Neural Circuits Research Articles

Automated dual olfactory device for studying head/tail chemosensation in Caenorhabditis elegans.

The correct interpretation of threat and reward is important for animal survival. Often, the decisions underlying these behavioral programs are mediated by volatile compounds in the animal's environment, which they detect and discriminate with specialized olfactory neurons along their body. Caenorhabditis (C.) elegans senses chemical stimuli with neurons located in the head and the tail of the animal, which mediate either attractive or aversive behaviors. How conflicting stimuli are processed in animals navigating different chemical gradients is poorly understood. Here, we conceived, created, and capitalized on a novel microfluidic device to enable automated and precise stimulation of head and tail neurons, either simultaneously or sequentially, while reading out neuronal activity in sensory and interneurons using genetically encoded calcium indicators. We achieve robust and programmable chemical pulses through the modulation of inlet pressures. To evaluate the device performance, we synchronized the flow control with microscopy data acquisition and characterized the flow properties in the fabricated devices. Together, our design has the potential to provide insight into the neural circuits and behavior of C. elegans simulating the experience of natural environments.

A Spectral Method for a Fokker-Planck Equation in Neuroscience with Applications in Neuron Networks with Learning Rules

A Spectral Method for a Fokker-Planck Equation in Neuroscience with Applications in Neuron Networks with Learning Rules

Optogenetics: Illuminating the Future of Hearing Restoration and Understanding Auditory Perception.

Hearing loss is a prevalent sensory impairment significantly affecting communication and quality of life. Traditional approaches for hearing restoration, such as cochlear implants, have limitations in frequency resolution and spatial selectivity. Optogenetics, an emerging field utilizing light-sensitive proteins, offers a promising avenue for addressing these limitations and revolutionizing hearing rehabilitation. This review explores the methods of introducing Channelrhodopsin- 2 (ChR2), a key light-sensitive protein, into cochlear cells to enable optogenetic stimulation. Viral- mediated gene delivery is a widely employed technique in optogenetics. Selecting a suitable viral vector, such as adeno-associated viruses (AAV), is crucial in efficient gene delivery to cochlear cells. The ChR2 gene is inserted into the viral vector through molecular cloning techniques, and the resulting viral vector is introduced into cochlear cells via direct injection or round window membrane delivery. This allows for the expression of ChR2 and subsequent light sensitivity in targeted cells. Alternatively, direct cell transfection offers a non-viral approach for ChR2 delivery. The ChR2 gene is cloned into a plasmid vector, which is then combined with transfection agents like liposomes or nanoparticles. This mixture is applied to cochlear cells, facilitating the entry of the plasmid DNA into the target cells and enabling ChR2 expression. Optogenetic stimulation using ChR2 allows for precise and selective activation of specific neurons in response to light, potentially overcoming the limitations of current auditory prostheses. Moreover, optogenetics has broader implications in understanding the neural circuits involved in auditory processing and behavior. The combination of optogenetics and gene delivery techniques provides a promising avenue for improving hearing restoration strategies, offering the potential for enhanced frequency resolution, spatial selectivity, and improved auditory perception.

Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models

Computational models of brain regions are crucial for understanding neuronal network dynamics and the emergence of cognitive functions. However, current supercomputing limitations hinder the implementation of large networks with millions of morphological and biophysical accurate neurons. Consequently, research has focused on simplified spiking neuron models, ranging from the computationally fast Leaky Integrate and Fire (LIF) linear models to more sophisticated non-linear implementations like Adaptive Exponential (AdEX) and Izhikevic models, through Generalized Leaky Integrate and Fire (GLIF) approaches. However, in almost all cases, these models are tuned (and can be validated) only under constant current injections and they may not, in general, also reproduce experimental findings under variable currents. This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations.

Ketamine can produce oscillatory dynamics by engaging mechanisms dependent on the kinetics of NMDA receptors

Ketamine is an N-methyl-D-aspartate (NMDA)-receptor antagonist that produces sedation, analgesia, and dissociation at low doses and profound unconsciousness with antinociception at high doses. At high and low doses, ketamine can generate gamma oscillations (>25 Hz) in the electroencephalogram (EEG). The gamma oscillations are interrupted by slow-delta oscillations (0.1 to 4 Hz) at high doses. Ketamine's primary molecular targets and its oscillatory dynamics have been characterized. However, how the actions of ketamine at the subcellular level give rise to the oscillatory dynamics observed at the network level remains unknown. By developing a biophysical model of cortical circuits, we demonstrate how NMDA-receptor antagonism by ketamine can produce the oscillatory dynamics observed in human EEG recordings and nonhuman primate local field potential recordings. We have identified how impaired NMDA-receptor kinetics can cause disinhibition in neuronal circuits and how a disinhibited interaction between NMDA-receptor-mediated excitation and GABA-receptor-mediated inhibition can produce gamma oscillations at high and low doses, and slow-delta oscillations at high doses. Our work uncovers general mechanisms for generating oscillatory brain dynamics that differs from ones previously reported and provides important insights into ketamine's mechanisms of action as an anesthetic and as a therapy for treatment-resistant depression.

The interaction effect of high social support and resilience on functional connectivity using seed-based resting-state assessed by 7-Tesla ultra-high field MRI

Recent resilience research has increasingly emphasized the importance of focusing on investigating the protective factors in mentally healthy populations, complementing the traditional focus on psychopathology. Social support has emerged as a crucial element within the complex interplay of individual and socio-environmental factors that shape resilience. However, the neural underpinnings of the relationship between social support and resilience, particularly in healthy subjects, remain largely unexplored. With advances in neuroimaging techniques, such as ultra-high field MRI at 7T and beyond, researchers can more effectively investigate the neural mechanisms underlying these factors. Thus, our study employed ultra-high field rs-fMRI to explore how social support moderates the relationship between psychological resilience and functional connectivity in a healthy cohort. We hypothesized that enhanced social support would amplify resilience-associated connectivity within neural circuits essential for emotional regulation, cognitive processing, and adaptive problem-solving, signifying a synergistic interaction where strong social networks bolster the neural underpinnings of resilience. (n = 30). Through seed-based functional connectivity analyses and interaction analysis, we aimed to uncover the neural correlates at the interplay of social support and resilience. Our findings indicate that perceived social support significantly (p<0.001) alters functional connectivity in the right and left FP, PCC, and left hippocampus, affirming the pivotal roles of these regions in the brain’s resilience network. Moreover, we identified significant moderation effects of social support across various brain regions, each showing unique connectivity patterns. Specifically, the right FP demonstrated a significant interaction effect where high social support levels were linked to increased connectivity with regions involved in socio-cognitive processing, while low social support showed opposite effects. Similar patterns by social support levels were observed in the left FP, with connectivity changes in clusters associated with emotional regulation and cognitive functions. The PCC’s connectivity was distinctly influenced by support levels, elucidating its role in emotional and social cognition. Interestingly, the connectivity of the left hippocampus was not significantly impacted by social support levels, indicating a unique pattern within this region. These insights highlight the importance of high social support levels in enhancing the neural foundations of resilience and fostering adaptive neurological responses to environmental challenges.

Astrocyte Calcium Signaling.

Astrocytes are predominant glial cells that tile the central nervous system and participate in well-established functional and morphological interactions with neurons, blood vessels, and other glia. These ubiquitous cells display rich intracellular Ca2+ signaling, which has now been studied for over 30 years. In this review, we provide a summary and perspective of recent progress concerning the study of astrocyte intracellular Ca2+ signaling as well as discussion of its potential functions. Progress has occurred in the areas of imaging, silencing, activating, and analyzing astrocyte Ca2+ signals. These insights have collectively permitted exploration of the relationships of astrocyte Ca2+ signals to neural circuit function and behavior in a variety of species. We summarize these aspects along with a framework for mechanistically interpreting behavioral studies to identify directly causal effects. We finish by providing a perspective on new avenues of research concerning astrocyte Ca2+ signaling.

Machine learning approaches for simultaneous spectrophotometric determination of heavy metal ions in water samples

In this study, the simultaneous determination of Co, Cd, Ni, Cu, and Pb was carried out as a color complex with 4-(2-pyridylazo) resorcinol in an aqueous solution under the assesting of machine learning. A partial least-squares multivariate linear regression and artificial neuron network for the analysis of mixtures of metals were developed. MATLAB is a powerful software machine learning program that was used to support matrix calculations and displays. The benefit of MATLAB in the construction of the machine learning model allows the development of a rapid and highly effective analysis of multiple components in the mixtures without separation and enrichment. For individual determinations, the working ranges were discovered as the important information for choosing the initial concentration of each heavy metal in a mixture, r. The results of analysis of Ni2+, Pb2+, and Cd2+ by two methods Partial Least Squares - PLS and Artificial Neural Networks - ANN are sensitive and accurate for simultaneous determination of the concentration of these ions in the synthesis mixture with a high regression coefficient of 0.993, respectively, 0.997, 0.997 for Ni2+, Pb2+ and Cd2+. As for Cu2+ and Co2+, the accuracy is higher when using the ANN method.

Verbal Symbols Support Concrete but Enable Abstract Concept Formation: Evidence From Brain‐Constrained Deep Neural Networks

AbstractConcrete symbols (e.g., sun, run) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is controversial whether a comparable avenue to semantic learning exists for abstract symbols (e.g., democracy). When we simulated the putative brain mechanisms of conceptual/semantic grounding using brain‐constrained deep neural networks, the learning of instances of concrete concepts outside of language contexts led to robust neural circuits generating substantial and prolonged activations. In contrast, the learning of instances of abstract concepts yielded much reduced and only short‐lived activity. Crucially, when conceptual instances were learned in the context of wordforms, circuit activations became robust and long‐lasting for both concrete and abstract meanings. These results indicate that, although the neural correlates of concrete conceptual representations can be built from grounding experiences alone, abstract concept formation at the neurobiological level is enabled by and requires the correlated presence of linguistic forms.

Activity-dependent dendrite patterning in the postnatal barrel cortex

For neural circuit construction in the brain, coarse neuronal connections are assembled prenatally following genetic programs, being reorganized postnatally by activity-dependent mechanisms to implement area-specific computational functions. Activity-dependent dendrite patterning is a critical component of neural circuit reorganization, whereby individual neurons rearrange and optimize their presynaptic partners. In the rodent primary somatosensory cortex (barrel cortex), driven by thalamocortical inputs, layer 4 (L4) excitatory neurons extensively remodel their basal dendrites at neonatal stages to ensure specific responses of barrels to the corresponding individual whiskers. This feature of barrel cortex L4 neurons makes them an excellent model, significantly contributing to unveiling the activity-dependent nature of dendrite patterning and circuit reorganization. In this review, we summarize recent advances in our understanding of the activity-dependent mechanisms underlying dendrite patterning. Our focus lays on the mechanisms revealed by in vivo time-lapse imaging, and the role of activity-dependent Golgi apparatus polarity regulation in dendrite patterning. We also discuss the type of neuronal activity that could contribute to dendrite patterning and hence connectivity.

Coupling of neurons favors the bursting behavior and the predominance of the tripod gait

In nature, it has been observed that the dominant locomotor pattern of hexapod insects is often the tripod gait. Our analysis of a Central Pattern Generator (CPG) of the insect movement gives reasons for this dominance: the mathematical CPG model presents the tripod pattern with bursting dynamics in a parametric region where an isolated neuron has a simpler behavior. That is, we show how the coupling of small networks of neurons makes the system to continue having bursting dynamics even when an isolated neuron would be spiking. Moreover, in parametric regions where a neuron shows chaotic dynamics, the CPG exhibits a chaotic synchronization phenomenon: the chaotic tripod gait. In other words, coupling loves bursting… and tripod gait. The hyperchaotic phenomenon is also present and we locate regions where up to four Lyapunov exponents are positive.

Urolithin A Inhibits Anterior Basolateral Amygdala to Ventral Hippocampal CA1 Circuit to Ameliorate Amyloid-β-Impaired Social Ability.

Anxiety and social withdrawal are highly prevalent among patients with Alzheimer's disease (AD). However, the neural circuit mechanisms underlying these symptoms remain elusive, and there is a need for effective prevention strategies. This study aims to elucidate the neural circuitry mechanisms underlying social anxiety in AD. We utilized 5xFAD mice and conducted a series of experiments including optogenetic manipulation, Tandem Mass Tag-labeled proteome analysis, behavioral assessments, and immunofluorescence staining. In 5xFAD mice, we observed significant amyloid-β (Aβ) accumulation in the anterior part of basolateral amygdala (aBLA). Behaviorally, 6-month-old 5xFAD mice displayed excessive social avoidance during social interaction. Concurrently, the pathway from aBLA to ventral hippocampal CA1 (vCA1) was significantly activated and exhibited a disorganized firing patterns during social interaction. By optogenetically inhibiting the aBLA-vCA1 pathway, we effectively improved the social ability of 5xFAD mice. In the presence of Aβ accumulation, we identified distinct changes in the protein network within the aBLA. Following one month of administration of Urolithin A (UA), we observed significant restoration of the abnormal protein network within the aBLA. UA treatment also attenuated the disorganized firings of the aBLA-vCA1 pathway, leading to an improvement in social ability. The aBLA-vCA1 circuit is a vulnerable pathway in response to Aβ accumulation during the progression of AD and plays a crucial role in Aβ-induced social anxiety. Targeting the aBLA-vCA1 circuit and UA administration are both effective strategies for improving the Aβ-impaired social ability.

NMDA receptor autoantibodies primarily impair the extrasynaptic compartment.

Autoantibodies directed against the N-methyl-D-aspartate receptor (NMDAR-Ab) are pathogenic immunoglobulins detected in patients suffering from NMDAR encephalitis. NMDAR-Ab alter the receptor membrane trafficking, synaptic transmission and neuronal network properties, leading to patients' neurological and psychiatric symptoms. Patients often have very little neuronal damage but rapid and massive (treatment-responsive) brain dysfunctions related to unknown early mechanism of NMDAR-Ab. Our understanding of this early molecular cascade remains surprisingly fragmented. Here, we used a combination of single molecule-based imaging of membrane proteins to unveil the spatio-temporal action of NMDAR-Ab onto live hippocampal neurons. We first demonstrate that different clones of NMDAR-Ab primarily affect extrasynaptic -and not synaptic- NMDAR. In the first minutes, NMDAR-Ab increase extrasynaptic NMDAR membrane dynamics, de-clustering its surface interactome. NMDAR-Ab also rapidly reshuffle all membrane proteins located at the extrasynaptic compartment. Consistent with this alteration of multiple proteins, NMDAR-Ab effects were not mediated through the sole interaction between NMDAR and EphB2 receptor. At the long-term, NMDAR-Ab reduce NMDAR synaptic pool by slowing down receptor membrane dynamics in a cross-linking independent manner. Remarkably, exposing only extrasynaptic NMDAR to NMDAR-Ab was sufficient to produce their full-blown effect on synaptic receptors. Collectively, we demonstrate that NMDAR-Ab first impair extrasynaptic proteins, and then the synaptic ones. These data shed thus new, and unsuspected, lights on the mode of action of NMDAR-Ab and likely to our understanding of (extra)synaptopathies.

Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates

Deep neural feedforward networks are effective models for a wide array of problems, but training and deploying such networks presents a significant energy cost. Spiking neural networks (SNNs), which are modeled after biologically realistic neurons, offer a potential solution when deployed correctly on neuromorphic computing hardware. Still, many applications train SNNs offline, and running network training directly on neuromorphic hardware is an ongoing research problem. The primary hurdle is that back-propagation, which makes training such artificial deep networks possible, is biologically implausible. Neuroscientists are uncertain about how the brain would propagate a precise error signal backward through a network of neurons. Recent progress addresses part of this question, e.g., the weight transport problem, but a complete solution remains intangible. In contrast, novel learning rules based on the information bottleneck (IB) train each layer of a network independently, circumventing the need to propagate errors across layers. Instead, propagation is implicit due the layers' feedforward connectivity. These rules take the form of a three-factor Hebbian update a global error signal modulates local synaptic updates within each layer. Unfortunately, the global signal for a given layer requires processing multiple samples concurrently, and the brain only sees a single sample at a time. We propose a new three-factor update rule where the global signal correctly captures information across samples via an auxiliary memory network. The auxiliary network can be trained a priori independently of the dataset being used with the primary network. We demonstrate comparable performance to baselines on image classification tasks. Interestingly, unlike back-propagation-like schemes where there is no link between learning and memory, our rule presents a direct connection between working memory and synaptic updates. To the best of our knowledge, this is the first rule to make this link explicit. We explore these implications in initial experiments examining the effect of memory capacity on learning performance. Moving forward, this work suggests an alternate view of learning where each layer balances memory-informed compression against task performance. This view naturally encompasses several key aspects of neural computation, including memory, efficiency, and locality.

Single-Pore Nanofluidic Logic Memristor with Reconfigurable Synaptic Functions and Designable Combinations.

The biological neural network is a highly efficient in-memory computing system that integrates memory and logical computing functions within synapses. Moreover, reconfiguration by environmental chemical signals endows biological neural networks with dynamic multifunctions and enhanced efficiency. Nanofluidic memristors have emerged as promising candidates for mimicking synaptic functions, owing to their similarity to synapses in the underlying mechanisms of ion signaling in ion channels. However, realizing chemical signal-modulated logic functions in nanofluidic memristors, which is the basis for brain-like computing applications, remains unachieved. Here, we report a single-pore nanofluidic logic memristor with reconfigurable logic functions. Based on the different degrees of protonation and deprotonation of functional groups on the inner surface of the single pore, the modulation of the memristors and the reconfiguration of logic functions are realized. More noteworthy, this single-pore nanofluidic memristor can not only avoid the average effects in multipore but also act as a fundamental component in constructing complex neural networks through series and parallel circuits, which lays the groundwork for future artificial nanofluidic neural networks. The implementation of dynamic synaptic functions, modulation of logic gates by chemical signals, and diverse combinations in single-pore nanofluidic memristors opens up new possibilities for their applications in brain-inspired computing.

Simple Encoded Circularly Polarized Protein Lighting

AbstractLighting systems with circularly polarized luminescence (CPL) are an emerging field with high hopes in, for example, neural cell circuits and encoding applications. The major challenges that forfeits their real‐world application are i) the design of chiroptical materials (CMs) with high CPL brightness (BCPL; today's record is Eu‐based compounds with average 287 M−1cm−1, while 90% of other CMs show <150 M−1cm−1 in solution) and ii) how to keep CPL response in films/coatings of technological relevance. Since natural evolution is driven by chiral selectivity at the supramolecular level, fluorescent proteins (FPs) are ideal candidates to provide large BCPL spanning visible and near‐infrared regions. This hypothesis is confirmed for all the known FP classes, demonstrating high emission intensities (photoluminescence quantum yields (ϕ) up to 76%) and record average BCPL of |200| M−1cm−1 (solution). What is more, the CPL response is also kept in polymer coatings. It is rationalized that structural factors (chromophore rigidity, surrounding amino acids, supramolecular packaging, and exciton coupling) hold a significant influence, regardless of the ϕ values. Finally, proof‐of‐concept CPL‐encoded signals in monochromatic/white hybrid light‐emitting diodes with FP‐polymer filters show exceptional stabilities. Overall, this work stands out FPs toward a new CM family, in general, and biogenic CPL‐encoded lighting systems, in particular.

A sensory–neuromorphic interface capable of environmental perception, sensory coding, and biological stimuli

AbstractThe sensory–neuromorphic interface is key to the application of neuromorphic electronics. Artificial spiking neurons and artificial sensory nerves have been created, and a few studies showed a complete neuromorphic system through cointegration with synaptic electronics. However, artificial synaptic devices and systems often do not work in real environments, which limits their ability to provide realistic neural simulations and interface with biological nerves. We report a sensory–neuromorphic interface that uses a fiber synapse to emulate a biological afferent nerve. For the first time, a sensing–neuromorphic interface is connected to a living organism for peripheral nerve stimulation, allowing the organism to establish a connection with its surrounding environment. The interface converts perceived environmental information into analog electrical signals and then into frequency‐dependent pulse signals, which simplify the information interface between the sensor and the pulse‐data processing center. The frequency of the interface shows a sublinear dependence on strain amplitude at different stimulus intensities, and can deliver increased frequency spikes at potentially damaging stimulus intensities, similar to the response of biological afferent nerves. To verify the application of this interface, a system that monitors strain and provides an overstrain alarm was constructed based on this afferent neural circuit. The system has a response time of <2 ms, which is compatible with the response time in biological systems. The interface can be potentially extended to process signals from almost any type of sensors for other afferent senses, and these results demonstrate the potential for neuromorphic interfaces to be applied to bionic sensory interfaces.

High-intensity interval training in patients with glaucoma (HIT-GLAUCOMA): protocol for a multicenter randomized controlled exercise trial

BackgroundGlaucoma stands as a prominent global cause of irreversible blindness and the primary treatment approach involves reducing intraocular pressure (IOP). However, around one-third of patients exhibit disease progression despite effective IOP reduction. Microvascular endothelial function, chronic inflammation, and oxidative stress are known to affect retinal neuronal networks and have been associated with disease severity and progression. Exercise training has the potential to counteract these mechanisms as add-on treatment to usual care.AimsThe HIT-GLAUCOMA study will investigate the effects of a 6-month high-intensity interval training (HIIT) on intermediate endpoints such as local retinal microvascular and systemic large artery function, inflammation, and oxidative stress as well as clinical endpoints such as visual field indices, optic nerve rim assessment, retinal nerve fiber layer thickness, IOP, number of eye drops, vision-related quality of life and ocular surface disease symptomatology.MethodsThe study is a multi-center randomized controlled clinical trial in patients with both normal tension and high-tension primary open angle glaucoma. Across two study centers, 128 patients will be enrolled and randomized on a 1:1 basis into an exercise intervention group and a usual care control group. The primary microvascular endpoints are retinal arteriolar and venular flicker light-induced dilation at 6 months. The primary endpoint in the systemic circulation is brachial artery flow-mediated dilation at 6 months.Anticipated resultsWe hypothesize that exercise therapy will improve retinal microvascular function and thus ocular blood flow in patients with glaucoma. As clinical outcomes, we will investigate the effect of exercise on visual field indices, optic nerve rim assessment, retinal nerve fiber layer thickness, IOP, number of eye drops, vision-related quality of life and ocular surface disease symptomatology.DiscussionHIT-GLAUCOMA is a blueprint trial design to study the effect of exercise training on neurodegenerative and cardiovascular diseases. Importantly, patients are also expected to benefit from improvements in general health and cardiovascular co-morbidities. If proven effective, exercise may offer a new add-on treatment strategy to slow glaucoma progression.Clinical Trial Registration NumberThe trial is registered at Clinicaltrials.gov under the identifier NCT06058598 and is currently in the recruitment stage.

The role of pre-supplementary motor cortex in action control with emotional stimuli: Arepetitive transcranial magnetic stimulationstudy.

Swiftly halting ongoing motor actions is essential to react to unforeseen and potentially perilous circumstances. However, the neural bases subtending the complex interplay between emotions and motor control have been scarcely investigated. Here, we used an emotional stop signal task (SST) to investigate whether specific neural circuits engaged by action suppression are differently modulated by emotional signals with respect to neutral ones. Participants performed an SST before and after the administration of one session of repetitive transcranial magnetic stimulation (rTMS) over the pre-supplementary motor cortex (pre-SMA), the right inferior frontal gyrus (rIFG), and the left primary motor cortex (lM1). Results show that rTMS over the pre-SMA improved the ability to inhibit prepotent action (i.e., better action control) when emotional stimuli were presented. In contrast, action control in a neutral context was fostered by rTMS over the rIFG. No changes were observed after lM1 stimulation. Intriguingly, individuals with higher impulsivity traits exhibited enhanced motor control when facing neutral stimuli following rIFG stimulation. These results further our understanding of the interplay between emotions and motor functions, shedding light on the selective modulation of neural pathways underpinning these processes.

Unveiling the Hidden Impact: Hematoma Volumes Unravel Circuit Disruptions in Intracerebral Hemorrhage.

Intracerebral hemorrhage (ICH) imposes a significant burden on patients, and the volume of hematoma plays a crucial role in determining the severity and prognosis of ICH. Although significant recent progress has been made in understanding the cellular and molecular mechanisms of surrounding brain tissue in ICH, our current knowledge regarding the precise impact of hematoma volumes on neural circuit damage remains limited. Here, using a viral tracing technique in a mouse model of striatum ICH, two distinct patterns of injury response were observed in upstream connectivity, characterized by both linear and nonlinear trends in specific brain areas. Notably, even low-volume hematomas had a substantial impact on downstream connectivity. Neurons in the striatum-ICH region exhibited heightened excitability, evidenced by electrophysiological measurements and changes in metabolic markers. Furthermore, a strong linear relationship (R2 = 0.91) was observed between hematoma volumes and NFL damage, suggesting a novel biochemical index for evaluating changes in neural injury. RNA sequencing analysis revealed the activation of the MAPK signaling pathway following hematoma, and the addition of MAPK inhibitor revealed a decrease in neuronal circuit damage, leading to alleviation of motor dysfunction in mice. Taken together, our study highlights the crucial role of hematoma size as a determinant of circuit injury in ICH. These findings have important implications for clinical evaluations and treatment strategies, offering opportunities for precise therapeutic approaches to mitigate the detrimental effects of ICH and improve patient outcomes.