Brain Disease Research Articles

Vanadium Carbide Quantum Dots Exert Efficient Anti‐Inflammatory Effects in Lipopolysaccharide‐Induced BV2 Microglia and Mice

The regulation of glial cell activation is a critical step for the treatment or prevention of neuroinflammation‐based brain diseases. However, the development of therapeutic drugs that pass the blood–brain barrier (BBB) and inhibit the glia cell activation remains a significant challenge. Herein, an ultrasmall 2D vanadium carbide quantum dots (V2C QDs) that are capable of crossing the BBB are prepared, and the admirable anti‐neuroinflammatory effects are presented. The prepared 2D V2C QDs with an average size of 2.54 nm show good hydrophilicity, physiological stability, and effective BBB‐crossing ability. The biological effect of V2C QDs on inflammatory reactions demonstrates fascinating results in preventing the impairment of learning and memory in BALB/c mice stimulated by lipopolysaccharide. Investigation of molecular mechanism reveals that V2C QDs not only inhibit the toll‐like receptor 4/myeloid differentiation factor 88‐mediated nuclear factor kappa B and mitogen‐activated protein kinase pathways, but also prevent eukaryotic translation initiation factor 2α/activating transcription factor 4/C/EBP homologous protein‐signaling pathway and reduce oxidative stress via activating the NF‐E2‐related factor‐2/heme oxygenase‐1‐signaling pathway, leading to greatly inhibited activation of microglia and astrocytes and weakened production of inflammatory cytokines. In summary, V2C QDs exert potent anti‐inflammatory effects through multiple pathways, thus offer great potential for the treatment of neurodegenerative diseases.

A ResNet based transfer learning pulmonary medical image disease detection system

Medical imaging represents a significant application of deep learning. In response to the vast domain of medical image analysis and the limitations and inefficiencies of conventional methods for diagnosing brain diseases, this paper explores the application of an enhanced ResNet-50 model in the classification of brain CT images. The goal is to improve the detection and classification accuracy of aneurysms, cancer, and malignant tumors. The study utilized 259 images, undergoing training, validation, and testing processes to verify the model's efficacy. The ResNet-50 model addresses the issue of vanishing gradients in deep networks through residual learning, making it suitable for high-resolution images and small datasets. Results indicate good training performance, though some fluctuations in validation performance, achieving a validation accuracy of 0.960. This research not only enhances diagnostic accuracy for brain diseases but also paves a new path for the further application of deep learning technologies in the medical field.

A trans-synaptic IgLON adhesion molecular complex directly contacts and clusters a nicotinic receptor.

The localization and clustering of neurotransmitter receptors at appropriate postsynaptic sites is a key step in the control of synaptic transmission. Here, we identify a novel paradigm for the synaptic localization of an ionotropic acetylcholine receptor (AChR) based on the direct interaction of its extracellular domain with a cell adhesion molecule of the IgLON family. Our results show that RIG-5 and ZIG-8, which encode the sole IgLONs in C. elegans, are tethered in the pre- and postsynaptic membranes, respectively, and interact in vivo through their first immunoglobulin-like (Ig) domains. In addition, ZIG-8 traps ACR-16 via a direct cis- interaction between the ZIG-8 Ig2 domain and the base of the large extracellular AChR domain. Such mechanism has never been reported, but all these molecules are conserved during evolution. Similar interactions may directly couple Ig superfamily adhesion molecules and members of the large family of Cys-loop ionotropic receptors, including AChRs, in the mammalian nervous system, and may be relevant in the context of IgLON-associated brain diseases.

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation.

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next-generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct ~10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimer's disease and healthy brain ageing based on ~4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 μm isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

Defining of the nasal anthropometry on facial computed tomography in subjects with epilepsy and healthy subjects.

Epilepsy is a frequent chronic and genetic brain disease. In diagnose of genetic and craniofacial disease, the face is one of the gold standard phenotypic features. This study was carried out to determine the angle and linear measurements of face region in Turkish healthy and epilepsy subjects, and to apply a feature selection method to identify the most important attributes that affect epilepsy decision. The retrospective and observational study was conducted with 120 subjects with epilepsy aged between 18 to 60 years (56 males; 64 females) and 60 healthy subjects aged between 18 and 55 years (29 males, 31 females). Pyramidal angle performed from 3 reference points, nasal bone length, the width of piriform aperture, nasofrontal angle, nasolabial angle, distance between glabella and nasion were measured on computed tomography. Also, we used supervised machine learning to learn classification models to detect epilepsy as our dataset has class label where 1 means epilepsy, 2 means healthy. The well-known classification model learning algorithms implemented in Weka (with version 3.8.6) machine learning toolkit were applied. All parameters excluding nasal bone length were higher in epilepsy patients than in healthy subjects. Also, there was a significant difference in nasal pyramidal angle nasal bone, nasal pyramidal angle nasal tip, piriform aperture, and nasal bone lengths between epilepsy and healthy subjects. However, age related changes for healthy subjects were no seen in healthy subjects. In epilepsy subjects there was a significant difference in 6 measurements all nasal pyramidal angles, piriform aperture width, nasofrontal angle, and nasolabial angle. Gender related changes were found in only nasal pyramidal angle nasal root and nasal bone of healthy subjects, in nasal pyramidal angle nasal bone, nasal bone length, nasofrontal angle of epilepsy subjects. We can say epilepsy may affect the some facial parameters and these, although anthropometric measurements are affected by age and gender parameters. Comprehensive knowledge of this region's normal references ranges is essential for planning, proper selection of silicone implants or osteotomy determining the limitations of the surgical field and minimizing the risk of complication and performing aesthetic facial surgery or rhinoplasty in epilepsy patients.

Taraxacum coreanum (Korean Dandelion) Extract Protects against Lipopolysaccharide-Induced Blood-Brain Barrier Destruction via Regulation of Tight Junctions and Inflammatory Responses in bEnd.3 Cells.

Dysfunction of the blood-brain barrier (BBB) is closely related to neuroinflammation-mediated neurodegenerative disorders. Lipopolysaccharide (LPS), an endotoxin, can cause inflammation by impairing the brain endothelial barrier function and increasing the BBB permeability. Although Taraxacum coreanum NAKAI extract (TC), a traditional medicine widely used in Korea, has antioxidant and anti-inflammatory properties, the protective effects on neuroinflammation and BBB dysfunction are not fully understood. In the present study, bEnd.3 cerebral vascular endothelial cells were treated with TC followed by LPS exposure, and the effects on transendothelial electrical resistance (TEER) values, pro-inflammatory cytokine production, and expression of proteins related to inflammatory responses and tight junction integrity were assessed. The TC-treated group exhibited elevated TEER values in bEnd.3 monolayer compared to LPS-only treated group. In addition, TC treatment increased the expression of proteins involved in the tight junctions, such as ZO-1, claudin-5, and occludin. Furthermore, the TC-treated group suppressed the proteins expression-related to nuclear factor-κB (NF-κB) pathway. Taken together, TC attenuates LPS-induced neuroinflammatory responses by regulating NF-κB activation, which may contribute to protecting against BBB disruption. These findings suggest that TC may have the potential to be used as a material for functional foods to prevent neuroinflammation-related brain diseases.

The schizophrenia risk gene C4 induces pathological synaptic loss by impairing AMPAR trafficking.

Neuroimmune interactions play a significant role in regulating synaptic plasticity in both the healthy and diseased brain. The complement pathway, an extracellular proteolytic cascade, exemplifies these interactions. Its activation triggers microglia-dependent synaptic elimination via the complement receptor 3 (CR3). Current models of pathological complement activity in the brain propose that accelerated synaptic loss resulting from overexpression of C4 (C4-OE), a gene associated with schizophrenia, follows this pathway. Here, we report that C4-mediated cortical hypoconnectivity is CR3-independent. Instead, C4-OE triggers impaired GluR1 trafficking through an intracellular mechanism involving the endosomal protein SNX27, resulting in pathological synaptic loss. Moreover, C4 circuit alterations in the prefrontal cortex, a brain region associated with neuropsychiatric disorders, were rescued by increasing neuronal levels of SNX27, which we identify as an interacting partner of this neuroimmune protein. Our results link excessive complement activity to an intracellular endo-lysosomal trafficking pathway altering synaptic plasticity.

A Comprehensive Analysis of Metastatic Disease following Surgery for Clinically Localized Cutaneous Melanoma.

The NCCN considers "baseline staging" (whole body CT or PET scan +/- brain MRI) for all asymptomatic melanoma patients with a positive sentinel lymph node biopsy. The true yield of these workups is unknown. We created cohorts of adult malignant melanoma patients, using the National Cancer Database (2012-2020) to mimic three common scenarios: (1) clinically node negative, with positive sentinel lymph node(s) (SLNB[+]); (2) clinically node negative, with negative sentinel lymph node(s) (SLNB[-]); (3) clinically node positive with confirmed lymph node metastases (cN[+] and pN[+]). Multivariable regression, supervised decision trees, and nomograms were constructed to assess the risk of metastases based on key features. 10,371 patients were SLNB[+], 55,172 were SLNB[-], and 4,012 were cN[+] and pN[+]. The proportion of patients with any metastatic disease (brain metastases) were as follows: SLNB[+]: 1.4% (0.3%); SLNB[-] 0.3% (<0.1%); cN[+] and pN[+] 11.6% (1.6%). On multivariable regression, Breslow depth > 4, ulceration, and lymphovascular invasion were associated with greater risk of metastatic disease. A supervised decision tree for SLNB[+] and SLNB[-] patients found the only groups with >2% risk of metastases were T4 tumors or T2/T3 tumors with ulceration and LVI. Most groups had a negligible risk (<0.1%) of brain metastases. This is the first large analysis to guide the use of imaging for cutaneous melanoma. Among clinically node negative patients, metastatic disease is uncommon and brain metastases are exceedingly rare. Further investigation could promote a tailored approach to metastatic workups guided by individual risk factors.

Gastrodin, a Promising Natural Small Molecule for the Treatment of Central Nervous System Disorders, and Its Recent Progress in Synthesis, Pharmacology and Pharmacokinetics.

Gastrodia elata Blume is a traditional medicinal and food homology substance that has been used for thousands of years, is mainly distributed in China and other Asian countries, and has always been distinguished as a superior class of herbs. Gastrodin is the main active ingredient of G. elata Blume and has attracted increasing attention because of its extensive pharmacological activities. In addition to extraction and isolation from the original plant, gastrodin can also be obtained via chemical synthesis and biosynthesis. Gastrodin has significant pharmacological effects on the central nervous system, such as sedation and improvement of sleep. It can also improve epilepsy, neurodegenerative diseases, emotional disorders and cognitive impairment to a certain extent. Gastrodin is rapidly absorbed and widely distributed in the body and can also penetrate the blood-brain barrier. In brief, gastrodin is a promising natural small molecule with significant potential in the treatment of brain diseases. In this review, we summarised studies on the synthesis, pharmacological effects and pharmacokinetic characteristics of gastrodin, with emphasis on its effects on central nervous system disorders and the possible mechanisms, in order to find potential therapeutic applications and provide favourable information for the research and development of gastodin.

Dual vision Transformer-DSUNET with feature fusion for brain tumor segmentation

Brain tumors are one of the leading causes of cancer death; screening early is the best strategy to diagnose and treat brain tumors. Magnetic Resonance Imaging (MRI) is extensively utilized for brain tumor diagnosis; nevertheless, achieving improved accuracy and performance, a critical challenge in most of the previously reported automated medical diagnostics, is a complex problem. The study introduces the Dual Vision Transformer-DSUNET model, which incorporates feature fusion techniques to provide precise and efficient differentiation between brain tumors and other brain regions by leveraging multi-modal MRI data. The impetus for this study arises from the necessity of automating the segmentation process of brain tumors in medical imaging, a critical component in the realms of diagnosis and therapy strategy. The BRATS 2020 dataset is employed to tackle this issue, an extensively utilized dataset for segmenting brain tumors. This dataset encompasses multi-modal MRI images, including T1-weighted, T2-weighted, T1Gd (contrast-enhanced), and FLAIR modalities. The proposed model incorporates the dual vision idea to comprehensively capture the heterogeneous properties of brain tumors across several imaging modalities. Moreover, feature fusion techniques are implemented to augment the amalgamation of data originating from several modalities, enhancing the accuracy and dependability of tumor segmentation. The Dual Vision Transformer-DSUNET model's performance is evaluated using the Dice Coefficient as a prevalent metric for quantifying segmentation accuracy. The results obtained from the experiment exhibit remarkable performance, with Dice Coefficient values of 91.47 % for enhanced tumors, 92.38 % for core tumors, and 90.88 % for edema. The cumulative Dice score for the entirety of the classes is 91.29 %. In addition, the model has a high level of accuracy, roughly 99.93 %, which underscores its durability and efficacy in segmenting brain tumors. Experimental findings demonstrate the integrity of the suggested architecture, which has quickly improved the detection accuracy of many brain diseases.

Key Challenges, Influencing Factors, and Future Perspectives of Nanosuspensions in Enhancing Brain Drug Delivery.

Many brain diseases pose serious challenges to human life. Alzheimer's Disease (AD) and Parkinson's Disease (PD) are common neurodegenerative diseases that seriously threaten human health. Glioma is a common malignant tumor. However, drugs cannot cross physiological and pathological barriers and most therapeutic drugs cannot enter the brain because of the presence of the Blood-brain Barrier (BBB) and Bloodbrain Tumor Barrier (BBTB). How to enable drugs to penetrate the BBB to enter the brain, reduce systemic toxicity, and penetrate BBTB to exert therapeutic effects has become a challenge. Nanosuspension can successfully formulate drugs that are difficult to dissolve in water and oil by using surfactants as stabilizers, which is suitable for the brain target delivery of class II and IV drugs in the Biopharmaceutical Classification System (BCS). In nanosuspension drug delivery systems, the physical properties of nanostructures have a great impact on the accumulation of drugs at the target site, such as the brain. Optimizing the physical parameters of the nanosuspension can improve the efficiency of brain drug delivery and disease treatment. Therefore, the key challenges, influencing factors, and future perspectives of nanosuspension in enhancing brain drug delivery are summarized and reviewed here. This article aims to provide a better understanding of nanosuspension formulation technology used for brain delivery and strategies used to overcome various physiological barriers.

3D model for human glia conversion into subtype-specific neurons, including dopamine neurons

Two-dimensional neuronal cultures have a limited ability to recapitulate the invivo environment of the brain. Here, we introduce a three-dimensional invitro model for human glia-to-neuron conversion, surpassing the spatial and temporal constrains of two-dimensional cultures. Focused on direct conversion to induced dopamine neurons (iDANs) relevant to Parkinson disease, the model generates functionally mature iDANs in 2weeks and allows long-term survival. As proof of concept, we use single-nucleus RNA sequencing and molecular lineage tracing during iDAN generation and find that all glial subtypes generate neurons and that conversion relies on the coordinated expression of three neural conversion factors. We also show the formation of mature and functional iDANs over time. The model facilitates molecular investigations of the conversion process to enhance understanding of conversion outcomes and offers a system for invitro reprogramming studies aimed at advancing alternative therapeutic strategies in the diseased brain.

Disease-modifying Drugs for non-Alzheimer Dementias

Neurodegenerative diseases represent the most common cause of dementia. Protein aggregation is upstream in the pathological mechanisms and is a therapeutic target in the development of disease-modifying drugs in this patient population. Notably, α-synuclein or DNA-binding protein of 43kDa (TDP-43) is commonly involved in the pathomechanisms that contribute to non-Alzheimer neurodegenerative diseases. Several immunotherapy clinical trials on α-synuclein have progressed to phase 2, and small-molecule therapeutics are ongoing. With regard to TDP-43, immunotherapies that target protein aggregates are currently being developed, and research is underway to investigate several drugs that target the associated causative gene. Further research is warranted for deeper insight into both disease-modifying drugs; biomarker tests need to be developed to determine their efficacy. However, both proteins aggregate and accumulate in the brain in many neurodegenerative diseases and dementia; therefore, they are therapeutically significant, and future progress is expected in research and development.

Development of a nanomachine for efficient drug delivery to the brain

Recently, bottom-up technologies, in particular the utilization of self-assembly of functional polymers to form nanostructures in solutions have been collecting attention. These technologies are being explored for various applications, especially for usage in therapeutics. One of the goals of such studies is to develop a drug delivery system (DDS) that delivers bioactive substances to specific targets within our body, eliciting the desired functionality. The authors have been developing "nanomachines" using biocompatible polymers to safely and efficiently deliver drugs mainly to tumors. The aim of this study is to utilize our expertise in designing a nanomachine to develop a cutting-edge nanomachine that can efficiently penetrate the blood-brain barrier (BBB) and deliver drugs to the brain parenchyma. Furthermore, leveraging this "nanomachine" technology, the authors are advancing the "Hayabusa Nanomachine," which can non-invasively collect and detect brain molecules, correlating them with various biological processes, ultimately leading to a better understanding of brain function and diseases. This paper also introduces the concept and ongoing efforts to the development of "Hayabusa Nanomachines," which have the potential to revolutionize existing approaches in this field.

Brain organoids: A new tool for modelling of neurodevelopmental disorders.

Neurodevelopmental disorders are mostly studied using mice as models. However, the mouse brain lacks similar cell types and structures as those of the human brain. In recent years, emergence of three-dimensional brain organoids derived from human embryonic stem cells or induced pluripotent stem cells allows for controlled monitoring and evaluation of early neurodevelopmental processes and has opened a window for studying various aspects of human brain development. However, such organoids lack original anatomical structure of the brain during maturation, and neurodevelopmental maturation processes that rely on unique cellular interactions and neural network connections are limited. Consequently, organoids are difficult to be used extensively and effectively while modelling later stages of human brain development and disease progression. To address this problem, several methods and technologies have emerged that aim to enhance the sophisticated regulation of brain organoids developmental processes through bioengineering approaches, which may alleviate some of the current limitations. This review discusses recent advances and application areas of human brain organoid culture methods, aiming to generalize optimization strategies for organoid systems, improve the ability to mimic human brain development, and enhance the application value of organoids.

Choroid plexus enlargement correlates with periventricular pathology but not with disease activity in radiologically isolated syndrome

Background: Choroid plexus (ChP) enlargement is an emerging radiological biomarker in multiple sclerosis (MS). Objectives: This study aims to assess ChP volume in a large cohort of patients with radiologically isolated syndrome (RIS) versus healthy controls (HC) and explore its relationship with other brain volumes, disease activity, and biological markers. Methods: RIS individuals were included retrospectively and compared with HC. ChPs were automatically segmented using an in-house automated algorithm and manually corrected. Results: A total of 124 patients fulfilled the 2023 RIS criteria, and 55 HCs were included. We confirmed that ChPs are enlarged in RIS versus HC (mean (±SD) normalized ChP volume: 17.24 (±4.95) and 11.61 (±3.58), respectively, p < 0.001). Larger ChPs were associated with more periventricular lesions (ρ = 0.26; r2 = 0.27; p = 0.005 for the correlation with lesion volume, and ρ = 0.2; r2 = 0.21; p = 0.002 for the correlation with lesion number) and lower thalamic volume (ρ = −0.38; r2 = 0.44; p < 0.001), but not with lesions in other brain regions. Conversely, ChP volume did not correlate with biological markers. No significant difference in ChP volume was observed between subjects who presented or did not have a clinical event or between those with or without imaging disease activity. Conclusions: This study provides evidence that ChP volume is higher in RIS and is associated with measures reflecting periventricular pathology but does not correlate with biological, radiological, or clinical markers of disease activity.

When Dreams Foreshadow Brain Disease: Acting out dreams is one of the earliest signs of Parkinson's disease.

When Dreams Foreshadow Brain Disease: Acting out dreams is one of the earliest signs of Parkinson's disease.

Accurate Whole-Brain Image Enhancement for Low-Dose Integrated PET/MR Imaging Through Spatial Brain Transformation.

Positron emission tomography/magnetic resonance imaging (PET/MRI) systems can provide precise anatomical and functional information with exceptional sensitivity and accuracy for neurological disorder detection. Nevertheless, the radiation exposure risks and economic costs of radiopharmaceuticals may pose significant burdens on patients. To mitigate image quality degradation during low-dose PET imaging, we proposed a novel 3D network equipped with a spatial brain transform (SBF) module for low-dose whole-brain PET and MR images to synthesize high-quality PET images. The FreeSurfer toolkit was applied to derive the spatial brain anatomical alignment information, which was then fused with low-dose PET and MR features through the SBF module. Moreover, several deep learning methods were employed as comparison measures to evaluate the model performance, with the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and Pearson correlation coefficient (PCC) serving as quantitative metrics. Both the visual results and quantitative results illustrated the effectiveness of our approach. The obtained PSNR and SSIM were 41.96 ± 4.91 dB (p < 0.01) and 0.9654 ± 0.0215 (p < 0.01), which achieved a 19% and 20% improvement, respectively, compared to the original low-dose brain PET images. The volume of interest (VOI) analysis of brain regions such as the left thalamus (PCC = 0.959) also showed that the proposed method could achieve a more accurate standardized uptake value (SUV) distribution while preserving the details of brain structures. In future works, we hope to apply our method to other multimodal systems, such as PET/CT, to assist clinical brain disease diagnosis and treatment.

Multi-Task Collaborative Pre-Training and Adaptive Token Selection: A Unified Framework for Brain Representation Learning.

Structural magnetic resonance imaging (sMRI) reveals the structural organization of the brain. Learning general brain representations from sMRI is an enduring topic in neuroscience. Previous deep learning models neglect that the brain, as the core of cognition, is distinct from other organs whose primary attribute is anatomy. Capturing the high-level representation associated with inter-individual cognitive variability is key to appropriately represent the brain. Given that this cognition-related information is subtle, mixed, and distributed in the brain structure, sMRI-based models need to both capture fine-grained details and understand how they relate to the overall global structure. Additionally, it is also necessary to explicitly express the cognitive information that implicitly embedded in local-global image features. Therefore, we propose MCPATS, a brain representation learning framework that combines Multi-task Collaborative Pre-training (MCP) and Adaptive Token Selection (ATS). First, we develop MCP, including mask-reconstruction to understand global context, distort-restoration to capture fine-grained local details, adversarial learning to integrate features at different granularities, and age-prediction, using age as a surrogate for cognition to explicitly encode cognition-related information from local-global image features. This co-training allows progressive learning of implicit and explicit cognition-related representations. Then, we develop ATS based on mutual attention for downstream use of the learned representation. During fine-tuning, the ATS highlights discriminative features and reduces the impact of irrelevant information. MCPATS was validated on three different public datasets for brain disease diagnosis, outperforming competing methods and achieving accurate diagnosis. Further, we performed detailed analysis to confirm that the MCPATS-learned representation captures cognition-related information.

Curcumin attenuates neuroinflammatory damage induced by LPS: implications for the role of S100B

Brain disease results in inflammatory damage that alters cell function in microglia and astrocytes as well as other neuronal cell types. Astrocytes modulate blood flow, regulate glutamate metabolism, and exert antioxidant protection. When responding to inflammatory damage, astrocytes enhance immune cell infiltration and amplify inflammatory responses via the upregulation of cytokine production. Several molecules have been proposed to attenuate neuroinflammation and control neurological diseases. Curcumin gained attention due to its capacity to cross the blood-brain barrier and its well-described anti-inflammatory and antioxidant activities. Our study aimed to understand if oral curcumin administration could protect against central nervous system inflammatory damage induced by intracerebroventricular injection of LPS while focusing on astrocyte function. Despite its poor bioavailability, we found that curcumin reaches the central nervous system, prevents the locomotory damage caused by LPS, and reduces inflammatory signaling via IL-1β and COX-2. Furthermore, we observed that curcumin was protective against LPS-induced S100B secretion in the cerebrospinal fluid and GSH reduction in the hippocampal tissue. However, curcumin could not protect the animals from anhedonia, assessed by the sucrose preference test, and weight loss induced by LPS. Our results indicate that oral curcumin administration exerts a protective anti-inflammatory action in the central nervous system, attenuating the sickness behavior induced by ICV LPS. This work demonstrates that curcumin has an important modulative effect on astrocytes, thus suggesting that astrocytes are critical to the anti-inflammatory effects of curcumin.