Pub Date : 2024-04-24DOI: 10.3389/fnins.2024.1396917
Jun Liang, Abdelkader Nasreddine Belkacem, Yanxin Song, Jiaxin Wang, Zhiguo Ai, Xuanqi Wang, Jun Guo, Lingfeng Fan, Changming Wang, Bowen Ji, Zengguang Wang
Sleep plays a critical role in human physiological and psychological health, and electroencephalography (EEG), an effective sleep-monitoring method, is of great importance in revealing sleep characteristics and aiding the diagnosis of sleep disorders. Sleep spindles, which are a typical phenomenon in EEG, hold importance in sleep science.This paper proposes a novel convolutional neural network (CNN) model to classify sleep spindles. Transfer learning is employed to apply the model trained on the sleep spindles of healthy subjects to those of subjects with insomnia for classification. To analyze the effect of transfer learning, we discuss the classification results of both partially and fully transferred convolutional layers.The classification accuracy for the healthy and insomnia subjects’ spindles were 93.68% and 92.77%, respectively. During transfer learning, when transferring all convolutional layers, the classification accuracy for the insomnia subjects’ spindles was 91.41% and transferring only the first four convolutional layers achieved a classification result of 92.80%. The experimental results demonstrate that the proposed CNN model can effectively classify sleep spindles. Furthermore, the features learned from the data of the normal subjects can be effectively applied to the data for subjects with insomnia, yielding desirable outcomes.These outcomes underscore the efficacy of both the collected dataset and the proposed CNN model. The proposed model exhibits potential as a rapid and effective means to diagnose and treat sleep disorders, thereby improving the speed and quality of patient care.
{"title":"Classification and transfer learning of sleep spindles based on convolutional neural networks","authors":"Jun Liang, Abdelkader Nasreddine Belkacem, Yanxin Song, Jiaxin Wang, Zhiguo Ai, Xuanqi Wang, Jun Guo, Lingfeng Fan, Changming Wang, Bowen Ji, Zengguang Wang","doi":"10.3389/fnins.2024.1396917","DOIUrl":"https://doi.org/10.3389/fnins.2024.1396917","url":null,"abstract":"Sleep plays a critical role in human physiological and psychological health, and electroencephalography (EEG), an effective sleep-monitoring method, is of great importance in revealing sleep characteristics and aiding the diagnosis of sleep disorders. Sleep spindles, which are a typical phenomenon in EEG, hold importance in sleep science.This paper proposes a novel convolutional neural network (CNN) model to classify sleep spindles. Transfer learning is employed to apply the model trained on the sleep spindles of healthy subjects to those of subjects with insomnia for classification. To analyze the effect of transfer learning, we discuss the classification results of both partially and fully transferred convolutional layers.The classification accuracy for the healthy and insomnia subjects’ spindles were 93.68% and 92.77%, respectively. During transfer learning, when transferring all convolutional layers, the classification accuracy for the insomnia subjects’ spindles was 91.41% and transferring only the first four convolutional layers achieved a classification result of 92.80%. The experimental results demonstrate that the proposed CNN model can effectively classify sleep spindles. Furthermore, the features learned from the data of the normal subjects can be effectively applied to the data for subjects with insomnia, yielding desirable outcomes.These outcomes underscore the efficacy of both the collected dataset and the proposed CNN model. The proposed model exhibits potential as a rapid and effective means to diagnose and treat sleep disorders, thereby improving the speed and quality of patient care.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"75 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140659408","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 : 2024-04-24DOI: 10.3389/fnins.2024.1385233
S. Agostini, R. Mancuso, Domenico Caputo, M. Rovaris, Mario Clerici
Several evidences, including increased serum titers of Epstein–Barr virus (EBV)-specific antibodies and the presence of EBV DNA in brain of patients suggest a possible role of this virus in the pathogenesis of Multiple Sclerosis (MS), a chronic neurodegenerative disease with an unknown etiopathology. Aim of the present study is to verify if the expression of LMP2A and EBNA-1, two EBV genes, is altered in MS patients. EBV viral load, LMP2A and EBNA-1 gene expression and EBNA-1 antibodies titers were evaluated in blood of EBV-seropositive MS patients (n = 57; 31 relapsing remitting –RRMS- and 26 progressive -PMS-patients) and age- and sex-matched healthy controls (HC, n = 49). Results showed that EBNA-1 and VCA antibodies titers are significantly augmented in MS patients compared to HC (p < 0.05 for both antibodies); detection of EBV DNA was more frequent as well in MS patients compared to HC, although without reaching statistical significance. Regarding viral gene expression, LMP2A was significantly more frequently detected and more expressed in MS patients compared to HC (p < 0.005) whereas no differences were observed for EBNA-1. Considering patients alone, EBNA-1 was significantly more frequent in PMS compared to RRMS (p < 0.05), whereas no differences were observed for LMP2A. Increased expression of the LMP2A latency-associated gene in MS patients supports the hypothesis that EBV plays a role in disease etiopathology.
{"title":"EBV and multiple sclerosis: expression of LMP2A in MS patients","authors":"S. Agostini, R. Mancuso, Domenico Caputo, M. Rovaris, Mario Clerici","doi":"10.3389/fnins.2024.1385233","DOIUrl":"https://doi.org/10.3389/fnins.2024.1385233","url":null,"abstract":"Several evidences, including increased serum titers of Epstein–Barr virus (EBV)-specific antibodies and the presence of EBV DNA in brain of patients suggest a possible role of this virus in the pathogenesis of Multiple Sclerosis (MS), a chronic neurodegenerative disease with an unknown etiopathology. Aim of the present study is to verify if the expression of LMP2A and EBNA-1, two EBV genes, is altered in MS patients. EBV viral load, LMP2A and EBNA-1 gene expression and EBNA-1 antibodies titers were evaluated in blood of EBV-seropositive MS patients (n = 57; 31 relapsing remitting –RRMS- and 26 progressive -PMS-patients) and age- and sex-matched healthy controls (HC, n = 49). Results showed that EBNA-1 and VCA antibodies titers are significantly augmented in MS patients compared to HC (p < 0.05 for both antibodies); detection of EBV DNA was more frequent as well in MS patients compared to HC, although without reaching statistical significance. Regarding viral gene expression, LMP2A was significantly more frequently detected and more expressed in MS patients compared to HC (p < 0.005) whereas no differences were observed for EBNA-1. Considering patients alone, EBNA-1 was significantly more frequent in PMS compared to RRMS (p < 0.05), whereas no differences were observed for LMP2A. Increased expression of the LMP2A latency-associated gene in MS patients supports the hypothesis that EBV plays a role in disease etiopathology.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"76 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140665542","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}
Probucol has been utilized as a cholesterol-lowering drug with antioxidative properties. However, the impact and fundamental mechanisms of probucol in obesity-related cognitive decline are unclear. In this study, male C57BL/6J mice were allocated to a normal chow diet (NCD) group or a high-fat diet (HFD) group, followed by administration of probucol to half of the mice on the HFD regimen. Subsequently, the mice were subjected to a series of behavioral assessments, alongside the measurement of metabolic and redox parameters. Notably, probucol treatment effectively alleviates cognitive and social impairments induced by HFD in mice, while exhibiting no discernible influence on mood-related behaviors. Notably, the beneficial effects of probucol arise independently of rectifying obesity or restoring systemic glucose and lipid homeostasis, as evidenced by the lack of changes in body weight, serum cholesterol levels, blood glucose, hyperinsulinemia, systemic insulin resistance, and oxidative stress. Instead, probucol could regulate the levels of nitric oxide and superoxide-generating proteins, and it could specifically alleviate HFD-induced hippocampal insulin resistance. These findings shed light on the potential role of probucol in modulating obesity-related cognitive decline and urge reevaluation of the underlying mechanisms by which probucol exerts its beneficial effects.
{"title":"Probucol mitigates high-fat diet-induced cognitive and social impairments by regulating brain redox and insulin resistance","authors":"Han-ming Wu, Yang Vivian Yang, Na-Jun Huang, Li-Ping Fan, Ying-Ying Dai, Ke-Ting Hu, Tian-Yu Tang, Lin Liu, Yue Xu, Dong-Tai Liu, Ze-Xin Cai, Xiao-Yu Niu, Xin-Yi Ren, Zheng-Hao Yao, Hao-Yu Qin, Jian-Zhen Chen, Xi Huang, Cixiong Zhang, Xiang You, Chen Wang, Ying He, Wei Hong, Yu-Xia Sun, Yi-Hong Zhan, Shu-Yong Lin","doi":"10.3389/fnins.2024.1368552","DOIUrl":"https://doi.org/10.3389/fnins.2024.1368552","url":null,"abstract":"Probucol has been utilized as a cholesterol-lowering drug with antioxidative properties. However, the impact and fundamental mechanisms of probucol in obesity-related cognitive decline are unclear. In this study, male C57BL/6J mice were allocated to a normal chow diet (NCD) group or a high-fat diet (HFD) group, followed by administration of probucol to half of the mice on the HFD regimen. Subsequently, the mice were subjected to a series of behavioral assessments, alongside the measurement of metabolic and redox parameters. Notably, probucol treatment effectively alleviates cognitive and social impairments induced by HFD in mice, while exhibiting no discernible influence on mood-related behaviors. Notably, the beneficial effects of probucol arise independently of rectifying obesity or restoring systemic glucose and lipid homeostasis, as evidenced by the lack of changes in body weight, serum cholesterol levels, blood glucose, hyperinsulinemia, systemic insulin resistance, and oxidative stress. Instead, probucol could regulate the levels of nitric oxide and superoxide-generating proteins, and it could specifically alleviate HFD-induced hippocampal insulin resistance. These findings shed light on the potential role of probucol in modulating obesity-related cognitive decline and urge reevaluation of the underlying mechanisms by which probucol exerts its beneficial effects.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"59 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668062","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 : 2024-04-23DOI: 10.3389/fnins.2024.1380886
Dan Yu, Jia hui Fang
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, and impulsivity. Current research in this field faces challenges, particularly in accurate diagnosis and effective treatment strategies. The analysis of motor information, enriched by artificial intelligence methodologies, plays a vital role in deepening our understanding and improving the management of ADHD. The integration of AI techniques, such as machine learning and data analysis, into the study of ADHD-related motor behaviors, allows for a more nuanced understanding of the disorder. This approach facilitates the identification of patterns and anomalies in motor activity that are often characteristic of ADHD, thereby contributing to more precise diagnostics and tailored treatment strategies. Our approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies. On the ADHD dataset, the model significantly improved accuracy to 98.21% and recall to 93.86%, especially excelling in EEG data processing with accuracy and recall rates of 96.62 and 95.21%, respectively, demonstrating precise capturing of ADHD characteristic behaviors and physiological responses. These results not only reveal the great potential of our model in improving ADHD diagnostic accuracy and developing personalized treatment plans, but also open up new research perspectives for understanding the complex neurological logic of ADHD. In addition, our study not only suggests innovative perspectives and approaches for ADHD treatment, but also provides a solid foundation for future research exploring similar complex neurological disorders, providing valuable data and insights. This is scientifically important for improving treatment outcomes and patients' quality of life, and points the way for future-oriented medical research and clinical practice.
{"title":"Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD","authors":"Dan Yu, Jia hui Fang","doi":"10.3389/fnins.2024.1380886","DOIUrl":"https://doi.org/10.3389/fnins.2024.1380886","url":null,"abstract":"Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, and impulsivity. Current research in this field faces challenges, particularly in accurate diagnosis and effective treatment strategies. The analysis of motor information, enriched by artificial intelligence methodologies, plays a vital role in deepening our understanding and improving the management of ADHD. The integration of AI techniques, such as machine learning and data analysis, into the study of ADHD-related motor behaviors, allows for a more nuanced understanding of the disorder. This approach facilitates the identification of patterns and anomalies in motor activity that are often characteristic of ADHD, thereby contributing to more precise diagnostics and tailored treatment strategies. Our approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies. On the ADHD dataset, the model significantly improved accuracy to 98.21% and recall to 93.86%, especially excelling in EEG data processing with accuracy and recall rates of 96.62 and 95.21%, respectively, demonstrating precise capturing of ADHD characteristic behaviors and physiological responses. These results not only reveal the great potential of our model in improving ADHD diagnostic accuracy and developing personalized treatment plans, but also open up new research perspectives for understanding the complex neurological logic of ADHD. In addition, our study not only suggests innovative perspectives and approaches for ADHD treatment, but also provides a solid foundation for future research exploring similar complex neurological disorders, providing valuable data and insights. This is scientifically important for improving treatment outcomes and patients' quality of life, and points the way for future-oriented medical research and clinical practice.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"132 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140669133","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}
Understanding the intricate architecture of the brain through the lens of graph theory and advanced neuroimaging techniques has become increasingly pivotal in unraveling the complexities of neural networks. This bibliometric analysis explores the evolving landscape of brain research by focusing on the intersection of graph theoretical approaches, neuroanatomy, and diverse neuroimaging modalities. A systematic search strategy was used that resulted in the retrieval of a comprehensive dataset of articles and reviews. Using CiteSpace and VOSviewer, a detailed scientometric analysis was conducted that revealed emerging trends, key research clusters, and influential contributions within this multidisciplinary domain. Our review highlights the growing synergy between graph theory methodologies and neuroimaging modalities, reflecting the evolving paradigms shaping our understanding of brain networks. This study offers comprehensive insight into brain network research, emphasizing growth patterns, pivotal contributions, and global collaborative networks, thus serving as a valuable resource for researchers and institutions navigating this interdisciplinary landscape.
{"title":"Beyond nodes and edges: a bibliometric analysis on graph theory and neuroimaging modalities","authors":"Makliya Mamat, Ziteng Wang, Ling Jin, Kailong He, Lin Li, Yiyong Chen","doi":"10.3389/fnins.2024.1373264","DOIUrl":"https://doi.org/10.3389/fnins.2024.1373264","url":null,"abstract":"Understanding the intricate architecture of the brain through the lens of graph theory and advanced neuroimaging techniques has become increasingly pivotal in unraveling the complexities of neural networks. This bibliometric analysis explores the evolving landscape of brain research by focusing on the intersection of graph theoretical approaches, neuroanatomy, and diverse neuroimaging modalities. A systematic search strategy was used that resulted in the retrieval of a comprehensive dataset of articles and reviews. Using CiteSpace and VOSviewer, a detailed scientometric analysis was conducted that revealed emerging trends, key research clusters, and influential contributions within this multidisciplinary domain. Our review highlights the growing synergy between graph theory methodologies and neuroimaging modalities, reflecting the evolving paradigms shaping our understanding of brain networks. This study offers comprehensive insight into brain network research, emphasizing growth patterns, pivotal contributions, and global collaborative networks, thus serving as a valuable resource for researchers and institutions navigating this interdisciplinary landscape.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"27 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672508","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 : 2024-04-22DOI: 10.3389/fnins.2024.1406502
Gaetano Di Caterina, Malu Zhang, Jundong Liu
{"title":"Editorial: Theoretical advances and practical applications of spiking neural networks","authors":"Gaetano Di Caterina, Malu Zhang, Jundong Liu","doi":"10.3389/fnins.2024.1406502","DOIUrl":"https://doi.org/10.3389/fnins.2024.1406502","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"14 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140674144","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 : 2024-04-22DOI: 10.3389/fnins.2024.1391413
Yutaka Itokazu, Alvin V. Terry
{"title":"Potential roles of gangliosides in chemical-induced neurodegenerative diseases and mental health disorders","authors":"Yutaka Itokazu, Alvin V. Terry","doi":"10.3389/fnins.2024.1391413","DOIUrl":"https://doi.org/10.3389/fnins.2024.1391413","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"54 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672632","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 : 2024-04-22DOI: 10.3389/fnins.2024.1370533
L. Ogunjimi, A. Alabi, Ibironke Oyenuga, Jeremiah Ogunkunle, Emmanuel Kasumu, Oluwaseun Ogunsanya, Oluwatobiloba Oluseyije, Pelumi Ogunbayo, Omorojo Idume, Adeola Kasali, Sarah Adesi, Mariam Oyebowale, Damilola Ogungbemi, A. Aderinola, Emmanuel Irokosu, Abdullahi A. Murtala, B. Osalusi
Sex steroid hormones are emerging significant biomarkers of depression among Women with Epilepsy (WWE) with promising prognostic potential and therapeutic end point. Therefore, the study is aimed at exploring the association between sex steroids hormones, Anti-seizure Medication (ASM) and depression among WWE.A baseline questionnaire was used to obtain socio-demographics and clinical characteristic from one hundred and twelve (112) WWE and 50 age matched healthy control. The diagnosis of epilepsy and Electroencephalography (EEG) description was based on 2017 International League Against Epilepsy (ILAE) criteria. Blood samples were collected from cases and control during Luteal Phase (LP) and Follicular Phase (FP). The Zung Self-Rating Depression Scale (ZSRDS) was used to assess depression.The prevalence of depression among WWE is 18.8%, with a significant difference between the level of formal education (p0.000), age (p0.000), and mean ZSRDS (p0.000) among cases and control. There is a statistical difference in hormonal levels between cases and control with regards to higher testosterone [3.28 ± 9.99 vs. 0.31 ± 0.30; p0.037], lower FP prolactin [16.37 ± 20.14 vs. 17.20 ± 7.44; p0.778], and lower LP prolactin [15.74 ± 18.22 vs. 17.67 ± 7.27; p0.473]. Testosterone (p0.024), FP Follicle Stimulating Hormone (FSH) (p0.009), FP Estradiol (p0.006), LP FSH (p0.031), LP Progesterone (p0.023), and LP Prolactin (p0.000) were associated with depression. However, only prolactin (p0.042) and testosterone (p0.000) predicts depression among WWE.There was higher mean depression score, lower prolactin and higher testosterone level among cases compared to control. Furthermore, there was lower prolactin and higher testosterone level in Carbamazepine (CBZ) group compared to Levetiracetam (LEV) groups.
{"title":"Relationship between depression and sex steroid hormone among women with epilepsy","authors":"L. Ogunjimi, A. Alabi, Ibironke Oyenuga, Jeremiah Ogunkunle, Emmanuel Kasumu, Oluwaseun Ogunsanya, Oluwatobiloba Oluseyije, Pelumi Ogunbayo, Omorojo Idume, Adeola Kasali, Sarah Adesi, Mariam Oyebowale, Damilola Ogungbemi, A. Aderinola, Emmanuel Irokosu, Abdullahi A. Murtala, B. Osalusi","doi":"10.3389/fnins.2024.1370533","DOIUrl":"https://doi.org/10.3389/fnins.2024.1370533","url":null,"abstract":"Sex steroid hormones are emerging significant biomarkers of depression among Women with Epilepsy (WWE) with promising prognostic potential and therapeutic end point. Therefore, the study is aimed at exploring the association between sex steroids hormones, Anti-seizure Medication (ASM) and depression among WWE.A baseline questionnaire was used to obtain socio-demographics and clinical characteristic from one hundred and twelve (112) WWE and 50 age matched healthy control. The diagnosis of epilepsy and Electroencephalography (EEG) description was based on 2017 International League Against Epilepsy (ILAE) criteria. Blood samples were collected from cases and control during Luteal Phase (LP) and Follicular Phase (FP). The Zung Self-Rating Depression Scale (ZSRDS) was used to assess depression.The prevalence of depression among WWE is 18.8%, with a significant difference between the level of formal education (p0.000), age (p0.000), and mean ZSRDS (p0.000) among cases and control. There is a statistical difference in hormonal levels between cases and control with regards to higher testosterone [3.28 ± 9.99 vs. 0.31 ± 0.30; p0.037], lower FP prolactin [16.37 ± 20.14 vs. 17.20 ± 7.44; p0.778], and lower LP prolactin [15.74 ± 18.22 vs. 17.67 ± 7.27; p0.473]. Testosterone (p0.024), FP Follicle Stimulating Hormone (FSH) (p0.009), FP Estradiol (p0.006), LP FSH (p0.031), LP Progesterone (p0.023), and LP Prolactin (p0.000) were associated with depression. However, only prolactin (p0.042) and testosterone (p0.000) predicts depression among WWE.There was higher mean depression score, lower prolactin and higher testosterone level among cases compared to control. Furthermore, there was lower prolactin and higher testosterone level in Carbamazepine (CBZ) group compared to Levetiracetam (LEV) groups.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"31 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140676338","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 : 2024-04-22DOI: 10.3389/fnins.2024.1294417
Siyuan Cui, Sainan Chen, Xuechao Wu, Qing Wang
Patients with pituitary neuroendocrine tumors (PitNETs) often experience neuropsychiatric disorders due to factors such as hormonal imbalances, and inadequate management of medications, surgeries, and radiation therapies. Commonly observed disorders include depression, anxiety, and cognitive dysfunction, which significantly impact patients’ quality of life and prognosis. PitNETs have a significant presence of immune cells within the tumor microenvironment (TME), predominantly macrophages and T lymphocytes. These immune cells secrete a variety of cytokines, growth factors, and chemokines, which regulate the biological behaviors of PitNETs, including tumor initiation, proliferation, migration, invasion, and angiogenesis. In addition, this review provides a pioneering summary of the close relationships between the aberrant secretion of proinflammatory cytokines within the TME of PitNETs and the occurrence of neuropsychiatric disorders, along with their potential underlying mechanisms. The cytokines produced as a result of TME dysregulation may affect various aspects of the central nervous system, including neurotransmitter metabolism, neuroendocrine function, and neurovascular plasticity, thereby leading to a higher susceptibility to neurobehavioral disorders in PitNET patients.
垂体神经内分泌肿瘤（PitNETs）患者经常会因荷尔蒙失调、药物治疗不当、手术和放射治疗等因素而出现神经精神障碍。常见的精神障碍包括抑郁、焦虑和认知功能障碍，严重影响患者的生活质量和预后。PitNET 的肿瘤微环境（TME）中存在大量免疫细胞，主要是巨噬细胞和 T 淋巴细胞。这些免疫细胞会分泌多种细胞因子、生长因子和趋化因子，调节 PitNET 的生物学行为，包括肿瘤的发生、增殖、迁移、侵袭和血管生成。此外，这篇综述还开创性地总结了 PitNET TME 内促炎细胞因子异常分泌与神经精神疾病发生之间的密切关系及其潜在的内在机制。TME失调产生的细胞因子可能会影响中枢神经系统的各个方面，包括神经递质代谢、神经内分泌功能和神经血管可塑性，从而导致PitNET患者更容易出现神经行为紊乱。
{"title":"Research status and prospects of pituitary adenomas in conjunction with neurological and psychiatric disorders and the tumor microenvironment","authors":"Siyuan Cui, Sainan Chen, Xuechao Wu, Qing Wang","doi":"10.3389/fnins.2024.1294417","DOIUrl":"https://doi.org/10.3389/fnins.2024.1294417","url":null,"abstract":"Patients with pituitary neuroendocrine tumors (PitNETs) often experience neuropsychiatric disorders due to factors such as hormonal imbalances, and inadequate management of medications, surgeries, and radiation therapies. Commonly observed disorders include depression, anxiety, and cognitive dysfunction, which significantly impact patients’ quality of life and prognosis. PitNETs have a significant presence of immune cells within the tumor microenvironment (TME), predominantly macrophages and T lymphocytes. These immune cells secrete a variety of cytokines, growth factors, and chemokines, which regulate the biological behaviors of PitNETs, including tumor initiation, proliferation, migration, invasion, and angiogenesis. In addition, this review provides a pioneering summary of the close relationships between the aberrant secretion of proinflammatory cytokines within the TME of PitNETs and the occurrence of neuropsychiatric disorders, along with their potential underlying mechanisms. The cytokines produced as a result of TME dysregulation may affect various aspects of the central nervous system, including neurotransmitter metabolism, neuroendocrine function, and neurovascular plasticity, thereby leading to a higher susceptibility to neurobehavioral disorders in PitNET patients.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673420","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 : 2024-04-19DOI: 10.3389/fnins.2024.1371195
Tsuyoshi Kitajima
{"title":"Commentary: Aripiprazole disrupts cellular synchrony in the suprachiasmatic nucleus and enhances entrainment to environmental light–dark cycles in mice","authors":"Tsuyoshi Kitajima","doi":"10.3389/fnins.2024.1371195","DOIUrl":"https://doi.org/10.3389/fnins.2024.1371195","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140684605","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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