Brain Research Research Articles

Sex differences in the human brain related to visual motion perception.

Previous studies have found that the temporal duration required for males to perceive visual motion direction is significantly shorter than that for females. However, the neural correlates of such shortened duration perception remain yet unclear. Given that motion perception is primarily associated with the neural activity of the middle temporal visual complex (MT+), we here test the novel hypothesis that the neural mechanism of these behavioral sex differences is mainly related to the MT+ region. We utilized ultra-high field (UHF) MRI to investigate sex differences in the MT+ brain region. A total of 95 subjects (48 females) participated in two separate studies. Cohort 1, consisting of 33 subjects (16 females), completed task-fMRI (drafting grating stimuli) experiment. Cohort 2, comprising 62 subjects (32 females), engaged in a psychophysical experiment measuring motion perception along different temporal thresholds as well as conducting structural and functional MRI scanning of MT+. Our findings show pronounced sex differences in major brain parameters within the left MT+ (but not the right MT+, i.e., laterality). In particular, males demonstrate (i) larger gray matter volume (GMV) and higher brain's spontaneous activity at the fastest infra-slow frequency band in the left MT+; and (ii) stronger functional connectivity between the left MT+ and the left centromedial amygdala (CM). Meanwhile, both female and male participants exhibited comparable correlations between motion perception ability and the multimodal imaging indexes of the MT+ region, i.e., larger GMV, higher brain's spontaneous activity, and faster motion discrimination. Our findings reveal sex differences of imaging indicators of structure and function in the MT+ region, which also relate to the temporal threshold of motion discrimination. Overall, these results show how behavioral sex differences in visual motion perception are generated, and advocate considering sex as a crucial biological variable in both human brain and behavioral research.

Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme.

Neuroscience education is challenged by rapidly evolving technology and the development of interdisciplinary approaches for brain research. The Human Brain Project (HBP) Education Programme aimed to address the need for interdisciplinary expertise in brain research by equipping a new generation of researchers with skills across neuroscience, medicine, and information technology. Over its ten year duration, the programme engaged over 1,300 experts and attracted more than 5,500 participants from various scientific disciplines in its blended learning curriculum, specialised schools and workshops, and events fostering dialogue among early-career researchers. Key principles of the programme's approach included fostering interdisciplinarity, adaptability to the evolving research landscape and infrastructure, and a collaborative environment with a focus on empowering early-career researchers. Following the programme's conclusion, we provide here an analysis and in-depth view across a diverse range of educational formats and events. Our results show that the Education Programme achieved success in its wide geographic reach, the diversity of participants, and the establishment of transversal collaborations. Building on these experiences and achievements, we describe how leveraging digital tools and platforms provides accessible and highly specialised training, which can enhance existing education programmes for the next generation of brain researchers working in decentralised European collaborative spaces. Finally, we present the lessons learnt so that similar initiatives may improve upon our experience and incorporate our suggestions into their own programme.

Ageing and brain research networks in Norway

The global population is ageing rapidly with over 1.6 billion people forecast to be over 65 by 2050. While this ‘crisis of ageing’ builds, medical research is rushing to prepare to meet the expected increase in the number of patients, especially those with dementia, including Alzheimer’s disease. With the growth of the digital world, sharing of information and resources has come into focus as one way to help meet the crisis through creating positive collaborative working environments. In Norway, particularly two networks on ageing research have grown through the need for connectivity and collaboration, NO-Age and NO-AD. Their growth, and the growth of international collaborative environments, will help researchers seek for the keys to longer, healthier lives for older people around the world.

Direct visualization of microwires in hybrid depth electrodes using high-resolution photon-counting CT.

Hybrid depth electrodes are increasingly being used for epilepsy monitoring and human neurophysiology research. Microwires extending from the tip of the Behnke-Fried (BF) electrode into (sub)cortical areas allow to isolate single neurons and perform microstimulation. Conventional CT or MRI visualize the entire microwire bundle as an artifact extending from the BF electrode tip with low resolution, without proper identification of individual microwires. We illustrate the first direct visualization method of individual microwires using high-resolution photon-counting CT (PCCT). Coregistration of the PCCT scan with a preoperative MRI can visualize individual wires directly in cortex, which is an advantage as it provides feedback on the accuracy of the implantation method and can guide future implantations. This PCCT technique allows for accurately depicting individual microwires which could be relevant for neuroscientific research through improved visualization and implantation of specific cortical and subcortical brain areas. PLAIN LANGUAGE SUMMARY: Researchers are using hybrid depth electrodes to study epilepsy and brain activity. These electrodes, called Behnke-Fried (BF) electrodes, have microwires at the tip that can record single neurons and stimulate brain areas. Regular CT or MRI scans do not show the individual microwires clearly. The authors use a new high-resolution photon-counting CT (PCCT) technique, which can show each individual microwire in the brain. By combining PCCT with MRI, the authors can precisely see where the microwires are located. This could improve future implantation surgeries and brain research.

Neuropeptide Y and Pain: Insights from Brain Research

Neuropeptide Y and Pain: Insights from Brain Research

Managing clinical heterogeneity in psychopathology: Perspectives from brain research.

Clinical heterogeneity is a significant factor to contend with when seeking to organize, understand, and treat psychopathology. In recent years, the field has prioritized efforts to minimize nonmeaningful heterogeneity and leverage meaningful heterogeneity to improve assessment and diagnostics, inform mechanistic understanding, and facilitate the development of novel treatments. Indeed, exciting developments such as the National Institute for Mental Health Research Domain Criteria and the Hierarchical Taxonomy of Psychopathology have provided powerful frameworks for facing clinical complexity. While these developments have spurred many advancements, the movement has yet to effectively harness the tremendous potential provided by the brain. Initial work incorporating brain data has focused on validating clinical observations with a biomarker rather than leveraging the brain to provide unique insight into meaningful clinical heterogeneity. To provide future guidance and examples of innovation in the area, we solicited articles from teams seeking to utilize brain research to manage clinical heterogeneity. The search resulted in a diverse illustration of how best to leverage brain data to greater mechanistic understanding and clinical utility. In this introduction, we consider this work and discuss strategies through which brain data can best be used to provide unique insight into clinical heterogeneity. As the science of psychopathology continues to grapple with the promise and costs inherent in utilizing this powerful and complex array of methodologies, it will be important to leverage unique insights from brain science. This special issue provides a useful guide for new and upcoming work and a catalyst for moving the field forward. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Mindfulness dalam Perspektif Neuropsikologi: Analisis Bibliometrik

Mindfulness is gaining prominence in academic community particularly in neuropsychology. This research aims to conduct a bibliometric analysis of neuropsychology and brain research on mindfulness. 929 documents from Scopus database were analyzed with Bibliometrix software. The retrieved data regarding institutions, journal titles, countries, authors, and keywords. The analysis is focused on the research trend, the performance, and the collaboration network. The result indicates that neuropsychology and brain research on mindfulness were conducted from 1994 – 2023. The highest peak in the total production of neuropsychology research articles on mindfulness is 2022 with 142 documents. In addition, the data shows that certain authors, countries, and institutions contribute greatly to neuropsychology research on mindfulness. There are social interactions among authors, institutions, and countries. Future trends in this area of research will focus on topics related to depression, meditation, psychotherapy, and executive function. The results of this study provide insight into the growing trends in neuropsychology studies on mindfulness and data to assist researchers in identifying research gaps.

Plant Disease Detection Using Machine Learning

In this project, we have developed a plant disease detection model using a machine learning approach on a dataset named “Plant Disease Expert” which is available on Kaggle. The dataset contains 199,611 images across 58 subdirectories. The splitting of the dataset in training, validation and testing is done in ratio 90:5:5. The data preprocessing included resizing the images, normalizing pixel values and applying augmentation techniques. We have used different libraries throughout the project such as NumPy, Pandas, Operating system, Time, Matplotlib, OpenCV, Shutil, TensorFlow, Keras, Seaborn, etc. EfficientNetB3 is used as the base model. EfficientNetB3 is a convolutional neural network architecture developed by Google Brain researchers [1]. It is known for its efficiency and effectiveness in image classification tasks. Additional custom layers such as max pooling, batch normalization, dense layers, and activation functions are used to optimize the performance. The adamax optimizer and categorical cross-entropy were used during training. The model was trained for 10 epochs with a batch size of 20 and a learning rate of 0.01. For evaluation, classification report, accuracy, precision, recall, F1-score and support were used. The model achieved an accuracy of 98.76% [1].

Policy Actions Required to Improve Nutrition for Brain Health.

Brain health is a pressing global concern. Poor diet quality is a recognized major environmental risk factor for brain disorders and one of the few that is modifiable. There is substantial evidence that nutrition impacts brain development and brain health across the life course. So why then is the full potential of nutrition not utilized to improve brain function? This commentary, which is based on discussions of the European Brain Research Area BRAINFOOD cluster, aims to highlight the most urgent research priorities concerning the evidence base in the area of nutrition and brain health and identifies 3 major issues that need to be addressed: (1) increase causal and mechanistic evidence on the link between nutrition and brain health, (2) produce effective messages/education concerning the role of food for brain health, and (3) provide funding to support collaborative working across diverse stakeholders.

Human TRPV1 is an efficient thermogenetic actuator for chronic neuromodulation

Thermogenetics is a promising neuromodulation technique based on the use of heat-sensitive ion channels. However, on the way to its clinical application, a number of questions have to be addressed. First, to avoid immune response in future human applications, human ion channels should be studied as thermogenetic actuators. Second, heating levels necessary to activate these channels in vivo in brain tissue should be studied and cytotoxicity of these temperatures addressed. Third, the possibility and safety of chronic neuromodulation has to be demonstrated. In this study, we present a comprehensive framework for thermogenetic neuromodulation in vivo using the thermosensitive human ion channel hTRPV1. By targeting hTRPV1 expression to excitatory neurons of the mouse brain and activating them within a non-harmful temperature range with a fiber-coupled infrared laser, we not only induced neuronal firing and stimulated locomotion in mice, but also demonstrated that thermogenetics can be employed for repeated neuromodulation without causing evident brain tissue injury. Our results lay the foundation for the use of thermogenetic neuromodulation in brain research and therapy of neuropathologies.

Exploring the Impact of Sex and Gender in Brain Function: Implications and Considerations.

Sex and gender are crucial variables in understanding brain development and disease. Biological sex is determined by genetic and hormonal factors, whereas gender is a multidimensional construct shaped by social and cultural influences. The interplay of these factors contributes to sex-specific susceptibilities and disease progression in psychiatric and neurological disorders. However, sex and gender are often considered as a single variable, which can lead to biased data analysis and interpretation. This commentary aims to analyze how sex and gender influence brain structure and function, with implications for personalized medicine, research, and the development of gender-sensitive clinical guidelines. Findings from various studies employing neuroimaging techniques and animal models are discussed, as well as the impact of biological sex, gender, environmental, cultural, and social factors on brain development, organization, and behavior. Evidence suggests that sex differences in brain structure and function are not only genetically determined but arealso influenced by gender-related experiences and societal contexts. Importantly, discrepancies between male and female brains are reduced in gender-equal societies. Preclinical studies play a pivotal role in determining the influence of biological sex, independent of gender, in different disease models. The findings underscore the need to consider both sex and gender in research and clinical practice to avoid biases and promote equitable health outcomes. Moving forward, we advocate for gender-sensitive approaches to be integrated into brain research and in clinical guidelines to achieve personalized and precision medicine.

Testing Visual Function by Assessment of the Optomotor Reflex in Glaucoma.

Optomotor response/reflex (OMR) is a fast and efficient first-in-line visual screening method, especially for rodents. It has the potential to evaluate both the scotopic and photopic visions of nonrestrained animals through tracking head movement, providing a quantitative estimate of visual functions. In restrained animals, optokinetic response (OKR), compensatory eye movements for visual shifts in the surroundings, is utilized. Both OMR and OKR capitalize on an individual's innate reflex to stabilize images for the purpose of capturing clear vision. The two reflexes have similar reliability when evaluating stimulus luminance, contrast, spatial frequency, and velocity. They have emerged as powerful tools to evaluate the efficacy of pharmacological treatments and phenotypes of subjects undergoing study. With OMR and OKR accurately assessing visual acuity (VA) as well as contrast sensitivity (CS), the gold standards for measuring clinical vision, they provide reliable and easily accessible results that further eye and brain research. These methods of sight evaluation have been used in multiple animal models, particularly mice and zebrafish. Through OMR assays, these animal models have been utilized to investigate retinal degenerative diseases, helping researchers differentiate between worsening stages. Alongside tests such as optical coherence tomography (OCT), OMR provides confirmation of visual status, where increased OMR function often correlates with improved visual status. OMR has continued to be used outside of glaucoma in various retinal diseases, such as retinitis pigmentosa (RP), diabetic retinopathy, and age-related macular degeneration.In this chapter, we will introduce the concept and application of visual stimulus-induced head or eye reflex movement in different animal species and experimental models of eye diseases, such as glaucoma and other neurodegenerative disorders, and in patients with glaucoma.

DBAII-Net with multiscale feature aggregation and cross-modal attention for enhancing infant brain injury classification in MRI.

MRI is pivotal in diagnosing brain injuries in infants. However, the dynamic development of the brain introduces variability in infant MRI characteristics, posing challenges for MRI-based classification in this population. Furthermore, manual data selection in large-scale studies is labor-intensive, and existing algorithms often underperform with thick-slice MRI data. To enhance research efficiency and classification accuracy in large datasets, we propose an advanced classification model. We introduce the Dual-Branch Attention Information Interactive Neural Network (DBAII-Net), a cutting-edge model inspired by radiologists' use of multiple MRI sequences. DBAII-Net features two innovative modules: (1) the Convolutional Enhancement Module (CEM), which leverages advanced convolutional techniques to aggregate multi-scale features, significantly enhancing information representation; and (2) the Cross-Modal Attention Module (CMAM), which employs state-of-the-art attention mechanisms to fuse data across branches, dramatically improving positional and channel feature extraction. Performances (accuracy, sensitivity, specificity, area under the curve, etc.) of DBAII-Net were compared with eight benchmark models for brain MRI classification in infants aged 6 months to 2 years. Utilizing a self-constructed dataset of 240 thick-slice brain MRI scans (122 with brain injuries, 118 without), DBAII-Net demonstrated superior performance. On a test set of approximately 50 cases, DBAII-Net achieved average performance metrics of 92.53% accuracy, 90.20% sensitivity, 94.93% specificity, and an area under the curve (AUC) of 0.9603. Ablation studies confirmed the effectiveness of CEM and CMAM, with CMAM significantly boosting classification metrics. DBAII-Net with CEM and CMAM outperforms existing benchmarks in enhancing the precision of brain MRI classification in infants, significantly reducing manual effort in infant brain research. Our code is available at https://github.com/jiazhen4585/DBAII-Net.

Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons.

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson's, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research.

The African Initiative for Bioinformatics Online Training in Neurodegenerative Diseases (AI‐BOND): Investing in the next generation of African neuroscientists

AbstractNeurodegenerative disorders, including Alzheimer's disease and AD‐related dementias (AD/ADRD), pose significant challenges to health care systems globally, particularly in Africa. With the advances in medical technology and research capabilities, especially in next‐generation sequencing and imaging, vast amounts of data have been generated from AD/ADRD research. Given that the greatest increase in AD/ADRD prevalence is expected to occur in Africa, it is critical to establish comprehensive bioinformatics training programs to help African scientists leverage existing data and collect additional information to untangle AD/ADRD heterogeneity in African populations. The South Texas Alzheimer's Disease Research Center, with efforts from the National Institutes of Health and the Global Brain Health Institute, has partnered with the Brain Research Africa Initiative to develop the African Initiative on Bioinformatics Online Training in Neurodegenerative Disease (AI‐BOND). AI‐BOND is a comprehensive and accessible training program, the aim of which is to advance biostatistics and bioinformatics expertise in Africa in studying neurodegenerative diseases. This expertise is essential to enable African scientists to utilize the extensive AD/ADRD data and enhance the continent's ability to contribute to global research efforts in this field. The training addresses the gap in analyzing neurodegenerative disease data by providing skills and knowledge in genetic epidemiology, biostatistics, and bioinformatics to African students and researchers. This innovative online training program will last 6 months and provide training in skill sets R, SAS, and Python programing, genome‐wide association studies, genomics, transcriptomics, proteomics, metabolomics, microbiome analysis, and advanced statistical methods. Additional training will include study design and manuscript and grant writing. The first cohort of the AI‐BOND program will graduate in June 2024. The AI‐BOND program is expected to build research computational capacities in Africa that will improve the ability of graduates to conduct and utilize large‐scale studies, with the goal of curbing the growing incidence of neurodegenerative diseases in Africa.Highlights Alzheimer's disease (AD) and AD‐related dementias (ADRD) pose significant health challenges globally, particularly in Africa. The most significant AD/ADRD prevalence increase is predicted to occur in Africa. It is crucial to establish a bioinformatics training capacity in Africa to leverage the vast number of multi‐omics and imaging biomarkers of AD/ADRD data being generated. The African Initiative on Bioinformatics Online Training in Neurodegenerative Disease (AI‐BOND) training addresses the gaps in study design, biostatistics, genetic epidemiology, and bioinformatics related to neurodegenerative diseases in Africa. The success of AI‐BOND is anticipated to help build computational research capacities in Africa.

Scalp surface Laplacian potential monitoring system based on novel hydrogel active tri-polar concentric ring electrodes

Electroencephalography is valued in brain research for its high temporal resolution and user-friendly nature. However, limitations in spatial resolution and susceptibility to biological artifacts restrict its broader application. The surface Laplacian potential obtained through tri-polar concentric ring electrodes offers a solution by mitigating biological artifact interference and enhancing spatial resolution while preserving essential information. In this study, an active tri-polar concentric ring electrode based on conductive hydrogel and a 3-lead surface Laplacian potential monitoring system designed for use with the electrode were proposed. The part of the electrode that came into contact with the human body was the hydrogel with high electrical conductivity. The contact surface curvature was designed based on the real forehead curvature of 22 subjects, resulting in a normalized electrode-skin contact impedance of 30.95±16.76KΩ*cm2 at 10 Hz, which was lower than commercial disposable wet electrodes and remained stable over a 10-hour period of long-term wear. And the electrode still exhibited excellent performance after being left for 20 days. Additionally, the internal noise of the system was about 1.9μV, which was comparable to that of the FDA-approved equipment. The signal analysis results demonstrated that the surface Laplacian potential collected by the proposed electrode and system had better signal-to-noise ratio and spatial resolution, and had a good suppression effect on biological artifacts.

Tract-specific magnetization transfer ratio provides insights into the severity of degenerative cervical myelopathy.

Cross-sectional study. This study's goal is to report whether Magnetization Transfer Ratio (MTR) can evaluate the severity of white matter (WM) injury in degenerative cervical myelopathy (DCM). Laureate Institute of Brain Research, USA; Department of Neurosurgery, University of Oklahoma Health Sciences Center, USA. 27 DCM patients were aged-matched with 20 healthy controls (HC) and categorized into treatment groups based on modified Japanese Orthopedic Association (mJOA) severity (11 mild and 16 moderate/severe). Regional and tract MTRs were extracted from the two vertebral levels containing maximum compression within magnetization transfer images. MTR differences between groups were assessed using a one-way ANOVA or Kruskal-Wallis test. The association between MTR and mJOA measures was evaluated using Spearman's correlation. Significant decreases in MTR were found between HC and moderate/severe groups in the overall (p = 0.0065) and ventral (p = 0.0009) WM regions; and ventral corticospinal (p = 0.0101), ventral reticulospinal (p = 0.0084), spinal lemniscus (p = 0.0079), and fasciculus cuneatus (p = 0.0219) tracts. The spinal lemniscus MTR also significantly decreased between HC and mild groups (p = 0.038). Ventral reticulospinal tract MTR correlated with upper (r = 0.439; p = 0.022) and lower (r = 0.386; p = 0.047) limb motor mJOA scores. Significant tract-based MTR changes and correlations align with known DCM symptoms, are demonstrated to be lost at the regional level, and display the inhomogeneous compressive damage occurring within DCM spinal cords.

Changing Your Love of Music by Stimulating the Brain

For many of us, listening to our favorite songs makes us feel happy, energetic, and inspired. One goal of brain research is to understand the role of the brain as we listen to music. For years, researchers have hypothesized that the brain areas involved in processing the music we hear are involved in musical pleasure—but they had no hard proof. By stimulating people’s brains using strong magnets, along with a brain-imaging technique that could see which areas of the brain were active, scientists have unraveled why we feel so awesome when we listen to our favorite jams. Now there is good evidence to show that our favorite tunes involve connections between specific brain areas, including the brain’s “reward center”—an area that makes us feel good when we do something that we like. These brain circuits provide a solid piece to this complex puzzle of why music makes us feel so good.

Cortical oscillations are modified by expertise in dance and music: Evidence from live dance audience.

Over the past decades, the focus of brain research has expanded from using strictly controlled stimuli towards understanding brain functioning in complex naturalistic contexts. Interest has increased in measuring brain processes in natural interaction, including classrooms, theatres, concerts and museums to understand the brain functions in the real world. Here, we examined how watching a live dance performance with music in a real-world dance performance setting engages the brains of the spectators. Expertise in dance or music has been shown to modify brain functions, including when watching dance or listening to music. Therefore, we recorded electroencephalography (EEG) from an audience of dancers, musicians and novices as they watched the live dance performance and analysed their cortical oscillations. We compared intrabrain oscillations when participants watched the performance (with music) or listened to the music alone without the dance. We found that dancers have stronger fronto-central and parieto-occipital theta phase synchrony (4-8Hz) than novices when watching dance, likely reflecting the effects of dance experience on motor imagery, multisensory and social interaction processes. Also, compared with novices, dancers had stronger delta phase synchrony (0.5-4Hz) when listening to music, and musicians had stronger delta phase synchrony when watching dance, suggesting expertise in music and dance enhances sensitivity or attention to temporal regularities in movement and sound.

Mapping knowledge domain of acupuncture for Parkinson's disease: a bibliometric and visual analysis.

This study points to probing the inclination and mapping knowledge domain of acupuncture for Parkinson's disease through bibliometrics. A search was conducted on 1 February 2024 using the Web of Science to identify papers published on acupuncture for Parkinson's disease. The analysis included scientific research, countries, organizations, authors/cited authors, keywords, journals, and cited references. Bibliometric data were analyzed using VOSviewer software, CiteSpace, GraphPad Prism, and Scimago Graphica. The studies on acupuncture for Parkinson's disease were visualized as a network map according to the publication year. The cumulative publication trend on acupuncture for Parkinson's disease is increasing year by year. China is the leading contributor in this field. International collaboration is predominantly concentrated in Europe, while institutional collaboration is chiefly limited to Chinese universities specializing in traditional Chinese medicine. Park HJ is the most prolific author, with "Movement Disorders" being the journal with the most publications. "Brain Research" is identified as a key journal, reflecting a focus on neuroscience. Kim SN is the most cited author, while Eisenberg DM is a prominent author in this field. Research topics such as mouse models, systematic reviews, and non-motor symptoms are frequently explored, with messenger RNA of substantia nigra emerging as a notable keyword in this field. Choi YG's 2009 paper, published in the Neuroscience Letters journal, is a critical reference in this field. Key papers include Eisenberg DM's 1998 study on randomized trials of acupuncture for non-motor symptoms of PD, as well as research focusing on the neuroinflammatory regulatory mechanisms of acupuncture for PD. The bibliometric analysis offers an exhaustive generality of the advancement and worldwide trends in acupuncture treatments for Parkinson's disease, shedding light on potential avenues for prospective research.