Psychoradiology最新文献_第10页

Microbiota-gut-brain axis: the mediator of exercise and brain health. 微生物群-肠-脑轴：运动与大脑健康的媒介。

Psychoradiology Pub Date : 2024-04-19 eCollection Date: 2024-01-01 DOI: 10.1093/psyrad/kkae007

Piao Kang, Alan Zi-Xuan Wang

{"title":"Microbiota-gut-brain axis: the mediator of exercise and brain health.","authors":"Piao Kang, Alan Zi-Xuan Wang","doi":"10.1093/psyrad/kkae007","DOIUrl":"10.1093/psyrad/kkae007","url":null,"abstract":"The brain controls the nerve system, allowing complex emotional and cognitive activities. The microbiota-gut-brain axis is a bidirectional neural, hormonal, and immune signaling pathway that could link the gastrointestinal tract to the brain. Over the past few decades, gut microbiota has been demonstrated to be an essential component of the gastrointestinal tract that plays a crucial role in regulating most functions of various body organs. The effects of the microbiota on the brain occur through the production of neurotransmitters, hormones, and metabolites, regulation of host-produced metabolites, or through the synthesis of metabolites by the microbiota themselves. This affects the host's behavior, mood, attention state, and the brain's food reward system. Meanwhile, there is an intimate association between the gut microbiota and exercise. Exercise can change gut microbiota numerically and qualitatively, which may be partially responsible for the widespread benefits of regular physical activity on human health. Functional magnetic resonance imaging (fMRI) is a non-invasive method to show areas of brain activity enabling the delineation of specific brain regions involved in neurocognitive disorders. Through combining exercise tasks and fMRI techniques, researchers can observe the effects of exercise on higher brain functions. However, exercise's effects on brain health via gut microbiota have been little studied. This article reviews and highlights the connections between these three interactions, which will help us to further understand the positive effects of exercise on brain health and provide new strategies and approaches for the prevention and treatment of brain diseases.","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"4 ","pages":"kkae007"},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11096970/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain development of a school-aged boy with autism spectrum condition talented in arithmetic: a case report. 一名患有自闭症谱系障碍的学龄男孩在算术方面天赋异禀的大脑发育：个案报告。

Psychoradiology Pub Date : 2024-04-18 eCollection Date: 2024-01-01 DOI: 10.1093/psyrad/kkae008

Weixing Zhao, Lei Li, Xiujie Yang, Xiaotian Wang, Juan Kou, Jia Chen, Huafu Chen, Qi Wang, Xujun Duan

{"title":"Brain development of a school-aged boy with autism spectrum condition talented in arithmetic: a case report.","authors":"Weixing Zhao, Lei Li, Xiujie Yang, Xiaotian Wang, Juan Kou, Jia Chen, Huafu Chen, Qi Wang, Xujun Duan","doi":"10.1093/psyrad/kkae008","DOIUrl":"10.1093/psyrad/kkae008","url":null,"abstract":"Whereas autism spectrum condition is known for its social and communicative challenges, some autistic children demonstrate unusual islets of abilities including those related to mathematics, the neurobiological underpinnings of which are increasingly becoming the focus of research. Here we describe an 8-year-old autistic boy with intellectual and language challenges, yet exceptional arithmetic ability. He can perform verbal-based multiplication of three- and even four-digit numbers within 20 seconds. To gain insights into the neural basis of his talent, we investigated the gray matter in the child's brain in comparison to typical development, applying voxel-based morphometry to magnetic resonance imaging data. The case exhibited reduced gray matter volume in regions associated with arithmetic, which may suggest an accelerated development of brain regions with arithmetic compared to typically developing individuals: potentially a key factor contributing to his exceptional talent. Taken together, this case report describes an example of the neurodiversity of autism. Our research provides valuable insights into the potential neural basis of exceptional arithmetic abilities in individuals with the autism spectrum and its potential contribution to depicting the diversity and complexity of autism.","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"4 ","pages":"kkae008"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11074990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140878143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Focus on the sex-specific neural markers in the discrimination of various degrees of depression 关注鉴别不同程度抑郁症的性别特异性神经标记

Psychoradiology Pub Date : 2024-04-09 DOI: 10.1093/psyrad/kkae006

Yaqin Li, Xinyu Yan, Xianxin Meng, Jiajin Yuan

引用次数: 0

Transcriptomic and Neuroimaging Data Integration Enhances Machine Learning Classification of Schizophrenia 转录组和神经影像学数据整合增强了精神分裂症的机器学习分类能力

Psychoradiology Pub Date : 2024-03-26 DOI: 10.1093/psyrad/kkae005

Mengya Wang, Shu Zhao, Di Wu, Ya-Hong Zhang, Yan-Kun Han, Kun Zhao, Ting Qi, Yong Liu, Long-Biao Cui, Yongbin Wei

{"title":"Transcriptomic and Neuroimaging Data Integration Enhances Machine Learning Classification of Schizophrenia","authors":"Mengya Wang, Shu Zhao, Di Wu, Ya-Hong Zhang, Yan-Kun Han, Kun Zhao, Ting Qi, Yong Liu, Long-Biao Cui, Yongbin Wei","doi":"10.1093/psyrad/kkae005","DOIUrl":"https://doi.org/10.1093/psyrad/kkae005","url":null,"abstract":"\u0000 \u0000 \u0000 Schizophrenia is a polygenetic disorder associated with changes in brain structure and function. Integrating macroscale brain features and microscale genetic data may provide a more complete overview of the disease etiology and may serve as potential diagnostic markers for schizophrenia.\u0000 \u0000 \u0000 \u0000 We aim to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models.\u0000 \u0000 \u0000 \u0000 we collected brain imaging data and blood RNA-seq data from 43 schizophrenia patients and 60 age-, gender-matched healthy controls, and we extracted multi-omics features of macroscale brain morphology, brain structural connectivity and functional connectivity, and gene transcription of schizophrenia risk genes. Multi-scale data fusion was performed using a machine learning integration framework, together with several conventional machine learning methods and neural networks for patient classification.\u0000 \u0000 \u0000 \u0000 We found that multi-omics data fusion in conventional machine learning models achieved the highest accuracy in contrast to the single-modality models, with AUC improvements of 8.88% to 22.64%. Similar findings were observed for the neural network, showing an increase of 16.57% for the multimodal classification accuracy compared to the single-modal average. In addition, we identified several brain regions in the left posterior cingulate and right frontal pole that contribute to disease classification.\u0000 \u0000 \u0000 \u0000 We provide empirical evidence for the increased accuracy achieved by imaging genetic data integration in schizophrenia classification. Multi-scale data fusion holds promise for enhancing diagnostic precision, facilitating early detection and personalizing treatment regimens in schizophrenia.\u0000","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"76 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140377958","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}

引用次数: 0

Early diagnosis of autism spectrum disorder using structural connectivity biomarker 利用结构连接生物标记物早期诊断自闭症谱系障碍

Psychoradiology Pub Date : 2024-03-19 DOI: 10.1093/psyrad/kkae004

W. K. Lau, Mei-Kei Leung, Kean Poon, Ruibin Zhang

引用次数: 0

Self-objectification and Eating disorders: the psychopathological and neural processes from psychological distortion to psychosomatic illness 自我物化与进食障碍：从心理扭曲到心身疾病的心理病理学和神经过程

Psychoradiology Pub Date : 2024-02-28 DOI: 10.1093/psyrad/kkae003

Yinying Hu, Yafeng Pan, Liming Yue, Xiangping Gao

引用次数: 0

Identification of Parkinson's Disease Subtypes with Distinct Brain Atrophy Progression and its Association with Clinical Progression 识别具有不同脑萎缩进展的帕金森病亚型及其与临床进展的关系

Psychoradiology Pub Date : 2024-02-24 DOI: 10.1093/psyrad/kkae002

Guoqing Pan, Yuchao Jiang, Wei Zhang, Xuejuan Zhang, Linbo Wang, Wei Cheng

{"title":"Identification of Parkinson's Disease Subtypes with Distinct Brain Atrophy Progression and its Association with Clinical Progression","authors":"Guoqing Pan, Yuchao Jiang, Wei Zhang, Xuejuan Zhang, Linbo Wang, Wei Cheng","doi":"10.1093/psyrad/kkae002","DOIUrl":"https://doi.org/10.1093/psyrad/kkae002","url":null,"abstract":"\u0000 \u0000 \u0000 Studying of heterogeneity of Parkinson's disease (PD) is crucial for comprehending pathophysiological mechanisms underlying the disease. PD patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear.\u0000 \u0000 \u0000 \u0000 The objective of this study was to identify PD subtypes with different rates of GMV loss and to explore whether these subtypes were associated with clinical progression.\u0000 \u0000 \u0000 \u0000 Patients with PD (n = 107, mean age 60.06 ± 9.98 years, 70.09% male) who had baseline and at least three years of follow-up structural MRI scans were included in the study. Linear mixed-effects model (LME) was used to evaluate the rate of GMV loss for each patient at the regional level with adjusting for covariates. Hierarchical cluster analysis was applied to individual rate of GMV loss to test whether there exist different subtypes in PD. Longitudinal changes in clinical scores were compared between different subtypes.\u0000 \u0000 \u0000 \u0000 Hierarchical cluster analysis classified patients into two clusters based on their individual atrophy rates. Subtype 1 (n = 63) had moderate levels of atrophy rates in the prefrontal lobe and lateral temporal lobe, while subtype 2 (n = 44) was characterized by faster atrophy in almost the entire brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS Part Ⅰ, β=1.26 ± 0.18, P = 0.016) and motor (MDS-UPDRS Part Ⅱ, β=1.34 ± 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, β=1.15 ± 0.22, P = 0.043), memory (HVLT-Retention, β=-0.02 ± 0.01, P = 0.016) and depression (GDS, β=0.26 ± 0.083, P = 0.019) compared to subtype 1.\u0000 \u0000 \u0000 \u0000 The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.\u0000","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"3 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140434785","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}

引用次数: 0

The role of cortical midline structure in diagnoses and neuromodulation for major depressive disorder 皮层中线结构在重度抑郁障碍的诊断和神经调节中的作用

Psychoradiology Pub Date : 2024-01-30 DOI: 10.1093/psyrad/kkae001

Xinyuan Yan

引用次数: 0

Exploring Alzheimer's disease: a comprehensive brain connectome-based survey. 探索阿尔茨海默病：基于大脑连接体的综合调查。

IF 7.4

Psychoradiology Pub Date : 2024-01-11 eCollection Date: 2024-01-01 DOI: 10.1093/psyrad/kkad033

Lu Zhang, Junqi Qu, Haotian Ma, Tong Chen, Tianming Liu, Dajiang Zhu

{"title":"Exploring Alzheimer's disease: a comprehensive brain connectome-based survey.","authors":"Lu Zhang, Junqi Qu, Haotian Ma, Tong Chen, Tianming Liu, Dajiang Zhu","doi":"10.1093/psyrad/kkad033","DOIUrl":"10.1093/psyrad/kkad033","url":null,"abstract":"Dementia is an escalating global health challenge, with Alzheimer's disease (AD) at its forefront. Substantial evidence highlights the accumulation of AD-related pathological proteins in specific brain regions and their subsequent dissemination throughout the broader area along the brain network, leading to disruptions in both individual brain regions and their interconnections. Although a comprehensive understanding of the neurodegeneration-brain network link is lacking, it is undeniable that brain networks play a pivotal role in the development and progression of AD. To thoroughly elucidate the intricate network of elements and connections constituting the human brain, the concept of the brain connectome was introduced. Research based on the connectome holds immense potential for revealing the mechanisms underlying disease development, and it has become a prominent topic that has attracted the attention of numerous researchers. In this review, we aim to systematically summarize studies on brain networks within the context of AD, critically analyze the strengths and weaknesses of existing methodologies, and offer novel perspectives and insights, intending to serve as inspiration for future research.","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"4 ","pages":"kkad033"},"PeriodicalIF":7.4,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10848159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139708716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sex dimorphic cortical brain volumes associated with antisocial behavior in young adults. 与青少年反社会行为相关的大脑皮层性别二形体积。

Psychoradiology Pub Date : 2023-12-19 eCollection Date: 2023-01-01 DOI: 10.1093/psyrad/kkad031

Ke Ding, Miao Xu, Taicheng Huang, Yiying Song, Feng Kong, Zonglei Zhen

{"title":"Sex dimorphic cortical brain volumes associated with antisocial behavior in young adults.","authors":"Ke Ding, Miao Xu, Taicheng Huang, Yiying Song, Feng Kong, Zonglei Zhen","doi":"10.1093/psyrad/kkad031","DOIUrl":"https://doi.org/10.1093/psyrad/kkad031","url":null,"abstract":"Background: Although sex differences in antisocial behavior are well-documented, the extent to which neuroanatomical differences are related to sex differences in antisocial behavior is unclear. The inconsistent results from different clinical populations exhibiting antisocial behaviors are mainly due to the heterogeneity in etiologies, comorbidity inequality, and small sample size, especially in females.Objective: The study aimed to find sexual dimorphic brain regions associated with individual differences in antisocial behavior while avoiding the issues of heterogeneity and sample size.Methods: We collected structural neuroimaging data from 281 college students (131 males, 150 females) and analyzed the data using voxel-based morphometry.Results: The gray matter volume in three brain regions correlates with self-reported antisocial behavior in males and females differently: the posterior superior temporal sulcus, middle temporal gyrus, and precuneus. The findings have controlled for the total cortical gray matter volume, age, IQ, and socioeconomic status. Additionally, we found a common neural substrate of antisocial behavior in both males and females, extending from the anterior temporal lobe to the insula.Conclusion: This is the first neuroanatomical evidence from a large non-clinical sample of young adults. The study suggests that differences in males and females in reading social cues, understanding intentions and emotions, and responding to conflicts may contribute to the modulation of brain morphometry concerning antisocial behavior.","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"3 ","pages":"kkad031"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10917369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140869067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0