Neuronal Activity In Brain Research Articles

New Pharmacological Therapies in the Treatment of Epilepsy in the Pediatric Population.

Epilepsy is a disorder characterized by abnormal brain neuron activity, predisposing individuals to seizures. The International League Against Epilepsy (ILAE) categorizes epilepsy into the following groups: focal, generalized, generalized and focal, and unknown. Infants are the most vulnerable pediatric group to the condition, with the cause of epilepsy development being attributed to congenital brain developmental defects, white matter damage, intraventricular hemorrhage, perinatal hypoxic-ischemic injury, perinatal stroke, or genetic factors such as mutations in the Sodium Channel Protein Type 1 Subunit Alpha (SCN1A) gene. Due to the risks associated with this condition, we have investigated how the latest pharmacological treatments for epilepsy in children impact the reduction or complete elimination of seizures. We reviewed literature from 2018 to 2024, focusing on the age group from 1 month to 18 years old, with some studies including this age group as well as older individuals. The significance of this review is to present and compile research findings on the latest antiseizure drugs (ASDs), their effectiveness, dosing, and adverse effects in the pediatric population, which can contribute to selecting the best drug for a particular patient. The medications described in this review have shown significant efficacy and safety in the studied patient group, outweighing the observed adverse effects. The main aim of this review is to provide a comprehensive summary of the current state of knowledge regarding the newest pharmacotherapy for childhood epilepsy.

Ciliogenesis defects after neurulation impact brain development and neuronal activity in larval zebrafish

Cilia are slender, hair-like structures extending from cell surfaces and playing essential roles in diverse physiological processes. Within the nervous system, primary cilia contribute to signaling and sensory perception, while motile cilia facilitate cerebrospinal fluid flow. Here, we investigated the impact of ciliary loss on neural circuit development using a zebrafish line displaying ciliogenesis defects. We found that cilia defects after neurulation affect neurogenesis and brain morphology, especially in the cerebellum, and lead to altered gene expression profiles. Using whole brain calcium imaging, we measured reduced light-evoked and spontaneous neuronal activity in all brain regions. By shedding light on the intricate role of cilia in neural circuit formation and function in the zebrafish, our work highlights their evolutionary conserved role in the brain and sets the stage for future analysis of ciliopathy models.

A systematic review and meta-analysis of the preclinical and clinical results of low-field magnetic stimulation in cognitive disorders.

Cognitive disorders such as major depressive disorder and bipolar disorder severely compromise brain function and neuronal activity. Treatments to restore cognitive abilities can have severe side effects due to their intense and excitatory nature, in addition to the fact that they are expensive and invasive. Low-field magnetic stimulation (LFMS) is a novel non-invasive proposed treatment for cognitive disorders. It repairs issues in the brain by altering deep cortical areas with treatments of low-intensity magnetic stimulation. This paper aims to summarize the current literature on the effects and results of LFMS in cognitive disorders. We developed a search strategy to identify relevant studies utilizing LFMS and systematically searched eight scientific databases. Our review suggests that LFMS could be a viable and effective treatment for multiple cognitive disorders, especially major depressive disorder. Additionally, longer, more frequent, and more personalized LFMS treatments tend to be more efficacious.

NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping.

Quantitative analysis of activated neurons in mouse brains by a specific stimulation is usually a primary step to locate the responsive neurons throughout the brain. However, it is challenging to comprehensively and consistently analyze the neuronal activity trace in whole brains of a large cohort of mice from many terabytes of volumetric imaging data. Here, we introduce NEATmap, a deep learning-based high-efficiency, high-precision and user-friendly software for whole-brain neuronal activity trace mapping by automated segmentation and quantitative analysis of immunofluorescence labeled c-Fos+ neurons. We applied NEATmap to study the brain-wide differentiated neuronal activation in response to physical and psychological stressors in cohorts of mice.

Effects of L-Type Voltage-Gated Calcium Channel (LTCC) Inhibition on Hippocampal Neuronal Death after Pilocarpine-Induced Seizure.

Epilepsy, marked by abnormal and excessive brain neuronal activity, is linked to the activation of L-type voltage-gated calcium channels (LTCCs) in neuronal membranes. LTCCs facilitate the entry of calcium (Ca2+) and other metal ions, such as zinc (Zn2+) and magnesium (Mg2+), into the cytosol. This Ca2+ influx at the presynaptic terminal triggers the release of Zn2+ and glutamate to the postsynaptic terminal. Zn2+ is then transported to the postsynaptic neuron via LTCCs. The resulting Zn2+ accumulation in neurons significantly increases the expression of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase subunits, contributing to reactive oxygen species (ROS) generation and neuronal death. Amlodipine (AML), typically used for hypertension and coronary artery disease, works by inhibiting LTCCs. We explored whether AML could mitigate Zn2+ translocation and accumulation in neurons, potentially offering protection against seizure-induced hippocampal neuronal death. We tested this by establishing a rat epilepsy model with pilocarpine and administering AML (10 mg/kg, orally, daily for 7 days) post-epilepsy onset. We assessed cognitive function through behavioral tests and conducted histological analyses for Zn2+ accumulation, oxidative stress, and neuronal death. Our findings show that AML's LTCC inhibition decreased excessive Zn2+ accumulation, reactive oxygen species (ROS) production, and hippocampal neuronal death following seizures. These results suggest amlodipine's potential as a therapeutic agent in seizure management and mitigating seizures' detrimental effects.

KAT7/HMGN1 signaling epigenetically induces tyrosine phosphorylation-regulated kinase 1A expression to ameliorate insulin resistance in Alzheimer's disease.

Epidemiological studies have revealed a correlation between Alzheimer's disease (AD) and type 2 diabetes mellitus (T2D). Insulin resistance in the brain is a common feature in patients with T2D and AD. KAT7 is a histone acetyltransferase that participates in the modulation of various genes. To determine the effects of KAT7 on insulin patients with AD. APPswe/PS1-dE9 double-transgenic and db/db mice were used to mimic AD and diabetes, respectively. An in vitro model of AD was established by Aβ stimulation. Insulin resistance was induced by chronic stimulation with high insulin levels. The expression of microtubule-associated protein 2 (MAP2) was assessed using immunofluorescence. The protein levels of MAP2, Aβ, dual-specificity tyrosine phosphorylation-regulated kinase-1A (DYRK1A), IRS-1, p-AKT, total AKT, p-GSK3β, total GSK3β, DYRK1A, and KAT7 were measured via western blotting. Accumulation of reactive oxygen species (ROS), malondialdehyde (MDA), and SOD activity was measured to determine cellular oxidative stress. Flow cytometry and CCK-8 assay were performed to evaluate neuronal cell death and proliferation, respectively. Relative RNA levels of KAT7 and DYRK1A were examined using quantitative PCR. A chromatin immunoprecipitation assay was conducted to detect H3K14ac in DYRK1A. KAT7 expression was suppressed in the AD mice. Overexpression of KAT7 decreased Aβ accumulation and MAP2 expression in AD brains. KAT7 overexpression decreased ROS and MDA levels, elevated SOD activity in brain tissues and neurons, and simultaneously suppressed neuronal apoptosis. KAT7 upregulated levels of p-AKT and p-GSK3β to alleviate insulin resistance, along with elevated expression of DYRK1A. KAT7 depletion suppressed DYRK1A expression and impaired H3K14ac of DYRK1A. HMGN1 overexpression recovered DYRK1A levels and reversed insulin resistance caused by KAT7 depletion. We determined that KAT7 overexpression recovered insulin sensitivity in AD by recruiting HMGN1 to enhance DYRK1A acetylation. Our findings suggest that KAT7 is a novel and promising therapeutic target for the resistance in AD.

Electroencephalography (EEG) based epilepsy diagnosis via multiple feature space fusion using shared hidden space-driven multi-view learning.

Epilepsy is a chronic, non-communicable disease caused by paroxysmal abnormal synchronized electrical activity of brain neurons, and is one of the most common neurological diseases worldwide. Electroencephalography (EEG) is currently a crucial tool for epilepsy diagnosis. With the development of artificial intelligence, multi-view learning-based EEG analysis has become an important method for automatic epilepsy recognition because EEG contains difficult types of features such as time-frequency features, frequency-domain features and time-domain features. However, current multi-view learning still faces some challenges, such as the difference between samples of the same class from different views is greater than the difference between samples of different classes from the same view. In view of this, in this study, we propose a shared hidden space-driven multi-view learning algorithm. The algorithm uses kernel density estimation to construct a shared hidden space and combines the shared hidden space with the original space to obtain an expanded space for multi-view learning. By constructing the expanded space and utilizing the information of both the shared hidden space and the original space for learning, the relevant information of samples within and across views can thereby be fully utilized. Experimental results on a dataset of epilepsy provided by the University of Bonn show that the proposed algorithm has promising performance, with an average classification accuracy value of 0.9787, which achieves at least 4% improvement compared to single-view methods.

Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer's disease on routinely acquired T1-weighted imaging-based brain network.

Radiomics-based morphological brain networks (radMBN) constructed from routinely acquired structural MRI (sMRI) data have gained attention in Alzheimer's disease (AD). However, the radMBN suffers from limited characterization of AD because sMRI only characterizes anatomical changes and is not a direct measure of neuronal pathology or brain activity. To establish a group sparse representation of the radMBN under a joint constraint of group-level white matter fiber connectivity and individual-level sMRI regional similarity (JCGS-radMBN). Two publicly available datasets were adopted, including 120 subjects from ADNI with both T1-weighted image (T1WI) and diffusion MRI (dMRI) for JCGS-radMBN construction, 818 subjects from ADNI and 200 subjects solely with T1WI from AIBL for validation in early AD diagnosis. Specifically, the JCGS-radMBN was conducted by jointly estimating non-zero connections among subjects, with the regularization term constrained by group-level white matter fiber connectivity and individual-level sMRI regional similarity. Then, a triplet graph convolutional network was adopted for early AD diagnosis. The discriminative brain connections were identified using a two-sample t-test, and the neurobiological interpretation was validated by correlating the discriminative brain connections with cognitive scores. The JCGS-radMBN exhibited superior classification performance over five brain network construction methods. For the typical NC vs. AD classification, the JCGS-radMBN increased by 1-30% in accuracy over the alternatives on ADNI and AIBL. The discriminative brain connections exhibited a strong connectivity to hippocampus, parahippocampal gyrus, and basal ganglia, and had significant correlation with MMSE scores. The proposed JCGS-radMBN facilitated the AD characterization of brain network established on routinely acquired imaging modality of sMRI. The online version of this article (10.1007/s13755-023-00269-0) contains supplementary material, which is available to authorized users.

Advancing the path to in-vivo imaging in freely moving mice via multimode-multicore fiber based holographic endoscopy.

Hair-thin multimode optical fiber-based holographic endoscopes have gained considerable interest in modern neuroscience for their ability to achieve cellular and even subcellular resolution during in-vivo deep brain imaging. However, the application of multimode fibers in freely moving animals presents a persistent challenge as it is difficult to maintain optimal imaging performance while the fiber undergoes deformations. We propose a fiber solution for challenging in-vivo applications with the capability of deep brain high spatial resolution imaging and neuronal activity monitoring in anesthetized as well as awake behaving mice. We used our previously developed multimode-multicore fiber to record fluorescently labeled neurons in anesthetized mice. Our exhibits a cascaded refractive index structure, enabling two distinct regimes of light transport that imitate either a multimode or a multicore fiber. The has been specifically designed for use in the initial phase of an in-vivo experiment, allowing for the navigation of the endoscope's distal end toward the targeted brain structure. The multicore regime enables the transfer of light to and from each individual neuron within the field of view. For chronic experiments in awake behaving mice, it is crucial to allow for disconnecting the fiber and the animal between experiments. Therefore, we provide here an effective solution and establish a protocol for reconnection of two segments of with hexagonally arranged corelets. We successfully utilized the to image neurons in anaesthetized transgenic mice expressing enhanced green fluorescent protein. Additionally, we compared imaging results obtained with the with larger numerical aperture (NA) fibers in fixed whole-brain tissue. This study focuses on addressing challenges and providing insights into the use of multimode-multicore fibers as imaging solutions for in-vivo applications. We suggest that the upcoming version of the increases the overall NA between the two cladding layers to allow for access to high resolution spatial imaging. As the NA increases in the multimode regime, the fiber diameter and ring structure must be reduced to minimize the computational burden and invasiveness.

Astrocytes modulate cerebral blood flow and neuronal response to cocaine in prefrontal cortex.

Cocaine affects both cerebral blood vessels and neuronal activity in brain. Cocaine can also disrupt astrocytes, which modulate neurovascular coupling-a process that regulates cerebral hemodynamics in response to neuronal activation. However, separating neuronal and astrocytic effects from cocaine's direct vasoactive effects has been challenging, partially due to limitations of neuroimaging techniques able to differentiate vascular from neuronal and glial effects at high temporal and spatial resolutions. Here, we used a newly-developed multi-channel fluorescence and optical coherence Doppler microscope (fl-ODM) that allows for simultaneous measurements of neuronal and astrocytic activities (reflected by the intracellular calcium changes in neurons Ca2+N and astrocytes Ca2+A, respectively) alongside their vascular interactions in vivo to address this challenge. Using green and red genetically-encoded Ca2+ indicators differentially expressed in astrocytes and neurons, fl-ODM enabled concomitant imaging of large-scale astrocytic and neuronal Ca2+ fluorescence and 3D cerebral blood flow velocity (CBFv) in vascular networks in the mouse cortex. We assessed cocaine's effects in the prefrontal cortex (PFC) and found that the CBFv changes triggered by cocaine were temporally correlated with astrocytic Ca2+A activity. Chemogenetic inhibition of astrocytes during the baseline state resulted in blood vessel dilation and CBFv increases but did not affect neuronal activity, suggesting modulation of spontaneous blood vessel's vascular tone by astrocytes. Chemogenetic inhibition of astrocytes during a cocaine challenge prevented its vasoconstricting effects alongside the CBFv decreases, but it also attenuated the neuronal Ca2+N increases triggered by cocaine. These results document a role of astrocytes both in regulating vascular tone and consequently blood flow, at baseline and for modulating the vasoconstricting and neuronal activation responses to cocaine in the PFC. Strategies to inhibit astrocytic activity could offer promise for ameliorating vascular and neuronal toxicity from cocaine misuse.

A critical review of ethanol effects on neuronal firing: A metabolic perspective.

Ethanol metabolism is relatively understudied in neurons, even though changes in neuronal metabolism are known to affect their activity. Recent work demonstrates that ethanol is preferentially metabolized over glucose as a source of carbon and energy, and it reprograms neurons to a state of reduced energy potential and diminished capacity to utilize glucose once ethanol is exhausted. Ethanol intake has been associated with changes in neuronal firing and specific brain activity (EEG) patterns have been linked with risk for alcohol use disorder (AUD). Furthermore, a haplotype of the inwardly rectifying potassium channel subunit, GIRK2, which plays a critical role in regulating excitability of neurons, has been linked with AUD and shown to be directly regulated by ethanol. At the same time, overexpression of GIRK2 prevents ethanol-induced metabolic changes. Based on the available evidence, we conclude thatthe mechanisms underlying the effects of ethanol on neuronal metabolism are a novel target for developing therapies for AUD.

Chronic Hyponatremia and Brain Structure and Function Before and After Treatment

Rationale & ObjectiveHyponatremia is the most common electrolyte disorder and is associated with significant morbidity and mortality. This study investigated neurocognitive impairment, brain volume, and alterations in MRI-based measures of cerebral function in patients before and after treatment for hyponatremia. Study DesignProspective cohort study. Setting & ParticipantsPatients with presumed chronic hyponatremia without signs of hypo- or hypervolemia treated in the Emergency Department of a German tertiary care hospital. ExposureHyponatremia ([Na+] <125 mmol/L) before and after treatment leading to [Na+] >130 mmol/L. OutcomesStandardized neuropsychological testing (Mini-Mental-State examination [MMSE], DemTect, Trail-Making Test [TMT] A/B, Beck’s Depression Inventory, Timed Up and Go) and resting-state MRI were performed prior to and following treatment of hyponatremia to assess volumes of total brain, white and gray matter, as well as neuronal activity and its synchronization. Analytical ApproachChanges in outcomes after treatment for hyponatremia assessed using bootstrapped confidence intervals and Cohen’s d. Associations between parameters were assessed using correlation analyses. ResultsDuring a 3.7-year period, 26 patients were enrolled. Complete data were available for 21 patients. Mean [Na+] was 118.4 mmol/L before treatment and 135.5 mmol/L following treatment. Most measures of cognition improved significantly. Comparison of MRI studies showed a decrease in brain tissue volumes, neuronal activity, and synchronization across all gray matter after normalization of [Na+]. Volume effects were particularly prominent in the hippocampus. During hyponatremia, synchronization of neuronal activity was negatively correlated with [Na+] (r=-.836, [-.979, -.446]) and cognitive function (MMSE: r=-.523, [-.805, -.069]; DemTect: r=-.744, [-.951, -.385] and TMT-A: r=.692, [.255, .922]). LimitationsSmall sample size, insufficient quality of several MRI scans due to motion artifact. ConclusionsResolution of hyponatremia was associated with improved cognition and reductions in brain volumes and neuronal activity. Impaired cognition during hyponatremia is closely linked to increased neuronal activity rather than to tissue volumes. Furthermore, the hippocampus appears to be particularly susceptible to hyponatremia, exhibiting pronounced changes in tissue volume.

Imaging Single-Cell Ca2+ Dynamics of Brainstem Neurons and Glia in Freely Behaving Mice.

In vivo brain imaging, using a combination of genetically encoded Ca2+ indicators and gradient refractive index (GRIN) lens, is a transformative technology that has become an increasingly potent research tool over the last decade. It allows direct visualisation of the dynamic cellular activity of deep brain neurons and glia in conscious animals and avoids the effect of anaesthesia on the network. This technique provides a step change in brain imaging where fibre photometry combines the whole ensemble of cellular activity, and multiphoton microscopy is limited to imaging superficial brain structures either under anaesthesia or in head-restrained conditions. We have refined the intravital imaging technique to image deep brain nuclei in the ventral medulla oblongata, one of the most difficult brain structures to image due to the movement of brainstem structures outside the cranial cavity during free behaviour (head and neck movement), whose targeting requires GRIN lens insertion through the cerebellum-a key structure for balance and movement. Our protocol refines the implantation method of GRIN lenses, giving the best possible approach to image deep extracranial brainstem structures in awake rodents with improved cell rejection/acceptance criteria during analysis. We have recently reported this method for imaging the activity of retrotrapezoid nucleus and raphe neurons to outline their chemosensitive characteristics. This revised method paves the way to image challenging brainstem structures to investigate their role in complex behaviours such as breathing, circulation, sleep, digestion, and swallowing, and could be extended to image and study the role of cerebellum in balance, movement, motor learning, and beyond. Key features • We developed a protocol that allows imaging from brainstem neurons and glia in freely behaving rodents. • Our refined method of GRIN lenses implantation and cell sorting approach gives the highest number of cells with the least postoperative complications. • The revised deep brainstem imaging method paves way to understand complex behaviours such as cardiorespiratory regulation, sleep, swallowing, and digestion. • Our protocol can be implemented to image cerebellar structures to understand their role in key functions such as balance, movement, motor learning, and more.

Epileptic Seizures Detection Using Machine Learning: A Review

These episodes may be attributed to abnormal chemical alterations in the brain or anomalies in the cerebral region. These illnesses are classified as chronic and increase the likelihood of death due to their abrupt onset and absence of preceding symptoms. Seizures can be identified by analyzing the brain signals produced by neuronal cells. Traditionally, electroencephalogram (EEG) recordings a multi-channel depiction of brain neuronal activity is used for brain signal monitoring. This paper examines important advancements made in the analysis of EEG waveforms to create predictive algorithms for epilepsy.

Investigation of the functioning of a neurohybrid system based on the FitzHugh–Nagumo radio generator and mouse hippocampal neurons

Currently, in the treatment of neurodegenerative diseases that are difficult to respond to drug therapy, electrical stimulation of the brain is performed through invasive intervention in damaged structures of the nervous tissue. The development and development of invasive technologies of closed-loop neural interfaces have made it possible to achieve great success in restoring neural connections, since they have finer and more precise stimulation settings that respond to changes in the physiological state, which is important in the process of restoring the functions of the nervous tissue. Advances in these areas open up prospects for the treatment of a wide range of diseases of the motor system and neurodegenerative diseases of the brain. In this study, a neurohybrid closed system was used, consisting of a FitzHugh-Nagumo radio generator and live surviving slices of the mouse brain hippocampus. For the preparation of surviving slices of the hippocampus, sexually mature males aged 2-3 months of the C57BL/6 mouse line were used. For the preparation and incubation of slices of the hippocampus, a solution of artificial cerebrospinal fluid (ACSF) was used, composition in (mM): 126 NaCl; 3.5 KCl; 1.2 KH2PO4; 26 NaHCO3; 1.3 MgCl * 6H2O; 2 CaCl2 * 6H2O; 10 D-glucose at constant carbogen saturation (95% O2 and 5% CO2). Registration of the electrical activity of brain neurons was carried out using optical and electrophysiological methods. In experiments on pairing a neuron-like FitzHugh-Nagumo generator and biological nerve cells in a closed circuit, an effect was obtained when the activity of brain nerve cells switched the generator to a self-oscillating mode. The evoked oscillations in the neuron-like generator provided an effective stimulus for the activation of nerve fibers in the perforant pathway of the hippocampus. As a result, it was possible to fix a decrease in the frequency of the generator impulses, which was provoked by the responses of living neurons to the incoming stimulus from the neuron-like generator. These results show the ability of live neural networks to control an artificial signal by adjusting its parameters by changing their own activity and confirm the efficiency of using closed loop systems when combining live and artificial neurons. The present study requires further experiments to create more physiological conditions for the functioning of the proposed neurohybrid system. In addition, this neurohybrid system will be improved and have adaptive properties through the use of memristive devices. Advances in this direction will help solve the urgent problem of restoring lost brain functions at the cellular and network levels.

Deschloroclozapine exhibits an exquisite agonistic effect at lower concentration compared to clozapine-N-oxide in hM3Dq expressing chemogenetically modified rats

IntroductionWithin the realm of chemogenetics, a particular form of agonists targeting designer receptors exclusively activated by designer drugs (DREADDs) has emerged. Deschloroclozapine (DCZ), a recently introduced DREADDs agonist, demonstrates remarkable potency in activating targeted neurons at a lower dosage compared to clozapine-N-oxide (CNO).MethodsWe conducted a comparative analysis of the effects of subcutaneously administered CNO (1 mg/kg) and DCZ (0.1 mg/kg) in our transgenic rats expressing hM3Dq and mCherry exclusively in oxytocin (OXT) neurons.Results and DiscussionNotably, DCZ exhibited a swift and robust elevation of serum OXT, surpassing the effects of CNO, with a significant increase in the area under the curve (AUC) up to 3 hours post-administration. Comprehensive assessment of brain neuronal activity, using Fos as an indicator, revealed comparable effects between CNO and DCZ. Additionally, in a neuropathic pain model, both CNO and DCZ increased the mechanical nociceptive and thermal thresholds; however, the DCZ-treated group exhibited a significantly accelerated onset of the effects, aligning harmoniously with the observed alterations in serum OXT concentration following DCZ administration. These findings emphasize the remarkable efficacy of DCZ in rats, suggesting its equivalent or potentially superior performance to CNO at considerably lower dosages, thus positioning it as a promising contender among DREADDs agonists.

Visualizing Monocarboxylates and Other Relevant Metabolites in the Ex Vivo Drosophila Larval Brain Using Genetically Encoded Sensors.

The high energy requirements of brains due to electrical activity are one of their most distinguishing features. These requirements are met by the production of ATP from glucose and its metabolites, such as the monocarboxylates lactate and pyruvate. It is still unclear how this process is regulated or who the key players are, particularly in Drosophila. Using genetically encoded Förster resonance energy transfer-based sensors, we present a simple method for measuring the transport of monocarboxylates and glucose in glial cells and neurons in an ex-vivo Drosophila larval brain preparation. The protocol describes how to dissect and adhere a larval brain expressing one of the sensors to a glass coverslip. We present the results of an entire experiment in which lactate transport was measured in larval brains by knocking down previously identified monocarboxylate transporters in glial cells. Furthermore, we demonstrate how to rapidly increase neuronal activity and track metabolite changes in the active brain. The described method, which provides all necessary information, can be used to analyze other Drosophila living tissues.

Advances in the study of the role of chemokines in drug addiction and the potential effects of traditional Chinese medicines

Drug abuse significantly affects the neuroimmune system, which in turn contributes to the development of addiction. Chemokines, the critical components of neuroimmune factors, mediate many brain functions, including neuroinflammation, neuronal activity, and endocrine function. Extensive abnormalities in chemokines have been found in models of drug addiction; however, the underlying mechanisms and treatment efficacy remain unclear. Traditional Chinese medicines (TCMs) are increasingly recognized and applied in immune regulation and drug addiction. This review highlights the recent advances in our understanding of chemokine changes associated with exposure to drugs of abuse, including cocaine, opiates, and methamphetamine. We provide a brief overview of what is known about the role of chemokines in brain function and addiction and the TCMs that could be applied in chemokine regulation.

Hepatic Acyl CoA Oxidase1 Inhibition Modifies Brain Lipids and Electrical Properties of Dentate Gyrus.

Peroxisomes are essential organelles in lipid metabolism. They contain enzymes for β-oxidation of very long-chain fatty acids (VLCFA) that cannot be broken down in mitochondria. Reduced expression in hepatic acyl-CoA oxidase 1 (ACOX1), a peroxisome β-oxidation enzyme, followed by modification of the brain fatty acid profile has been observed in aged rodents. These studies have suggested a potential role for peroxisome β-oxidation in brain aging. This study was designed to examine the effect of hepatic ACOX1 inhibition on brain fatty acid composition and neuronal cell activities of young rats (200-250 g). A specific ACOX1 inhibitor, 10, 12- tricosadiynoic acid (TDYA), 100 μg/kg (in olive oil) was administered by daily gavage for 25 days in male Wistar rats. The brain fatty acid composition and electrophysiological properties of dentate gyrus granule cells were determined using gas chromatography and whole-cell patch-clamp, respectively. A significant increase in C20, C22, C18:1, C20:1, and a decrease of C18, C24, C20:3n6, and C22:6n3 were found in 10, 12- tricosadiynoic acid (TDYA) treated rats compared to the control group. The results showed that ACOX1 inhibition changes fatty acid composition similar to old rats. ACOX1 inhibition caused hyperpolarization of resting membrane potential, and also reduction of input resistance, action potential duration, and spike firing. Moreover, ACOX1 inhibition increased rheobase current and afterhyperpolarization amplitude in granule cells. The results indicated that systemic inhibition of ACOX1 causes hypo-excitability of neuronal cells. These results provide new evidence on the involvement of peroxisome function and hepatic ACOX1 activity in brain fatty acid profile and the electrophysiological properties of dentate gyrus cells.

A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics.

Mean-field (MF) models are computational formalism used to summarize in a few statistical parameters the salient biophysical properties of an inter-wired neuronal network. Their formalism normally incorporates different types of neurons and synapses along with their topological organization. MFs are crucial to efficiently implement the computational modules of large-scale models of brain function, maintaining the specificity of local cortical microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar microcircuit (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF reproduced the average dynamics of different neuronal populations in response to various input patterns and predicted the modulation of the Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool for future investigations of the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.