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Social intelligence mediates the protective role of resting-state brain activity in the social cognition network against social anxiety 社交智能介导社会认知网络中静息态大脑活动对社交焦虑的保护作用
Pub Date : 2024-04-24 DOI: 10.1093/psyrad/kkae009
Yingqiao Ma, Yuhan Zou, Xiqin Liu, Taolin Chen, Graham J. Kemp, Qiyong Gong, Song Wang
Social intelligence refers to an important psychosocial skill set encompassing an array of abilities, including effective self-expression, understanding of social contexts, and acting wisely in social interactions. While there is ample evidence of its importance in various mental health outcomes, particularly social anxiety, little is known on the brain correlates underlying social intelligence and how it can mitigate social anxiety. This research aims to investigate the functional neural markers of social intelligence and their relations to social anxiety. Data of resting-state functional magnetic resonance imaging and behavioral measures were collected from 231 normal students aged 16 to 20 years (48% male). Whole-brain voxel-wise correlation analysis was conducted to detect the functional brain clusters related to social intelligence. Correlation and mediation analyses explored the potential role of social intelligence in the linkage of resting-state brain activities to social anxiety. Social intelligence was correlated with neural activities (assessed as the fractional amplitude of low-frequency fluctuations, fALFF) among two key brain clusters in the social cognition networks: negatively correlated in left superior frontal gyrus (SFG) and positively correlated in right middle temporal gyrus (MTG). Further, the left SFG fALFF was positively correlated with social anxiety; brain–personality-symptom analysis revealed that this relationship was mediated by social intelligence. These results indicate that resting-state activities in the social cognition networks might influence a person's social anxiety via social intelligence: lower left SFG activity → higher social intelligence → lower social anxiety. These may have significance for developing neurobehavioral intervention to mitigate social anxiety.
社交智能是一种重要的社会心理技能,包含一系列能力,包括有效的自我表达、对社会环境的理解以及在社会交往中明智地行事。尽管有大量证据表明社交智能对各种心理健康结果(尤其是社交焦虑)的重要性,但人们对社交智能背后的大脑相关因素以及社交智能如何缓解社交焦虑却知之甚少。 本研究旨在调查社交智能的功能神经标记及其与社交焦虑的关系。 研究收集了 231 名 16 至 20 岁正常学生(48% 为男性)的静息态功能磁共振成像和行为测量数据。通过全脑体素相关分析,发现了与社交智能相关的大脑功能集群。相关分析和中介分析探讨了社交智力在静息态大脑活动与社交焦虑的联系中的潜在作用。 社会智力与社会认知网络中两个关键脑群的神经活动(评估为低频波动分数振幅,fALFF)相关:与左额上回(SFG)呈负相关,与右颞中回(MTG)呈正相关。此外,左侧 SFG 的 fALFF 与社交焦虑呈正相关;大脑-性格-症状分析表明,这种关系是由社交智力介导的。 这些结果表明,社会认知网络中的静息态活动可能会通过社会智力影响一个人的社会焦虑:左侧SFG活动较低→社会智力较高→社会焦虑较低。这可能对开发缓解社交焦虑的神经行为干预具有重要意义。
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引用次数: 0
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。
Pub Date : 2024-04-19 eCollection Date: 2024-01-01 DOI: 10.1093/psyrad/kkae007
Piao Kang, Alan Zi-Xuan Wang

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.

大脑控制着神经系统,可以进行复杂的情感和认知活动。微生物群-肠道-大脑轴是一条双向的神经、激素和免疫信号通路,可将胃肠道与大脑联系起来。在过去几十年中,肠道微生物群已被证实是胃肠道的重要组成部分,在调节人体各器官的大部分功能方面发挥着至关重要的作用。微生物群通过产生神经递质、激素和代谢物,调节宿主产生的代谢物,或通过微生物群自身合成代谢物,对大脑产生影响。这会影响宿主的行为、情绪、注意力状态和大脑的食物奖励系统。与此同时,肠道微生物群与运动之间也有着密切的联系。运动可以从数量和质量上改变肠道微生物群,这可能是定期体育锻炼对人类健康有广泛益处的部分原因。功能性磁共振成像(fMRI)是一种非侵入性方法,可显示大脑活动区域,从而划分出神经认知障碍所涉及的特定大脑区域。通过将运动任务与 fMRI 技术相结合,研究人员可以观察到运动对大脑高级功能的影响。然而,运动通过肠道微生物群对大脑健康的影响却鲜有研究。本文回顾并强调了这三种相互作用之间的联系,这将有助于我们进一步了解运动对大脑健康的积极影响,并为预防和治疗脑部疾病提供新的策略和方法。
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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。
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

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.

自闭症谱系以其社交和沟通方面的挑战而闻名,而一些自闭症儿童则表现出不同寻常的能力,包括与数学有关的能力,其神经生物学基础正日益成为研究的焦点。在这里,我们描述了一名 8 岁的自闭症男孩,他在智力和语言方面都面临挑战,但算术能力却出类拔萃。他能在 20 秒内完成三位数甚至四位数的乘法运算。为了深入了解他的天赋的神经基础,我们通过对磁共振成像数据进行体素形态测量,研究了孩子大脑灰质与典型发育情况的对比。该病例表现出与算术相关区域的灰质体积减少,这可能表明与正常发育的个体相比,与算术相关的大脑区域加速发育:这可能是导致他具有非凡天赋的一个关键因素。综上所述,本病例报告描述了自闭症神经多样性的一个实例。我们的研究为了解自闭症谱系中特殊算术能力的潜在神经基础提供了有价值的见解,并为描述自闭症的多样性和复杂性做出了潜在贡献。
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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
Pub Date : 2024-04-09 DOI: 10.1093/psyrad/kkae006
Yaqin Li, Xinyu Yan, Xianxin Meng, Jiajin Yuan
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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
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
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. We aim to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models. 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. 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. 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.
精神分裂症是一种与大脑结构和功能变化相关的多基因疾病。整合宏观尺度的大脑特征和微观尺度的遗传数据可以更全面地了解疾病的病因,并可作为精神分裂症的潜在诊断标志物。 我们收集了 43 名精神分裂症患者和 60 名年龄、性别匹配的健康对照者的脑成像数据和血液 RNA-seq 数据,提取了宏观脑形态学、脑结构连通性和功能连通性以及精神分裂症风险基因转录的多组学特征。多尺度数据融合采用了机器学习集成框架,并结合几种传统的机器学习方法和神经网络对患者进行分类。 我们发现,在传统的机器学习模型中,多组学数据融合的准确率与单模态模型相比最高,AUC提高了8.88%至22.64%。神经网络也有类似发现,多模态分类准确率比单模态平均准确率提高了 16.57%。此外,我们还发现了左侧扣带回后部和右侧额极的几个脑区有助于疾病分类。 我们为在精神分裂症分类中通过成像基因数据整合提高准确性提供了经验证据。多尺度数据融合有望提高精神分裂症的诊断精确度、促进早期检测和个性化治疗方案。
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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
Pub Date : 2024-03-19 DOI: 10.1093/psyrad/kkae004
W. K. Lau, Mei-Kei Leung, Kean Poon, Ruibin Zhang
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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
Pub Date : 2024-02-28 DOI: 10.1093/psyrad/kkae003
Yinying Hu, Yafeng Pan, Liming Yue, Xiangping Gao
Self-objectification, characterized by treating oneself primarily as a physical entity (A body) or a collection of body parts, has been linked to the development of eating disorders. Yet, the precise mechanisms underpinning this link have remained elusive. From a psychopathological perspective, this article proposes that both self-objectification and eating disorders can be seen as manifestations of self-rumination (repetitive, negative self-focus). While self-objectification involves psychological rumination, eating disorders encompass a complex interplay of psychological and physical (bodily) rumination. In addition, at the neural level, the underlying neural foundations underlying such self-rumination are likely rooted in brain activity and connectivity within networks associated with self-reference, cognitive control, and body perception. Collectively, these perspectives shed light on the psychopathological and neural processes that links self-objectification to the onset of eating disorders.
自我物化的特点是将自己主要视为一个物理实体(身体)或身体部位的集合,它与进食障碍的发生有关。然而,这种联系的确切机制却一直难以捉摸。本文从精神病理学的角度出发,提出自我物化和饮食失调都可以看作是自我唠叨(重复、消极的自我关注)的表现形式。自我物化涉及心理反刍,而进食障碍则包含心理和生理(身体)反刍的复杂相互作用。此外,在神经层面上,这种自我反刍的潜在神经基础很可能植根于与自我参照、认知控制和身体感知相关的大脑活动和网络连接中。总之,这些观点揭示了将自我物化与饮食失调症的发病联系起来的心理病理学和神经过程。
{"title":"Self-objectification and Eating disorders: the psychopathological and neural processes from psychological distortion to psychosomatic illness","authors":"Yinying Hu, Yafeng Pan, Liming Yue, Xiangping Gao","doi":"10.1093/psyrad/kkae003","DOIUrl":"https://doi.org/10.1093/psyrad/kkae003","url":null,"abstract":"\u0000 Self-objectification, characterized by treating oneself primarily as a physical entity (A body) or a collection of body parts, has been linked to the development of eating disorders. Yet, the precise mechanisms underpinning this link have remained elusive. From a psychopathological perspective, this article proposes that both self-objectification and eating disorders can be seen as manifestations of self-rumination (repetitive, negative self-focus). While self-objectification involves psychological rumination, eating disorders encompass a complex interplay of psychological and physical (bodily) rumination. In addition, at the neural level, the underlying neural foundations underlying such self-rumination are likely rooted in brain activity and connectivity within networks associated with self-reference, cognitive control, and body perception. Collectively, these perspectives shed light on the psychopathological and neural processes that links self-objectification to the onset of eating disorders.","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140420868","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
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
Pub Date : 2024-02-24 DOI: 10.1093/psyrad/kkae002
Guoqing Pan, Yuchao Jiang, Wei Zhang, Xuejuan Zhang, Linbo Wang, Wei Cheng
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. 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. 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. 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. 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.
研究帕金森病(PD)的异质性对于理解该病的病理生理机制至关重要。帕金森病患者的灰质体积(GMV)进行性减少,但帕金森病是否存在不同的萎缩进展模式仍不清楚。 本研究旨在确定具有不同灰质丢失率的帕金森病亚型,并探讨这些亚型是否与临床进展相关。 研究纳入了基线和至少三年随访结构磁共振成像扫描的帕金森病患者(n = 107,平均年龄为 60.06 ± 9.98 岁,70.09% 为男性)。线性混合效应模型(LME)用于评估每位患者在区域层面的GMV损失率,并对协变量进行调整。分层聚类分析适用于单个GMV丢失率,以检验是否存在不同的PD亚型。比较了不同亚型临床评分的纵向变化。 层次聚类分析根据患者的个体萎缩率将其分为两类。亚型1(n = 63)的前额叶和外侧颞叶萎缩率处于中等水平,而亚型2(n = 44)的特点是几乎整个大脑萎缩较快,尤其是外侧颞叶区域。此外,亚型 2 在非运动症状(MDS-UPDRS Part Ⅰ,β=1.26 ± 0.18,P = 0.016)和运动症状(MDS-UPDRS Part Ⅱ,β=1.34 ± 0.20,P = 0.与第一亚型相比,第二亚型患者的症状、自主神经功能障碍(SCOPA-AUT,β=1.15 ± 0.22,P = 0.043)、记忆力(HVLT-Retention,β=-0.02 ± 0.01,P = 0.016)和抑郁(GDS,β=0.26 ± 0.083,P = 0.019)均有所改善。 该研究发现了两种具有不同萎缩进展和临床进展模式的帕金森病亚型,这可能会对制定个性化治疗策略产生影响。
{"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
Pub Date : 2024-01-30 DOI: 10.1093/psyrad/kkae001
Xinyuan Yan
{"title":"The role of cortical midline structure in diagnoses and neuromodulation for major depressive disorder","authors":"Xinyuan Yan","doi":"10.1093/psyrad/kkae001","DOIUrl":"https://doi.org/10.1093/psyrad/kkae001","url":null,"abstract":"","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"280 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140484918","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
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。
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

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.

痴呆症是一个不断升级的全球性健康挑战,阿尔茨海默病(AD)是其首要病因。大量证据表明,与阿尔茨海默病相关的病理蛋白在特定脑区积累,随后沿着脑网络扩散到更广阔的区域,导致单个脑区及其相互联系的破坏。虽然目前还缺乏对神经变性-脑网络联系的全面了解,但不可否认的是,脑网络在AD的发生和发展过程中起着举足轻重的作用。为了彻底阐明构成人类大脑的错综复杂的元素和连接网络,人们提出了大脑连接组的概念。基于连接组的研究在揭示疾病发展机制方面具有巨大的潜力,它已成为一个突出的课题,吸引了众多研究人员的关注。在这篇综述中,我们旨在系统地总结有关注意力缺失症背景下大脑网络的研究,批判性地分析现有方法的优缺点,并提供新的视角和见解,以期对未来的研究有所启发。
{"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":"<p><p>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.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"4 ","pages":"kkad033"},"PeriodicalIF":0.0,"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
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Psychoradiology
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