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Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular–Structural–Functional Co-alterations

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Research 2024年第1期2023卷 471-487页

作者： Lin Jiang Yueheng Peng Runyang He Qingqing Yang Chanlin Yi Yuqin Li Bin Zhu Yajing Si Tao Zhang Bharat B.Biswal dezhong yao Lan Xiong Fali Li Peng Xu The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Life Science and Technology Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Psychology Xinxiang Medical UniversityXinxiang 453003China School of Science Xihua UniversityChengdu 610039China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJUSA School of Electrical Engineering Zhengzhou UniversityZhengzhou 450001China Research Unit of NeuroInformation Chinese Academy of Medical Sciences2019RU035ChengduChina Montreal Neurological Institute and Hospital McGill UniversityMontrealQCCanada Department of Electrical and Computer Engineering Faculty of Science and TechnologyUniversity of MacaoMacaoChina Radiation Oncology Key Laboratory of Sichuan Province 610041 ChengduChina Rehabilitation Center Qilu Hospital of Shandong UniversityJinan 250012China

Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain *** to lacking an effective approach to quantifying the covarying of structure and functional responses,how the structural–functional circuits interact and how genes encode the relationships,to deepen our knowledge of human cognition and disease,are still ***,we propose a multimodal covariance network(MCN)construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single *** further explored the potential association between brain-wide gene expression patterns and structural–functional covarying in individuals involved in a gambling task and individuals with major depression disorder(MDD),adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent *** analysis showed a replicable cortical structural–functional fine map in healthy individuals,and the expression of cognition-and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN *** analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences.

关键词： alterations evoked spatially

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Morphological disruption and visual tuning alterations in the primary visual cortex in glaucoma(DBA/2J)mice

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Neural Regeneration Research 2024年第1期19卷 220-225页

作者： Yin Yang Zhaoxi Yang Maoxia Lv Ang Jia Junjun Li Baitao Liao Jing’an Chen Zhengzheng Wu Yi Shi Yang Xia dezhong yao Ke Chen Department of Ophthalmology Sichuan Provincial People’s HospitalMedical SchoolUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina Research Unit of NeuroInformation Chinese Academy of Medical SciencesChengduSichuan ProvinceChina Institute of Basic Medical Sciences Chinese Academy of Medical Sciences School of Basic Medicine Peking Union Medical CollegeBeijingChina Health Management Center Sichuan Provincial Key Laboratory for Human Disease Gene StudySichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina Research Unit for Blindness Prevention of Chinese Academy of Medical Sciences(2019RU026) Sichuan Academy of Medical SciencesChengduSichuan ProvinceChina School of Electrical Engineering Zhengzhou UniversityZhengzhouHenan ProvinceChina

Glaucoma is a leading cause of irreve rsible blindness wo rldwide,and previous studies have shown that,in addition to affecting the eyes,it also causes abnormalities in the ***,it is not yet clear how the primary visual cortex(V1)is altered in *** study used DBA/2J mice as a model for spontaneous secondary *** aim of the study was to compare the electrophysiological and histomorphological chara cteristics of neurons in the V1between 9-month-old DBA/2J mice and age-matched C57BL/6J *** conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses,including single-unit spiking and gamma band *** morphology of layerⅡ/Ⅲneurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue ***-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were *** with the C57BL/6J group,V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial *** r,fewer neuro ns were observed in the V1 of DBA/2J mice compared with C57BL/6J *** findings suggest that DBA/2J mice have fewer neurons in the VI compared with C57BL/6J mice,and that these neurons have impaired visual *** findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse *** study might offer some innovative perspectives regarding the treatment of glaucoma.

关键词： DBA/2J degeneration gamma band oscillations glaucoma primary visual cortex(V1) retina single-unit recording tuning curve

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从脑-机接口到脑-器交互

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重庆邮电大学学报(自然科学版) 2024年第4期36卷 738-746页

作者：尧德中秦云电子科技大学生命科学与技术学院四川脑科学与类脑智能研究院

脑-机接口(brain computer interface, BCI)建立了大脑和非生物设备之间直接的信息交互通道。在认可BCI技术独特应用价值的基础上，进一步整合了身心医学与脑-机接口，强调了“脑与身体”和“脑与环境”的相互支撑关系，提出了脑-器交互... 详细信息

脑-机接口(brain computer interface, BCI)建立了大脑和非生物设备之间直接的信息交互通道。在认可BCI技术独特应用价值的基础上，进一步整合了身心医学与脑-机接口，强调了“脑与身体”和“脑与环境”的相互支撑关系，提出了脑-器交互(brain-apparatus communication, BAC)框架。对脑-机接口到脑-器交互的发展历程进行了概述，并从脑-器官交互、脑-外部环境交互以及两者融合方面讨论了脑器交互对人类健康的影响。

关键词：脑-机接口(BCI) 心身医学脑-器交互(BAC)

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基于典型相关分析的脑网络研究方法综述

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中国生物医学工程学报 2024年第2期43卷 240-251页

作者：尹顺杰陈凯薛开庆尧德中徐鹏张涛西华大学理学院成都610039 西华大学计算机与软件工程学院成都610039 电子科技大学神经信息教育部重点实验室成都610054

脑网络分析在研究大脑的认知活动、探究大脑的信息处理模式和辅助精神类疾病的诊断等方面都起着重要作用。近年来,基于多变量数据集的脑网络研究方法得到了普遍关注。典型相关分析(CCA)作为一种基于数据驱动的多元统计方法,能够有效捕... 详细信息

脑网络分析在研究大脑的认知活动、探究大脑的信息处理模式和辅助精神类疾病的诊断等方面都起着重要作用。近年来,基于多变量数据集的脑网络研究方法得到了普遍关注。典型相关分析(CCA)作为一种基于数据驱动的多元统计方法,能够有效捕捉多变量数据间的隐含关系,被广泛地应用于脑网络研究。综述CCA在脑网络研究中的作用、具体应用模式、存在的优势和局限性。首先,对传统的CCA其及常见变体的算法原理进行归纳总结;然后,阐述基于CCA分析方法在脑网络构建、脑网络分析、脑网络标记物识别方面的研究现状;最后,对基于CCA的脑网络研究方法进行总结并探讨未来研究的方向。

关键词：典型相关分析脑网络功能连接功能性磁共振成像(fMRI)

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基于脑电探索阿尔茨海默病识别的智能路径

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阿尔茨海默病及相关病杂志 2024年第2期7卷 129-133页

作者：何润洋姜林朱颜尧德中李发礼徐鹏电子科技大学生命科学与技术学院生物医学信息中心成都611731

阿尔茨海默病(Alzheimer'sdisease,AD)是一种不可逆的神经变性疾病。近年来,脑电图(electroencephalogram,EEG)信号分析和处理技术的发展使得EEG成为提取AD有价值信息的工具。系统地评述了基于脑电的AD识别与诊断,包括脑电信号采集、生... 详细信息

阿尔茨海默病(Alzheimer'sdisease,AD)是一种不可逆的神经变性疾病。近年来,脑电图(electroencephalogram,EEG)信号分析和处理技术的发展使得EEG成为提取AD有价值信息的工具。系统地评述了基于脑电的AD识别与诊断,包括脑电信号采集、生物标志物提取以及识别模型的选择。研究旨在探索个体化特征的诊断和治疗策略,为提高AD诊断的准确性和个体化治疗方案的制定提供参考。

关键词：阿尔茨海默病脑电图生物标志物识别模型

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Sleep deprivation reorganizes the dynamic configurations of default mode network activity during recovery sleep

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Science China(Technological Sciences) 2022年第7期65卷 1456-1469页

作者： CUI Yan WU ShengDun ZHAO Shi LONG Tianyao JIAN ZhaoXin YU Shuang ZHANG Ge BISWAL Bharat GUO DaQing XIA Yang yao dezhong The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuro InformationCentre for Information in MedicineSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 611731China Department of Neurosurgery Sichuan Provincial People's HospitalUniversity of Electronic Science and Technology of ChinaChengdu 610072China Chinese Academy of Sciences Sichuan Translational Medicine Research HospitalChengdu 610072China Research Unit of Neuro Information(2019RU035) Chinese Academy of Medical SciencesChengdu 611731China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNew Jersey 07102USA School of Electrical Engineering Zhengzhou UniversityZhengzhou 450001China

Sleep deprivation causes disturbances of the neural activity, leading to the impairment of brain functions. However, the exact mechanism of sleep deprivation and how it affects the dynamics of brain activity during the recovery sleep remains unclear. In the current study, we performed sleep deprivation experiments on ten adult rats, and recorded the local field potentials from default mode network(DMN) regions during sleep before and after sleep deprivation. The DMN dynamics was assessed with the configurations of coactive micropatterns(CAMPs) using our previously proposed CAMP method. Our analysis revealed that the effects of sleep deprivation on DMN dynamics in the slow-wave sleep(SWS) state and the rapid eye-movement sleep(REM)state were disparate. Dynamic configurations of DMN activity in the SWS state were significantly impaired after sleep deprivation, with increased occurrence of low-activity CAMP and reorganized transition structure across three CAMPs. Moreover,enhanced functional connectivity and improved efficiencies in all CAMP networks were observed during the SWS state in the recovery sleep. However, there were no significant alterations in either DMN dynamics or CAMP network structures in the REM sleep state after sleep deprivation. Our results described the alterations of DMN dynamics in different sleep states after sleep deprivation, and illustrated the differential effects of sleep deprivation on two sleep states. These findings demonstrated the underlying neural mechanisms of the effects of sleep deprivation on DMN activity during sleep and increased our understanding of the physiological roles of the DMN in maintain sleep homeostasis after sleep deprivation.

关键词： sleep deprivation sleep state default mode network coactive micropattern dynamic configuration efficiency

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Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3011-3027页

作者： Rui Wang yao Zhou Guangchun Luo Peng Chen dezhong Peng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer Science Sichuan UniversityChengdu610065China National Innovation Center for UHD Video Technology Chengdu610095China

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

关键词： Time series anomaly detection unsupervised feature learning feature fusion

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Multimodal collaborative BCI system based on the improved CSP feature extraction algorithm

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Virtual Reality & Intelligent Hardware 2022年第1期4卷 22-37页

作者： Cunbo LI Ning LI Yuan QIU Yueheng PENG Yifeng WANG Lili DENG Teng MA Fali LI dezhong yao Peng XU The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation and School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 610054China School of Intelligent Engineering Zhengzhou University of AeronauticsZhengzhou 450015China

Background As a novel approach for people to directly communicate with an external device,the study of brain-computer interfaces(BCIs)has become ***,similar to the real-world scenario,where individuals are expected to work in groups,the BCI systems should be able to replicate group *** We proposed a 4-order cumulants feature extraction method(CUM4-CSP)based on the common spatial patterns(CSP)*** experiments conducted using motion visual evoked potentials(mVEP)EEG data verified the robustness of the proposed *** addition,to freely choose paradigms,we adopted the mVEP and steady-state visual evoked potential(SSVEP)paradigms and designed a multimodal collaborative BCI system based on the proposed CUM4-CSP *** feasibility of the proposed multimodal collaborative system framework was demonstrated using a multiplayer game controlling system that simultaneously facilitates the coordination and competitive control of two users on external *** verify the robustness of the proposed scheme,we recruited 30 subjects to conduct online game control experiments,and the results were statistically *** The simulation results prove that the proposed CUM4-CSP algorithm has good noise *** online experimental results indicate that the subjects could reliably perform the game confrontation operation with the selected BCI *** The proposed CUM4-CSP algorithm can effectively extract features from EEG data in a noisy ***,the proposed scheme may provide a new solution for EEG-based group BCI research.

关键词： Collaborative brain-computer interface(BCI) Motion visual evoked potentials(mVEP) Steady-state visual evoked potential(SSVEP) Game controlling system

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Attentional orienting and response inhibition: insights from spatial-temporal neuroimaging

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Neuroscience Bulletin 2014年第1期30卷 141-152页

作者： Yin Tian Shanshan Liang dezhong yao Bio-information College Chongqing University of Posts and Telecommunications Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and Technology University of Electronic Science and Technology of China

Attentional orienting and response inhibition have largely been studied separately. Each has yielded important findings, but controversy remains concerning whether they share any neurocognitive processes. These conflicting findings may originate from two issues：（1） at the cognitive level, attentional orienting and response inhibition are typically studied in isolation; and （2） at the technological level, a single neuroimaging method is typically used to study these processes. This article reviews recent achievements in both spatial and temporal neuroimaging, emphasizing the relationship between attentional orienting and response inhibition. We suggest that coordinated engagement, both top-down and bottom-up, serves as a common neural mechanism underlying these two cognitive processes. In addition, the right ventrolateral prefrontal cortex may play a major role in their harmonious operation.

关键词： attentional orienting response inhibition bottom-up driven top-down control event-related potentials functional magnetic resonance imaging

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Stochastic fluctuations of permittivity coupling regulate seizure dynamics in partial epilepsy

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Science China(Technological Sciences) 2017年第7期60卷 995-1002页

作者： GUO DaQing XIA Chuan WU ShengDun ZHANG TianJiao ZHANG YangSong XIA Yang yao dezhong Key Laboratory for Neurolnformation of Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 610054China Center for Information in Medicine University of Electronic Science and Technology of ChinaChengdu 610054China School of Computer Science and Technology Southwest University of Science and TechnologyMianyang 621002China

Partial epilepsy is characterized by recurrent seizures that arise from a localized pathological brain region. During the onset of partial epilepsy, the seizure evolution commonly exhibits typical timescale separation phenomenon. This timescale separation behavior can be mimicked by a paradigmatic model termed as Epileptor, which consists of coupled fast-slow neural populations via a permittivity variable. By incorporating permittivity noise into the Epileptor model, we show here that stochastic fluctuations of permittivity coupling participate in the modulation of seizure dynamics in partial epilepsy. In particular, introducing a certain level of permittivity noise can make the model produce more comparable seizure-like events that capture the temporal variability in realistic partial seizures. Furthermore, we observe that with the help of permittivity noise our stochastic Epileptor model can trigger the seizure dynamics even when it operates in the theoretical nonepileptogenic regime. These findings establish a deep mechanistic understanding on how stochastic fluctuations of permittivity coupling shape the seizure dynamics in partial epilepsy,and provide insightful biological implications.

关键词： partial epilepsy seizures dynamics Epileptor model slow permittivity variable stochastic fluctuations of permittivity coupling

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