stem cells in 3D Matrigel, explored the components and functional properties of hENTs. Approach. By immunofluorescence chemical staining and electron microscope imaging, we evaluated the effects of ES on (1) neuronal differentiation and maturation, (2) neurites outgrowth and alignment in hENT, (3) formation of synapses and myelin sheaths in hENT. We further investigated the formation of synaptic connections between ex-vivo-fused mouse and human tissue. We used calcium imaging to detect activities of neurons in hENT culture. Results. ES could induce neuronal differentiation, the orderly growth of neurites and the maturation of neuron subtypes to construct a well-developed neuronal network with synapses and myelin sheaths. Most importantly, we discovered that raising extracellular K+ concentration resulted the increasing neuronal excitability in the hENT, indicating electrical activities in neuronal cells. Significance. We applied ES to generate the organised 3D hENTs and identified them in both structural and functional manner.

Stimulation-mediated reverse engineering of silent neural networks.

Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol for deriving connectivity of simulated silent neuronal networks using stimulation combined with a supervised learning algorithm, which enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell levels with high accuracy. We apply our method on rat cortical recordings fed through a circuit of heterogeneously connected leaky integrate-and-fire neurons firing at typical lognormal distributions and demonstrate improved performance during stimulation for multiple subpopulations. These testable predictions about the number and protocol of the required stimulations are expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.NEW & NOTEWORTHY We introduce a new concept for reverse engineering silent neuronal networks using a supervised learning algorithm combined with stimulation. We quantify the performance of the algorithm and the precision of deriving synaptic weights in inhibitory and excitatory subpopulations. We then show that stimulation enables deciphering connectivity of heterogeneous circuits fed with real electrode array recordings, which could extend in the future to deciphering connectivity in broad biological and artificial neural networks.

Neuronal network activity and connectivity are impaired in a conditional knockout mouse model with PCDH19 mosaic expression

Mutations in PCDH19 gene, which encodes protocadherin-19 (PCDH19), cause Developmental and Epileptic Encephalopathy 9 (DEE9). Heterogeneous loss of PCDH19 expression in neurons is considered a key determinant of the disorder; however, how PCDH19 mosaic expression affects neuronal network activity and circuits is largely unclear. Here, we show that the hippocampus of Pcdh19 mosaic mice is characterized by structural and functional synaptic defects and by the presence of PCDH19-negative hyperexcitable neurons. Furthermore, global reduction of network firing rate and increased neuronal synchronization have been observed in different limbic system areas. Finally, network activity analysis in freely behaving mice revealed a decrease in excitatory/inhibitory ratio and functional hyperconnectivity within the limbic system of Pcdh19 mosaic mice. Altogether, these results indicate that altered PCDH19 expression profoundly affects circuit wiring and functioning, and provide new key to interpret DEE9 pathogenesis.

Evaluation of the effectiveness of migraine therapy using functional resting MRI with course rhythmic transcranial magnetic stimulation

Introduction. In the context of the search for new migraine therapy options, strict control of their eff ectiveness by means of objective examination methods is required.Objective. Objective assessment of the eff ectiveness of TMS in patients with migraine on the basis of functional magnetic resonance imaging (fMRI) data.Material and methods. Resting-state fMRI before and after a fi ve-day course of TMS of the junction of the inferior frontal and temporal lobes bilaterally was performed in 19 patients with migraine. Changes in functional connectivity (FC) of the main neuronal networks of the brain, as well as clinical parameters of pain and quality of life of the patients were assessed before and after the course of TMS.Results. A decrease in pain intensity and anxiety scores, as well as a decrease in the number of acute pain medications taken, was observed against the background of the therapy. Changes in FC aff ected three main networks: the default mode network, the salience and visual networks. At the same time, decreased effi cacy of therapy was noted in patients with higher severity of depressive symptoms and presence of neuroimaging criteria of depression.Conclusion. The study suggests the effi cacy of TMS in patients with migraine based on neuroimaging criteria. It is worth paying special attention to the presence of depressive symptoms in migraine patients.

Connectivity concepts in neuronal network modeling.

Sustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about mathematical concepts and assumptions, algorithmic implementations, or parameterizations hinder progress. Such flaws are unfortunately frequent and one reason is a lack of readily applicable standards and tools for model description. Our work aims to advance complete and concise descriptions of network connectivity but also to guide the implementation of connection routines in simulation software and neuromorphic hardware systems. We first review models made available by the computational neuroscience community in the repositories ModelDB and Open Source Brain, and investigate the corresponding connectivity structures and their descriptions in both manuscript and code. The review comprises the connectivity of networks with diverse levels of neuroanatomical detail and exposes how connectivity is abstracted in existing description languages and simulator interfaces. We find that a substantial proportion of the published descriptions of connectivity is ambiguous. Based on this review, we derive a set of connectivity concepts for deterministically and probabilistically connected networks and also address networks embedded in metric space. Beside these mathematical and textual guidelines, we propose a unified graphical notation for network diagrams to facilitate an intuitive understanding of network properties. Examples of representative network models demonstrate the practical use of the ideas. We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.

Altered resting-state functional connectivity in hiPSCs-derived neuronal networks from schizophrenia patients.

Schizophrenia (SZ) is a severe mental disorder that arises from abnormal neurodevelopment, caused by genetic and environmental factors. SZ often involves distortions in reality perception and it is widely associated with alterations in brain connectivity. In the present work, we used Human Induced Pluripotent Stem Cells (hiPSCs)-derived neuronal cultures to study neural communicational dynamics during early development in SZ. We conducted gene and protein expression profiling, calcium imaging recordings, and applied a mathematical model to quantify the dynamism of functional connectivity (FC) in hiPSCs-derived neuronal networks. Along the neurodifferentiation process, SZ networks displayed altered gene expression of the glutamate receptor-related proteins HOMER1 and GRIN1 compared to healthy control (HC) networks, suggesting a possible tendency to develop hyperexcitability. Resting-state FC in neuronal networks derived from HC and SZ patients emerged as a dynamic phenomenon exhibiting connectivity configurations reoccurring in time (hub states). Compared to HC, SZ networks were less thorough in exploring different FC configurations, changed configurations less often, presented a reduced repertoire of hub states and spent longer uninterrupted time intervals in this less diverse universe of hubs. Our results suggest that alterations in the communicational dynamics of SZ emerging neuronal networks might contribute to the previously described brain FC anomalies in SZ patients, by compromising the ability of their neuronal networks for rapid and efficient reorganization through different activity patterns.

Oh, What a Tangled Web Epilepsy Genes May Weave.

Aberrant Neuronal Connectivity in the Cortex Drives Generation of Seizures in Rat Absence Epilepsy Studer F, Jarre G, Pouyatos B, et al. Brain 2022;145(6):1978-1991. doi:10.1093/brain/awab438.Absence epilepsy belongs to genetic epilepsies and is characterized by recurrent generalized seizures that are concomitant with alterations of consciousness and associated with cognitive comorbidities. Little is known about the mechanisms leading to occurrence of epileptic seizures (i.e., epileptogenesis) and, in particular, it remains an open question whether neuronal hypersynchronization, a key feature in seizure initiation, could result from aberrant structural connectivity within neuronal networks endowing them with epileptic properties. In the present study, we addressed this question using a genetic model of absence epilepsy in the rat where seizures initiate in the whisker primary somatosensory cortex. We hypothesized that alterations in structural connectivity of neuronal networks within wS1 contribute to pathological neuronal synchronization responsible for seizures. First, we used rabies virus-mediated retrograde synaptic tracing and evidenced that cortical neurons located in both upper- and deep-layers of whisker primary somatosensory cortex displayed aberrant and significantly increased connectivity the genetic model of absence epilepsy, as highlighted by a higher number of presynaptic partners. Next, we showed at the functional level that disrupting these aberrant whisker primary somatosensory cortex neuronal networks with synchrotron X-ray-mediated cortical microtransections drastically decreased both whisker primary somatosensory cortex neuron synchronization and seizure power, as revealed by in vivo local field potential recordings with multichannel probes. Taken together, our data provide for the first time strong evidence that increased structural connectivity patterns of cortical neurons represent critical pathological substrates for increased neuronal synchronization and generation of absence seizures.

Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks.

Reconstructing the recurrent structural connectivity of neuronal networks is a challenge crucial to address in characterizing neuronal computations. While directly measuring the detailed connectivity structure is generally prohibitive for large networks, we develop a novel framework for reverse-engineering large-scale recurrent network connectivity matrices from neuronal dynamics by utilizing the widespread sparsity of neuronal connections. We derive a linear input-output mapping that underlies the irregular dynamics of a model network composed of both excitatory and inhibitory integrate-and-fire neurons with pulse coupling, thereby relating network inputs to evoked neuronal activity. Using this embedded mapping and experimentally feasible measurements of the firing rate as well as voltage dynamics in response to a relatively small ensemble of random input stimuli, we efficiently reconstruct the recurrent network connectivity via compressive sensing techniques. Through analogous analysis, we then recover high dimensional natural stimuli from evoked neuronal network dynamics over a short time horizon. This work provides a generalizable methodology for rapidly recovering sparse neuronal network data and underlines the natural role of sparsity in facilitating the efficient encoding of network data in neuronal dynamics.

Maximum entropy models provide functional connectivity estimates in neural networks

Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are said to be functionally connected when their firing patterns show significant synchronicity. Methods to infer functional connections are often based on pair-wise cross-correlation between activity patterns of (small groups of) neurons. However, these methods are not very sensitive to detect inhibitory connections, and they were not designed for use during stimulation. Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity. They have the potential benefit to estimate functional connectivity during stimulation, and to infer excitatory as well as inhibitory connections. MaxEnt models do not involve pairwise comparison, but aim to capture probability distributions of sets of neurons that are synchronously active in discrete time bins. We used electrophysiological recordings from in vitro neuronal cultures on micro electrode arrays to investigate the ability of MaxEnt models to infer functional connectivity. Connectivity estimates provided by MaxEnt models correlated well with those obtained by conditional firing probabilities (CFP), an established cross-correlation based method. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the same magnitude as those detected by CFP. Thus, MaxEnt models provide a potentially powerful new tool to study functional connectivity in neuronal networks.

Revealing directed effective connectivity of cortical neuronal networks from measurements.

In the study of biological networks, one of the major challenges is to understand the relationships between network structure and dynamics. In this paper, we model in vitro cortical neuronal cultures as stochastic dynamical systems and apply a method that reconstructs directed networks from dynamics [Ching and Tam, Phys. Rev. E 95, 010301(R) (2017)2470-004510.1103/PhysRevE.95.010301] to reveal directed effective connectivity, namely, the directed links and synaptic weights, of the neuronal cultures from voltage measurements recorded by a multielectrode array. The effective connectivity so obtained reproduces several features of cortical regions in rats and monkeys and has similar network properties as the synaptic network of the nematode Caenorhabditis elegans, whose entire nervous system has been mapped out. The distribution of the incoming degree is bimodal and the distributions of the average incoming and outgoing synaptic strength are non-Gaussian with long tails. The effective connectivity captures different information from the commonly studied functional connectivity, estimated using statistical correlation between spiking activities. The average synaptic strengths of excitatory incoming and outgoing links are found to increase with the spiking activity in the estimated effective connectivity but not in the functional connectivity estimated using the same sets of voltage measurements. These results thus demonstrate that the reconstructed effective connectivity can capture the general properties of synaptic connections and better reveal relationships between network structure and dynamics.

Evaluating spatial and network properties of NMDA-dependent neuronal connectivity in mixed cortical cultures

A technique combining fluorescence imaging with Ca2+ indicators and single-cell laser scanning photostimulation of caged glutamate (LSPS) allowed identification of functional connections between individual neurons in mixed cultures of rat neocortical cells as well as observation of synchronous spontaneous activity among neurons. LSPS performed on large numbers of neurons yielded maps of functional connections between neurons and allowed calculation of neuronal network parameters. LSPS also provided an indirect measure of excitability of neurons targeted for photostimulation. By repeating LSPS sessions with the same neurons, stability of connections and change in the number and strength of connections were also determined. Experiments were conducted in the presence of bicuculline to study in detail the properties of excitatory neurotransmission. The AMPA receptor inhibitor, 6-Cyano-7-nitroquinoxaline-2,3-dione (CNQX), abolished synchronous neuronal activity but had no effect on connections mapped by LSPS. In contrast, the NMDA receptor inhibitor, 2-Amino-5-phosphono-pentanoic acid (APV), dramatically decreased the number of functional connections between neurons while also affecting synchronous spontaneous activity. Functional connections were also decreased by increasing extracellular Mg2+ concentration. These data demonstrated that LSPS mapping interrogates NMDA receptor-dependent connectivity between neurons in the network. In addition, a GluN2A-specific inhibitor, NVP-AAM077, decreased the number and strength of connections between neurons as well as neuron excitability. Conversely, the GluN2A-specific positive modulator, GNE-0723, increased these same properties. These data showed that LSPS can be used to directly study perturbations in the properties of NMDA receptor-dependent connectivity in neuronal networks. This approach should be applicable in a wide variety of in vitro and in vivo experimental preparations.

Aberrant neuronal connectivity in the cortex drives generation of seizures in rat absence epilepsy.

Absence epilepsy belongs to genetic epilepsies and is characterized by recurrent generalized seizures that are concomitant with alterations of consciousness and associated with cognitive comorbidities. Little is known about the mechanisms leading to occurrence of epileptic seizures (i.e. epileptogenesis) and, in particular, it remains an open question as to whether neuronal hypersynchronization, a key feature in seizure initiation, could result from aberrant structural connectivity within neuronal networks endowing them with epileptic properties. In the present study, we addressed this question using a genetic model of absence epilepsy in the rat where seizures initiate in the whisker primary somatosensory cortex (wS1). We hypothesized that alterations in structural connectivity of neuronal networks within wS1 contribute to pathological neuronal synchronization responsible for seizures. First, we used rabies virus-mediated retrograde synaptic tracing and showed that cortical neurons located in both upper- and deep-layers of wS1 displayed aberrant and significantly increased connectivity in the genetic model of absence epilepsy, as highlighted by a higher number of presynaptic partners. Next, we showed at the functional level that disrupting these aberrant wS1 neuronal networks with synchrotron X-ray-mediated cortical microtransections drastically decreased both the synchronization and seizure power of wS1 neurons, as revealed by in vivo local field potential recordings with multichannel probes. Taken together, our data provide for the first time strong evidence that increased structural connectivity patterns of cortical neurons represent critical pathological substrates for increased neuronal synchronization and generation of absence seizures.

Directing spatiotemporal firing patterns in primary neuron networks using nanomagnetic forces

Controlling the connectivity and firing patterns in neuronal networks is key to information processing in the brain. Engineering directed connectivity patterns in vitro, where firing patterns propagate reliably and reproducibly along a user-defined direction within a regrown neurite network, has been challenging. Recent efforts to overcome these challenges are based on using magnetic nanomaterials as a mechanical cue to orient the growth of neurite networks, e.g., within magnetic field gradients.

In vivo hydrogen peroxide diffusivity in brain tissue supports volume signaling activity

Hydrogen peroxide is a major redox signaling molecule underlying a novel paradigm of cell function and communication. A role for H2O2 as an intercellular signaling molecule and neuromodulator in the brain has become increasingly apparent, with evidence showing this biological oxidant to regulate neuronal polarity, connectivity, synaptic transmission and tuning of neuronal networks. This notion is supported by its ability to diffuse in the extracellular space, from source of production to target. It is, thus, crucial to understand extracellular H2O2 concentration dynamics in the living brain and the factors which shape its diffusion pattern and half-life. To address this issue, we have used a novel microsensor to measure H2O2 concentration dynamics in the brain extracellular matrix both in an ex vivo model using rodent brain slices and in vivo. We found that exogenously applied H2O2 is removed from the extracellular space with an average half-life of t1/2 = 2.2 s in vivo. We determined the in vivo effective diffusion coefficient of H2O2 to be D* = 2.5 × 10−5 cm2 s−1. This allows it to diffuse over 100 μm in the extracellular space within its half-life. Considering this, we can tentatively place H2O2 within the class of volume neurotransmitters, connecting all cell types within the complex network of brain tissue, regardless of whether they are physically connected. These quantitative details of H2O2 diffusion and half-life in the brain allow us to interpret the physiology of the redox signal and lay the pavement to then address dysregulation in redox homeostasis associated with disease processes.