Pub Date : 2024-06-14DOI: 10.3389/fnins.2024.1407301
Yida Wang, Sile Chang, Dongxu Chen
Despite this growing interest, there remains a lack of comprehensive and systematic bibliometric analyses of ketamine research. This study aimed to summarize the progress in ketamine research through bibliometric analysis, providing insights into the development and direction of the field.Publications related to ketamine were retrieved from the Web of Science Core Collection (WoSCC) database on February 15, 2024. In conducting a comprehensive bibliometric analysis, a variety of bibliographic elements were meticulously collected to map the landscape of research within a specific field.Between January 1, 2014, and December 31, 2023, a total of 10,328 articles on ketamine research were published across 1,752 academic journals by 45,891 authors from 8,914 institutions in 128 countries. The publication volume has shown a steady increase over this period. The United States of America (USA) and the People’s Republic of China lead in both publication and citation counts. The National Institute of Mental Health (NIMH) and Yale University emerge as the most active institutions in this research domain. Carlos Zarate of the NIH National Institute of Mental Health was noted for the highest number of significant publications and received the most co-citations. The analysis revealed key research themes including mechanism of action, adverse events, psychiatric applications, and perioperative implications.This study provided comprehensive bibliometric and knowledge mapping analysis of the global ketamine research landscape, offering valuable insights into the trends, key contributors, and thematic focus areas within the field. By delineating the evolution of ketamine research, this study aims to guide future scholarly endeavors and enhance our understanding of ketamine’s therapeutic potential.
尽管人们对氯胺酮的兴趣与日俱增，但仍然缺乏对氯胺酮研究进行全面系统的文献计量分析。本研究旨在通过文献计量分析总结氯胺酮研究的进展，为该领域的发展和方向提供见解。研究人员于2024年2月15日从Web of Science Core Collection（WoSCC）数据库中检索了与氯胺酮相关的文献。2014年1月1日至2023年12月31日期间，来自128个国家8,914个机构的45,891位作者在1,752种学术期刊上发表了10,328篇有关氯胺酮研究的文章。在此期间，发表量呈现稳步增长趋势。美利坚合众国（美国）和中华人民共和国的论文发表量和被引用次数均居首位。美国国立精神卫生研究所（NIMH）和耶鲁大学成为该研究领域最活跃的机构。美国国立卫生研究院（NIH）国家精神卫生研究所的卡洛斯-扎拉特（Carlos Zarate）发表的重要论文数量最多，获得的共同引用次数也最多。这项研究对全球氯胺酮研究领域进行了全面的文献计量和知识图谱分析，为了解该领域的发展趋势、主要贡献者和主题重点领域提供了宝贵的见解。通过描述氯胺酮研究的发展历程，本研究旨在为未来的学术研究提供指导，并增进我们对氯胺酮治疗潜力的了解。
{"title":"Research trends and hotspots of ketamine from 2014 to 2023: a bibliometric analysis","authors":"Yida Wang, Sile Chang, Dongxu Chen","doi":"10.3389/fnins.2024.1407301","DOIUrl":"https://doi.org/10.3389/fnins.2024.1407301","url":null,"abstract":"Despite this growing interest, there remains a lack of comprehensive and systematic bibliometric analyses of ketamine research. This study aimed to summarize the progress in ketamine research through bibliometric analysis, providing insights into the development and direction of the field.Publications related to ketamine were retrieved from the Web of Science Core Collection (WoSCC) database on February 15, 2024. In conducting a comprehensive bibliometric analysis, a variety of bibliographic elements were meticulously collected to map the landscape of research within a specific field.Between January 1, 2014, and December 31, 2023, a total of 10,328 articles on ketamine research were published across 1,752 academic journals by 45,891 authors from 8,914 institutions in 128 countries. The publication volume has shown a steady increase over this period. The United States of America (USA) and the People’s Republic of China lead in both publication and citation counts. The National Institute of Mental Health (NIMH) and Yale University emerge as the most active institutions in this research domain. Carlos Zarate of the NIH National Institute of Mental Health was noted for the highest number of significant publications and received the most co-citations. The analysis revealed key research themes including mechanism of action, adverse events, psychiatric applications, and perioperative implications.This study provided comprehensive bibliometric and knowledge mapping analysis of the global ketamine research landscape, offering valuable insights into the trends, key contributors, and thematic focus areas within the field. By delineating the evolution of ketamine research, this study aims to guide future scholarly endeavors and enhance our understanding of ketamine’s therapeutic potential.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"65 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141338248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Previous neuroimaging studies have revealed structural and functional brain abnormalities in patients with cervical spondylosis (CS). However, the results are divergent and inconsistent. Therefore, the present study conducted a multi-modal meta-analysis to investigate the consistent structural and functional brain alterations in CS patients.A comprehensive literature search was conducted in five databases to retrieve relevant resting-state functional magnetic resonance imaging (rs-fMRI), structural MRI and diffusion tensor imaging (DTI) studies that measured brain functional and structural differences between CS patients and healthy controls (HCs). Separate and multimodal meta-analyses were implemented, respectively, by employing Anisotropic Effect-size Signed Differential Mapping software.13 rs-fMRI studies that used regional homogeneity, amplitude of low-frequency fluctuations (ALFF) and fractional ALFF, seven voxel-based morphometry (VBM) studies and one DTI study were finally included in the present research. However, no studies on surface-based morphometry (SBM) analysis were included in this research. Due to the insufficient number of SBM and DTI studies, only rs-fMRI and VBM meta-analyses were conducted. The results of rs-fMRI meta-analysis showed that compared to HCs, CS patients demonstrated decreased regional spontaneous brain activities in the right lingual gyrus, right middle temporal gyrus (MTG), left inferior parietal gyrus and right postcentral gyrus (PoCG), while increased activities in the right medial superior frontal gyrus, bilateral middle frontal gyrus and right precuneus. VBM meta-analysis detected increased GMV in the right superior temporal gyrus (STG) and right paracentral lobule (PCL), while decreased GMV in the left supplementary motor area and left MTG in CS patients. The multi-modal meta-analysis revealed increased GMV together with decreased regional spontaneous brain activity in the left PoCG, right STG and PCL among CS patients.This meta-analysis revealed that compared to HCs, CS patients had significant alterations in GMV and regional spontaneous brain activity. The altered brain regions mainly included the primary visual cortex, the default mode network and the sensorimotor area, which may be associated with CS patients' symptoms of sensory deficits, blurred vision, cognitive impairment and motor dysfunction. The findings may contribute to understanding the underlying pathophysiology of brain dysfunction and provide references for early diagnosis and treatment of CS.https://www.crd.york.ac.uk/PROSPERO/, CRD42022370967.
{"title":"Abnormalities of brain structure and function in cervical spondylosis: a multi-modal voxel-based meta-analysis","authors":"Lulu Cheng, Jianxin Zhang, Hongyu Xi, Mengting Li, Su Hu, Wenting Yuan, Peng Wang, Lanfen Chen, Linlin Zhan, Xize Jia","doi":"10.3389/fnins.2024.1415411","DOIUrl":"https://doi.org/10.3389/fnins.2024.1415411","url":null,"abstract":"Previous neuroimaging studies have revealed structural and functional brain abnormalities in patients with cervical spondylosis (CS). However, the results are divergent and inconsistent. Therefore, the present study conducted a multi-modal meta-analysis to investigate the consistent structural and functional brain alterations in CS patients.A comprehensive literature search was conducted in five databases to retrieve relevant resting-state functional magnetic resonance imaging (rs-fMRI), structural MRI and diffusion tensor imaging (DTI) studies that measured brain functional and structural differences between CS patients and healthy controls (HCs). Separate and multimodal meta-analyses were implemented, respectively, by employing Anisotropic Effect-size Signed Differential Mapping software.13 rs-fMRI studies that used regional homogeneity, amplitude of low-frequency fluctuations (ALFF) and fractional ALFF, seven voxel-based morphometry (VBM) studies and one DTI study were finally included in the present research. However, no studies on surface-based morphometry (SBM) analysis were included in this research. Due to the insufficient number of SBM and DTI studies, only rs-fMRI and VBM meta-analyses were conducted. The results of rs-fMRI meta-analysis showed that compared to HCs, CS patients demonstrated decreased regional spontaneous brain activities in the right lingual gyrus, right middle temporal gyrus (MTG), left inferior parietal gyrus and right postcentral gyrus (PoCG), while increased activities in the right medial superior frontal gyrus, bilateral middle frontal gyrus and right precuneus. VBM meta-analysis detected increased GMV in the right superior temporal gyrus (STG) and right paracentral lobule (PCL), while decreased GMV in the left supplementary motor area and left MTG in CS patients. The multi-modal meta-analysis revealed increased GMV together with decreased regional spontaneous brain activity in the left PoCG, right STG and PCL among CS patients.This meta-analysis revealed that compared to HCs, CS patients had significant alterations in GMV and regional spontaneous brain activity. The altered brain regions mainly included the primary visual cortex, the default mode network and the sensorimotor area, which may be associated with CS patients' symptoms of sensory deficits, blurred vision, cognitive impairment and motor dysfunction. The findings may contribute to understanding the underlying pathophysiology of brain dysfunction and provide references for early diagnosis and treatment of CS.https://www.crd.york.ac.uk/PROSPERO/, CRD42022370967.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"11 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141341506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One-shot learning, the ability to learn a new concept from a single instance, is a distinctive brain function that has garnered substantial interest in machine learning. While modeling physiological mechanisms poses challenges, advancements in artificial neural networks have led to performances in specific tasks that rival human capabilities. Proposing one-shot learning methods with these advancements, especially those involving simple mechanisms, not only enhance technological development but also contribute to neuroscience by proposing functionally valid hypotheses. Among the simplest methods for one-shot class addition with deep learning image classifiers is “weight imprinting,” which uses neural activity from a new class image data as the corresponding new synaptic weights. Despite its simplicity, its relevance to neuroscience is ambiguous, and it often interferes with original image classification, which is a significant drawback in practical applications. This study introduces a novel interpretation where a part of the weight imprinting process aligns with the Hebbian rule. We show that a single Hebbian-like process enables pre-trained deep learning image classifiers to perform one-shot class addition without any modification to the original classifier's backbone. Using non-parametric normalization to mimic brain's fast Hebbian plasticity significantly reduces the interference observed in previous methods. Our method is one of the simplest and most practical for one-shot class addition tasks, and its reliance on a single fast Hebbian-like process contributes valuable insights to neuroscience hypotheses.
{"title":"A single fast Hebbian-like process enabling one-shot class addition in deep neural networks without backbone modification","authors":"Kazufumi Hosoda, Keigo Nishida, S. Seno, Tomohiro Mashita, Hideki Kashioka, Izumi Ohzawa","doi":"10.3389/fnins.2024.1344114","DOIUrl":"https://doi.org/10.3389/fnins.2024.1344114","url":null,"abstract":"One-shot learning, the ability to learn a new concept from a single instance, is a distinctive brain function that has garnered substantial interest in machine learning. While modeling physiological mechanisms poses challenges, advancements in artificial neural networks have led to performances in specific tasks that rival human capabilities. Proposing one-shot learning methods with these advancements, especially those involving simple mechanisms, not only enhance technological development but also contribute to neuroscience by proposing functionally valid hypotheses. Among the simplest methods for one-shot class addition with deep learning image classifiers is “weight imprinting,” which uses neural activity from a new class image data as the corresponding new synaptic weights. Despite its simplicity, its relevance to neuroscience is ambiguous, and it often interferes with original image classification, which is a significant drawback in practical applications. This study introduces a novel interpretation where a part of the weight imprinting process aligns with the Hebbian rule. We show that a single Hebbian-like process enables pre-trained deep learning image classifiers to perform one-shot class addition without any modification to the original classifier's backbone. Using non-parametric normalization to mimic brain's fast Hebbian plasticity significantly reduces the interference observed in previous methods. Our method is one of the simplest and most practical for one-shot class addition tasks, and its reliance on a single fast Hebbian-like process contributes valuable insights to neuroscience hypotheses.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"19 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-12DOI: 10.3389/fnins.2024.1402039
Yuanqing Wu, Jun Yao, Xiao-Min Xu, Lei-Lei Zhou, Richard Salvi, Shaohua Ding, Xia Gao
Sensorineural hearing loss (SNHL) is the most common form of sensory deprivation and is often unrecognized by patients, inducing not only auditory but also nonauditory symptoms. Data-driven classifier modeling with the combination of neural static and dynamic imaging features could be effectively used to classify SNHL individuals and healthy controls (HCs).We conducted hearing evaluation, neurological scale tests and resting-state MRI on 110 SNHL patients and 106 HCs. A total of 1,267 static and dynamic imaging characteristics were extracted from MRI data, and three methods of feature selection were computed, including the Spearman rank correlation test, least absolute shrinkage and selection operator (LASSO) and t test as well as LASSO. Linear, polynomial, radial basis functional kernel (RBF) and sigmoid support vector machine (SVM) models were chosen as the classifiers with fivefold cross-validation. The receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated for each model.SNHL subjects had higher hearing thresholds in each frequency, as well as worse performance in cognitive and emotional evaluations, than HCs. After comparison, the selected brain regions using LASSO based on static and dynamic features were consistent with the between-group analysis, including auditory and nonauditory areas. The subsequent AUCs of the four SVM models (linear, polynomial, RBF and sigmoid) were as follows: 0.8075, 0.7340, 0.8462 and 0.8562. The RBF and sigmoid SVM had relatively higher accuracy, sensitivity and specificity.Our research raised attention to static and dynamic alterations underlying hearing deprivation. Machine learning-based models may provide several useful biomarkers for the classification and diagnosis of SNHL.
{"title":"Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study","authors":"Yuanqing Wu, Jun Yao, Xiao-Min Xu, Lei-Lei Zhou, Richard Salvi, Shaohua Ding, Xia Gao","doi":"10.3389/fnins.2024.1402039","DOIUrl":"https://doi.org/10.3389/fnins.2024.1402039","url":null,"abstract":"Sensorineural hearing loss (SNHL) is the most common form of sensory deprivation and is often unrecognized by patients, inducing not only auditory but also nonauditory symptoms. Data-driven classifier modeling with the combination of neural static and dynamic imaging features could be effectively used to classify SNHL individuals and healthy controls (HCs).We conducted hearing evaluation, neurological scale tests and resting-state MRI on 110 SNHL patients and 106 HCs. A total of 1,267 static and dynamic imaging characteristics were extracted from MRI data, and three methods of feature selection were computed, including the Spearman rank correlation test, least absolute shrinkage and selection operator (LASSO) and t test as well as LASSO. Linear, polynomial, radial basis functional kernel (RBF) and sigmoid support vector machine (SVM) models were chosen as the classifiers with fivefold cross-validation. The receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated for each model.SNHL subjects had higher hearing thresholds in each frequency, as well as worse performance in cognitive and emotional evaluations, than HCs. After comparison, the selected brain regions using LASSO based on static and dynamic features were consistent with the between-group analysis, including auditory and nonauditory areas. The subsequent AUCs of the four SVM models (linear, polynomial, RBF and sigmoid) were as follows: 0.8075, 0.7340, 0.8462 and 0.8562. The RBF and sigmoid SVM had relatively higher accuracy, sensitivity and specificity.Our research raised attention to static and dynamic alterations underlying hearing deprivation. Machine learning-based models may provide several useful biomarkers for the classification and diagnosis of SNHL.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"74 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-12DOI: 10.3389/fnins.2024.1389680
John A Kruper, McKenzie P. Hagen, François Rheault, Isaac Crane, Asa Gilmore, Manjari Narayan, Keshav Motwani, Eardi Lila, Chris Rorden, Jason D. Yeatman, A. Rokem
The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
人类连接组计划（HCP）已成为人类神经科学的关键数据集，在推进脑成像方法和了解人类大脑方面有着大量重要应用。我们重点研究了HCP弥散加权核磁共振成像（dMRI）数据的牵引成像。我们使用一个开源软件库（pyAFQ; https://yeatmanlab.github.io/pyAFQ）进行概率牵引成像，并在具有完整dMRI采集的HCP受试者（n = 1,041）中划分出主要的白质通路。我们使用扩散峰度成像（DKI）对白质每个体素的白质微观结构进行建模，并沿着束的长度提取 DKI 衍生组织属性的束剖面。我们探索了数据的经验特性：首先，我们利用在 HCP 中采样的大量双胞胎的已知遗传联系评估了 DKI 组织特性的遗传性。其次，与局部连接组特征相比，我们测试了牵引力测量作为个体特征（如年龄、结晶/流体智力、阅读能力等）预测模型基础的能力。为了便于探索数据集，我们创建了一个新的基于网络的可视化工具，并使用该工具对 HCP 牵引测量数据集中的数据进行可视化。最后，我们将 HCP 数据集作为一项新技术创新的试验平台：TRX 文件格式，用于表示基于 dMRI 的流线型数据。我们通过 AWS 开放数据计划的开放神经数据存储库发布了处理输出和牵引剖面，作为可公开获取的数据资源。我们发现，在某些大脑通路中，基于 DKI 的指标的遗传率高达 0.9。我们还发现，牵引测量法与局部连接组法一样能提取关于个体差异的有用信息。我们发布了一个新的基于网络的牵引测量可视化工具--"Tractoscope" (https://nrdg.github.io/tractoscope)。我们发现，TRX 文件所需的磁盘空间要少得多--这对于像 HCP 这样的大型数据集来说至关重要。此外，TRX 还集成了流线分组规范，进一步简化了牵引测量分析。
{"title":"Tractometry of the Human Connectome Project: resources and insights","authors":"John A Kruper, McKenzie P. Hagen, François Rheault, Isaac Crane, Asa Gilmore, Manjari Narayan, Keshav Motwani, Eardi Lila, Chris Rorden, Jason D. Yeatman, A. Rokem","doi":"10.3389/fnins.2024.1389680","DOIUrl":"https://doi.org/10.3389/fnins.2024.1389680","url":null,"abstract":"The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-12DOI: 10.3389/fnins.2024.1396345
Xin Qin, Shu Wang, Juan Huang, Binbin Hu, Xingyan Yang, Liying Liang, Rui Zhou, Wei Huang
Parkinson’s disease (PD) is a common neurodegenerative disease with a rapid increase in incidence in recent years. Existing treatments cannot slow or stop the progression of PD. It was proposed that neuroinflammation leads to neuronal death, making targeting neuroinflammation a promising therapeutic strategy. Our previous studies have demonstrated that rhein protects neurons in vitro by inhibiting neuroinflammation, and it has been found to exhibit neuroprotective effects in Alzheimer’s disease and epilepsy, but its neuroprotective mechanisms and effects on PD are still unclear.PD animal model was induced by 1-methyl-4-phenyl-1,2,3, 6-tetrahydropyridine (MPTP). ELISA, RT-qPCR, western blot and Immunofluorescence were used to detect the levels of inflammatory cytokines and M1 polarization markers. The protein expression levels of signaling pathways were measured by western blot. Hematoxylin–eosin (HE) staining showed that rhein did not damage the liver and kidney. Two behavioral tests, pole test and rotarod test, were used to evaluate the improvement effect of rhein on movement disorders. The number of neurons in the substantia nigra was evaluated by Nissl staining. Immunohistochemistry and western blot were used to detect tyrosine hydroxylase (TH) and α-synuclein.Rhein inhibited the activation of MAPK/IκB signaling pathway and reduced the levels of pro-inflammatory cytokines (IL-1β, IL-6 and TNF-α) and M1 polarization markers of microglia in vivo. In a mouse model of PD, rhein ameliorated movement disorders, reduced dopaminergic neuron damage and α-synuclein deposition.Rhein inhibits neuroinflammation through MAPK/IκB signaling pathway, thereby reducing neurodegeneration, α-synuclein deposition, and improving movement disorders in Parkinson’s disease.
{"title":"Rhein alleviates MPTP-induced Parkinson’s disease by suppressing neuroinflammation via MAPK/IκB pathway","authors":"Xin Qin, Shu Wang, Juan Huang, Binbin Hu, Xingyan Yang, Liying Liang, Rui Zhou, Wei Huang","doi":"10.3389/fnins.2024.1396345","DOIUrl":"https://doi.org/10.3389/fnins.2024.1396345","url":null,"abstract":"Parkinson’s disease (PD) is a common neurodegenerative disease with a rapid increase in incidence in recent years. Existing treatments cannot slow or stop the progression of PD. It was proposed that neuroinflammation leads to neuronal death, making targeting neuroinflammation a promising therapeutic strategy. Our previous studies have demonstrated that rhein protects neurons in vitro by inhibiting neuroinflammation, and it has been found to exhibit neuroprotective effects in Alzheimer’s disease and epilepsy, but its neuroprotective mechanisms and effects on PD are still unclear.PD animal model was induced by 1-methyl-4-phenyl-1,2,3, 6-tetrahydropyridine (MPTP). ELISA, RT-qPCR, western blot and Immunofluorescence were used to detect the levels of inflammatory cytokines and M1 polarization markers. The protein expression levels of signaling pathways were measured by western blot. Hematoxylin–eosin (HE) staining showed that rhein did not damage the liver and kidney. Two behavioral tests, pole test and rotarod test, were used to evaluate the improvement effect of rhein on movement disorders. The number of neurons in the substantia nigra was evaluated by Nissl staining. Immunohistochemistry and western blot were used to detect tyrosine hydroxylase (TH) and α-synuclein.Rhein inhibited the activation of MAPK/IκB signaling pathway and reduced the levels of pro-inflammatory cytokines (IL-1β, IL-6 and TNF-α) and M1 polarization markers of microglia in vivo. In a mouse model of PD, rhein ameliorated movement disorders, reduced dopaminergic neuron damage and α-synuclein deposition.Rhein inhibits neuroinflammation through MAPK/IκB signaling pathway, thereby reducing neurodegeneration, α-synuclein deposition, and improving movement disorders in Parkinson’s disease.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"32 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141354737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sensorineural hearing loss (SNHL) can arise from a diverse range of congenital and acquired factors. Detecting it early is pivotal for nurturing speech, language, and cognitive development in children with SNHL. In our study, we utilized synthetic magnetic resonance imaging (SyMRI) to assess alterations in both gray and white matter within the brains of children affected by SNHL.The study encompassed both children diagnosed with SNHL and a control group of children with normal hearing {1.5-month-olds (n = 52) and 3-month-olds (n = 78)}. Participants were categorized based on their auditory brainstem response (ABR) threshold, delineated into normal, mild, moderate, and severe subgroups.Clinical parameters were included and assessed the correlation with SNHL. Quantitative analysis of brain morphology was conducted using SyMRI scans, yielding data on brain segmentation and relaxation time.Through both univariate and multivariate analyses, independent factors predictive of SNHL were identified. The efficacy of the prediction model was evaluated using receiver operating characteristic (ROC) curves, with visualization facilitated through the utilization of a nomogram. It's important to note that due to the constraints of our research, we worked with a relatively small sample size.Neonatal hyperbilirubinemia (NH) and children with inner ear malformation (IEM) were associated with the onset of SNHL both at 1.5 and 3-month groups. At 3-month group, the moderate and severe subgroups exhibited elevated quantitative T1 values in the inferior colliculus (IC), lateral lemniscus (LL), and middle cerebellar peduncle (MCP) compared to the normal group. Additionally, WMV, WMF, MYF, and MYV were significantly reduced relative to the normal group. Additionally, SNHL-children with IEM had high T1 values in IC, and LL and reduced WMV, WMF, MYV and MYF values as compared with SNHL-children without IEM at 3-month group. LL-T1 and WMF were independent risk factors associated with SNHL. Consequently, a prediction model was devised based on LL-T1 and WMF. ROC for training set, validation set and external set were 0.865, 0.806, and 0.736, respectively.The integration of T1 quantitative values and brain volume segmentation offers a valuable tool for tracking brain development in children affected by SNHL and assessing the progression of the condition's severity.
{"title":"The value of synthetic MRI in detecting the brain changes and hearing impairment of children with sensorineural hearing loss","authors":"Penghua Zhang, Jinze Yang, Yikai Shu, Meiying Cheng, Xin Zhao, Kaiyu Wang, Lin Lu, Qing-na Xing, Guangying Niu, Lingsong Meng, Xueyuan Wang, Liang Zhou, Xiaoan Zhang","doi":"10.3389/fnins.2024.1365141","DOIUrl":"https://doi.org/10.3389/fnins.2024.1365141","url":null,"abstract":"Sensorineural hearing loss (SNHL) can arise from a diverse range of congenital and acquired factors. Detecting it early is pivotal for nurturing speech, language, and cognitive development in children with SNHL. In our study, we utilized synthetic magnetic resonance imaging (SyMRI) to assess alterations in both gray and white matter within the brains of children affected by SNHL.The study encompassed both children diagnosed with SNHL and a control group of children with normal hearing {1.5-month-olds (n = 52) and 3-month-olds (n = 78)}. Participants were categorized based on their auditory brainstem response (ABR) threshold, delineated into normal, mild, moderate, and severe subgroups.Clinical parameters were included and assessed the correlation with SNHL. Quantitative analysis of brain morphology was conducted using SyMRI scans, yielding data on brain segmentation and relaxation time.Through both univariate and multivariate analyses, independent factors predictive of SNHL were identified. The efficacy of the prediction model was evaluated using receiver operating characteristic (ROC) curves, with visualization facilitated through the utilization of a nomogram. It's important to note that due to the constraints of our research, we worked with a relatively small sample size.Neonatal hyperbilirubinemia (NH) and children with inner ear malformation (IEM) were associated with the onset of SNHL both at 1.5 and 3-month groups. At 3-month group, the moderate and severe subgroups exhibited elevated quantitative T1 values in the inferior colliculus (IC), lateral lemniscus (LL), and middle cerebellar peduncle (MCP) compared to the normal group. Additionally, WMV, WMF, MYF, and MYV were significantly reduced relative to the normal group. Additionally, SNHL-children with IEM had high T1 values in IC, and LL and reduced WMV, WMF, MYV and MYF values as compared with SNHL-children without IEM at 3-month group. LL-T1 and WMF were independent risk factors associated with SNHL. Consequently, a prediction model was devised based on LL-T1 and WMF. ROC for training set, validation set and external set were 0.865, 0.806, and 0.736, respectively.The integration of T1 quantitative values and brain volume segmentation offers a valuable tool for tracking brain development in children affected by SNHL and assessing the progression of the condition's severity.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"35 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.3389/fnins.2024.1324047
Kevin T. Willeford, Victoria Copel, Hua Rong
Currently, there is no established system for quantifying patterns of ocular ductions. This poses challenges in tracking the onset and evolution of ocular motility disorders, as current clinical methodologies rely on subjective observations of individual movements. We propose a protocol that integrates image processing, a statistical framework of summary indices, and criteria for evaluating both cross-sectional and longitudinal differences in ductions to address this methodological gap. We demonstrate that our protocol reliably transforms objective estimates of ocular rotations into normative patterns of total movement area and movement symmetry. This is a critical step towards clinical application in which our protocol could first diagnose and then track the progression and resolution of ocular motility disorders over time.
{"title":"A protocol to quantify cross-sectional and longitudinal differences in duction patterns","authors":"Kevin T. Willeford, Victoria Copel, Hua Rong","doi":"10.3389/fnins.2024.1324047","DOIUrl":"https://doi.org/10.3389/fnins.2024.1324047","url":null,"abstract":"Currently, there is no established system for quantifying patterns of ocular ductions. This poses challenges in tracking the onset and evolution of ocular motility disorders, as current clinical methodologies rely on subjective observations of individual movements. We propose a protocol that integrates image processing, a statistical framework of summary indices, and criteria for evaluating both cross-sectional and longitudinal differences in ductions to address this methodological gap. We demonstrate that our protocol reliably transforms objective estimates of ocular rotations into normative patterns of total movement area and movement symmetry. This is a critical step towards clinical application in which our protocol could first diagnose and then track the progression and resolution of ocular motility disorders over time.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.3389/fnins.2024.1383319
Yang Liu, Ruibin Liu, Jin-nian Ge, Yue Wang
In recent years, with the shift of focus in metaverse research toward content exchange and social interaction, breaking through the current bottleneck of audio-visual media interaction has become an urgent issue. The use of brain-machine interfaces for sensory simulation is one of the proposed solutions. Currently, brain-machine interfaces have demonstrated irreplaceable potential as physiological signal acquisition tools in various fields within the metaverse. This study explores three application scenarios: generative art in the metaverse, serious gaming for healthcare in metaverse medicine, and brain-machine interface applications for facial expression synthesis in the virtual society of the metaverse. It investigates existing commercial products and patents (such as MindWave Mobile, GVS, and Galea), draws analogies with the development processes of network security and neurosecurity, bioethics and neuroethics, and discusses the challenges and potential issues that may arise when brain-machine interfaces mature and are widely applied. Furthermore, it looks ahead to the diverse possibilities of deep and varied applications of brain-machine interfaces in the metaverse in the future.
{"title":"Advancements in brain-machine interfaces for application in the metaverse","authors":"Yang Liu, Ruibin Liu, Jin-nian Ge, Yue Wang","doi":"10.3389/fnins.2024.1383319","DOIUrl":"https://doi.org/10.3389/fnins.2024.1383319","url":null,"abstract":"In recent years, with the shift of focus in metaverse research toward content exchange and social interaction, breaking through the current bottleneck of audio-visual media interaction has become an urgent issue. The use of brain-machine interfaces for sensory simulation is one of the proposed solutions. Currently, brain-machine interfaces have demonstrated irreplaceable potential as physiological signal acquisition tools in various fields within the metaverse. This study explores three application scenarios: generative art in the metaverse, serious gaming for healthcare in metaverse medicine, and brain-machine interface applications for facial expression synthesis in the virtual society of the metaverse. It investigates existing commercial products and patents (such as MindWave Mobile, GVS, and Galea), draws analogies with the development processes of network security and neurosecurity, bioethics and neuroethics, and discusses the challenges and potential issues that may arise when brain-machine interfaces mature and are widely applied. Furthermore, it looks ahead to the diverse possibilities of deep and varied applications of brain-machine interfaces in the metaverse in the future.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"30 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.3389/fnins.2024.1368411
Mohamed Hesham Khalil
Hippocampal neurogenesis is critical for improving learning, memory, and spatial navigation. Inhabiting and navigating spatial complexity is key to stimulating adult hippocampal neurogenesis (AHN) in rodents because they share similar hippocampal neuroplasticity characteristics with humans. AHN in humans has recently been found to persist until the tenth decade of life, but it declines with aging and is influenced by environmental enrichment. This systematic review investigated the impact of spatial complexity on neurogenesis and hippocampal plasticity in rodents, and discussed the translatability of these findings to human interventions.Comprehensive searches were conducted on three databases in English: PubMed, Web of Science, and Scopus. All literature published until December 2023 was screened and assessed for eligibility. A total of 32 studies with original data were included, and the process is reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement and checklist.The studies evaluated various models of spatial complexity in rodents, including environmental enrichment, changes to in-cage elements, complex layouts, and navigational mazes featuring novelty and intermittent complexity. A regression equation was formulated to synthesize key factors influencing neurogenesis, such as duration, physical activity, frequency of changes, diversity of complexity, age, living space size, and temperature.Findings underscore the cognitive benefits of spatial complexity interventions and inform future translational research from rodents to humans. Home-cage enrichment and models like the Hamlet complex maze and the Marlau cage offer insight into how architectural design and urban navigational complexity can impact neurogenesis in humans. In-space changing complexity, with and without physical activity, is effective for stimulating neurogenesis. While evidence on intermittent spatial complexity in humans is limited, data from the COVID-19 pandemic lockdowns provide preliminary evidence. Existing equations relating rodent and human ages may allow for the translation of enrichment protocol durations from rodents to humans.
{"title":"Environmental enrichment: a systematic review on the effect of a changing spatial complexity on hippocampal neurogenesis and plasticity in rodents, with considerations for translation to urban and built environments for humans","authors":"Mohamed Hesham Khalil","doi":"10.3389/fnins.2024.1368411","DOIUrl":"https://doi.org/10.3389/fnins.2024.1368411","url":null,"abstract":"Hippocampal neurogenesis is critical for improving learning, memory, and spatial navigation. Inhabiting and navigating spatial complexity is key to stimulating adult hippocampal neurogenesis (AHN) in rodents because they share similar hippocampal neuroplasticity characteristics with humans. AHN in humans has recently been found to persist until the tenth decade of life, but it declines with aging and is influenced by environmental enrichment. This systematic review investigated the impact of spatial complexity on neurogenesis and hippocampal plasticity in rodents, and discussed the translatability of these findings to human interventions.Comprehensive searches were conducted on three databases in English: PubMed, Web of Science, and Scopus. All literature published until December 2023 was screened and assessed for eligibility. A total of 32 studies with original data were included, and the process is reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement and checklist.The studies evaluated various models of spatial complexity in rodents, including environmental enrichment, changes to in-cage elements, complex layouts, and navigational mazes featuring novelty and intermittent complexity. A regression equation was formulated to synthesize key factors influencing neurogenesis, such as duration, physical activity, frequency of changes, diversity of complexity, age, living space size, and temperature.Findings underscore the cognitive benefits of spatial complexity interventions and inform future translational research from rodents to humans. Home-cage enrichment and models like the Hamlet complex maze and the Marlau cage offer insight into how architectural design and urban navigational complexity can impact neurogenesis in humans. In-space changing complexity, with and without physical activity, is effective for stimulating neurogenesis. While evidence on intermittent spatial complexity in humans is limited, data from the COVID-19 pandemic lockdowns provide preliminary evidence. Existing equations relating rodent and human ages may allow for the translation of enrichment protocol durations from rodents to humans.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"97 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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