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Individualized brain mapping for navigated neuromodulation

Chinese Medical Journal 2024年第5期137卷 508-523页

作者： Chaohong Gao Xia Wu Xinle Cheng Kristoffer Hougaard Madsen Congying Chu Zhengyi Yang lingzhong fan Sino–Danish College University of Chinese Academy of SciencesBeijing 100190China Brainnetome Center National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Department of Applied Mathematics and Computer Science Technical University of DenmarkKongens Lyngby 2800Denmark Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital Amager and HvidovreHvidovre 2650Denmark CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Health and Life Sciences University of Health and Rehabilitation SciencesQingdaoShandong 266000China

The brain is a complex organ that requires precise mapping to understand its structure and function.Brain atlases provide a powerful tool for studying brain circuits,discovering biological markers for early diagnosis,and developing personalized treatments for neuropsychiatric disorders.Neuromodulation techniques,such as transcranial magnetic stimulation and deep brain stimulation,have revolutionized clinical therapies for neuropsychiatric disorders.However,the lack of fine-scale brain atlases limits the precision and effectiveness of these techniques.Advances in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation systems.Still,the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized solutions.The article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles,advantages,disadvantages,and future trends of these techniques.The article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.

关键词： Brain atlas Individualization Navigated neuromodulation Multimodal magnetic resonance imaging Transcranial magnetic stimulation Deep brain stimulation

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Decoding human brain functions: Multi-modal, multi-scale insights

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The Innovation 2024年第1期5卷 17-18页

作者： Camilla T.Erichsen Deying Li lingzhong fan Sino-Danish Center University of Chinese Academy of SciencesBeijing 100190China Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China Core Centre for Molecular Morphology Section for Stereology and MicroscopyDepartment of Clinical MedicineAarhus University8200 Aarhus NDenmark School of Health and Life Sciences University of Health and Rehabilitation SciencesQingdao 266000China

Unraveling the intricate relationship between the structure and function of the human brain remains a central and unresolved question in neuroscience.Ethical considerations impose significant constraints on invasive techniques in human neuroscience research.Consequently,knowledge about human brain function often relies on animal models to provide valuable discoveries and insights.However,caution is warranted,as findings from animal studies may not always be directly translatable to humans,especially when investigating higher cognitive functions.

关键词： functions insight modal

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Mapping Underlying Maturational Changes in Human Brain

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Neuroscience Bulletin 2017年第4期33卷 478-480页

作者： lingzhong fan Tianzi Jiang Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Key Laboratory for neuro information of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 625014China The Queensland Brain Institute University of QueenslandBrisbaneQLD 4072Australia University of Chinese Academy of Sciences Beijing 100190China

Human brain development is a complex process that continues between birth and maturity, and monitoring the underlying maturational changes at these stages is crucial for our understanding of typical development as well as neurodevelopmental disorders. During the critical periods of brain development, on one hand, many human capacities originate, but on the other hand, a brain undergoing rapid plastic changes may also be vulnerable to neuropsychiatric disorders . Multi-modal magnetic resonance imaging （MRI） has been increasingly used for its ability to noninvasively reveal structural and functional changes in the brain. However, interpretation of the neurobiological processes underlying the findings obtained with MRI is very limited .

关键词： Mapping Underlying Maturational Changes in Human Brain is for it been of that from have

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Developing Neuroimaging Biomarker for Brain Diseases with a Machine Learning Framework and the Brainnetome Atlas

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Neuroscience Bulletin 2021年第10期37卷 1523-1525页

作者： Weiyang Shi lingzhong fan Tianzi Jiang Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 610054China The Queensland Brain Institute University of QueenslandBrisbaneQLD 4072Australia

Neuroimaging made it possible to quantify brain structure and function.However,there are few neuroimaging biomarkers for the early diagnosis,prognosis,and evaluation of therapy for brain diseases.The development of neuroimaging biomarkers for brain diseases faces two major bottleneck problems.First,the neuroimaging datasets of brain diseases are always characterized by small sample size,high dimension,and large heterogeneity.Second,a fine-grained individualized human brain atlas for effective dimensionality reduction has always been lacking.

关键词： function diagnosis diseases

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Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia

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Neuroscience Bulletin 2015年第2期31卷 207-219页

作者： Yuan Zhou lingzhong fan Chenxiang Qiu Tianzi Jiang Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Magnetic Resonance Imaging Research Center Institute of Psychology Chinese Academy of Sciences Brainnetome Center Institute of Automation Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences The Queensland Brain Institute The University of Queensland Key Laboratory for Neuro Information of the Ministry of Education School of Life Science and Technology University of Electronic Science and Technology of China

Schizophrenia is hypothesized to arise from disrupted brain connectivity. This ＂dysconnectivity hypothesis＂ has generated interest in discovering whether there is anatomical and functional dysconnectivity between the prefrontal cortex （PFC） and other brain regions, and how this dysconnectivity is linked to the impaired cognitive functions and aberrant behaviors of schizophrenia. Critical advances in neuroimaging technologies, including diffusion tensor imaging （DTI） and functional magnetic resonance imaging （fMRI）, make it possible to explore these issues. DTI affords the possibility to explore anatomical connectivity in the human brain in vivo and fMRI can be used to make inferences about functional connections between brain regions. In this review, we present major advances in the understanding of PFC anatomical and functional dysconnectivity and their implications in schizophrenia. We then briefly discuss future prospects that need to be explored in order to move beyond simple mapping of connectivity changes to elucidate the neuronal mechanisms underlying schizophrenia.

关键词： prefrontal cortex schizophrenia anatomical connectivity functional connectivity

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The SACT Template:A Human Brain Diffusion Tensor Template for School-age Children

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Neuroscience Bulletin 2022年第6期38卷 607-621页

作者： Congying Chu Haoran Guan Sangma Xie Yanpei Wang Jie Luo Gai Zhao Zhiying Pan Mingming Hu Weiwei Men Shuping Tan Jia-Hong Gao Shaozheng Qin Yong He lingzhong fan Qi Dong Sha Tao National Key Laboratory of Cognitive Neuroscience and Learning&IDG/McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China Bejing Key Laboratory of Brain Imaging and Connectomics Beijing Normal UniversityBejing 100875China Center for MRI Research Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China Beijing City Key Laboratory for Medical Physics and Engineering Institute of Heavy lon PhysicsSchool of PhysicsPeking UniversityBeijing 100871China MeGovern Institute for Brain Research Peking UniversityBeijing 100871China Psychiatry Research Center Bejing HuiLongGuan HospitalPeking UniversityBejing 100096China Division of Molecular Neuroimaging Institute of Neuroscience and Medicine-2Research Center Julich52425 JilichGermany Institute of Biomedical Engineering and Instrumentation School of AutomationHangzhou Dianzi UniversityHangzhou 310018China Brainnetome Center and National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China

School-age children are in a specific development stage corresponding to juvenility,when the white matter of the brain experiences ongoing maturation.Dffusion-weighted magnetic resonance imaging(DWI),especially diffusion tensor imaging(DTI),is extensively used to characterize the maturation by assessing white matter properties in vivo.In the analysis of DWI data,spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space.Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization.However,there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development.Here,we established the school-age children diffusion tensor(SACT)template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years.With an age-balanced design,the SACT template represented the entire age range well by showing high similarity to the age-specific templates.Compared with the tensor template of adults,the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of schoolage children.A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template.Although similar spatial patterns were found,the SACT template showed significant effects on the distributions of the statistical results,which may be related to the performance of spatial normalization.Looking forward,the SACT template could contribute to future studies of white matter development in both healthy and clinical populations.The SACT template is publicly available now(tp://igshare com/aricles/dataseu'SACT_.template/14071283).

关键词： School-age children Diffusion-weighted MRI Diffusion tensor template Spatial normalization

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Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles

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Neuroscience Bulletin 2020年第12期36卷 1454-1473页

作者： Bin He Long Cao Xiaoluan Xia Baogui Zhang Dan Zhang Bo You lingzhong fan Tianzi Jiang School of Mechanical and Power Engineering Harbin University of Science and TechnologyHarbin 150080China Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences(CAS)Beijing 100190China Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationCASBeijing 100190China Key Laboratory for Neuroinformation of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 610054China The Queensland Brain Institute University of QueenslandBrisbaneQLD 4072Australia University of CAS Beijing 100049China College of Information and Computer Taiyuan University of TechnologyTaiyuan 030600China Chinese Institute for Brain Research Beijing 102206China Core Facility Center of Biomedical AnalysisTsinghua UniversityBeijing 100084China

The frontal pole cortex(FPC)plays key roles in various higher-order functions and is highly developed in non-human primates.An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results.This is important for understanding the functional architecture of the cerebral cortex.Here,combining cross-validation and principal component analysis,we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC(2 males and 6 females)into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4 T Bruker system,and then revealed their subregional connections.Furthermore,we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions,and found that the dorsolateral FPC,which contains an extension to the medial FPC,was mainly connected to regions of the default-mode network.The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network.These results enhance our understanding of the anatomy and circuitry of the macaque brain,and contribute to FPC-related clinical research.

关键词： Macaque Frontal pole cortex Anatomical connectivity profile Parcellation Neuroimaging Principal component analysis

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Mapping the Human Brain:What Is the Next Frontier?

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The Innovation 2021年第1期2卷 102-103页

作者： lingzhong fan Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

The human brain is considered to be the most complex and mysterious organ of our body.Due to technological and ethical considerations,we have not been able to fully map the multi-scale structures and understand the organizing principles.As the origin of human consciousness and intelligence,it inspires awe and marvel.Since the beginning of the 21st century,brain science has increasingly become popular around the whole world,and numerous large-scale brain initiatives have been launched to explore the structure and function of the human brain,to decode the nature of consciousness,and to address the problem of neuropsychiatric diseases.

关键词： Mapping consciousness Human

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基于结构磁共振的深度学习模型评估肝硬化及肝性脑病患者的脑龄改变

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中华医学杂志 2024年第4期104卷 269-275页

作者：李芳菲张晓东卢子宁陈诚徐君海樊令仲程悦天津市第一中心医院放射科天津市影像医学研究所天津300192 天津市认知计算与应用重点实验室天津大学智能与计算学部天津300350 中国科学院自动化研究所脑网络组研究中心北京100190

目的基于深度学习及全脑T 1高分辨MRI建立脑龄预测模型,依此探讨肝硬化及肝性脑病(HE)患者大脑衰老趋势,并试图揭示肝硬化及HE加速大脑衰老的具体损伤区域。方法横断面研究。从来自全球的8个公开数据库中选取3609名健康个体的全脑T 1高... 详细信息

目的基于深度学习及全脑T 1高分辨MRI建立脑龄预测模型,依此探讨肝硬化及肝性脑病(HE)患者大脑衰老趋势,并试图揭示肝硬化及HE加速大脑衰老的具体损伤区域。方法横断面研究。从来自全球的8个公开数据库中选取3609名健康个体的全脑T 1高分辨MRI数据作为训练集搭建基于3D全卷积神经网络的脑龄预测模型。通过计算实际年龄和预测脑龄间平均绝对误差(MAE)和皮尔逊相关系数(r)、决定系数(R^(2))来评价模型的预测效果。再从公开数据库中的人类连接组计划中制作一个测试集(n=555)来测试模型的准确性。招募2013年12月至2020年5月于天津市第一中心医院就诊的136例肝硬化患者作为病例组(无HE肝硬化组79例和伴HE肝硬化组57例),同期向社会招募70名健康者作为健康对照组。计算所有受试者的预测脑龄与实际年龄差值(Brain-PAD)、数字连接试验-A(NCT-A)和数字-符号试验(DST)评分,评估健康对照组、无HE肝硬化组及伴HE肝硬化组3组间大脑衰老程度及认知功能,并采用网络遮挡敏感性分析来评估各脑区在脑龄预测中的重要性。结果训练集中该脑龄预测模型的实际年龄和预测脑龄间MAE=2.85,r=0.98,R^(2)=0.96;测试集中,MAE=4.45,r=0.96,R^(2)=0.92。在本地数据集中,健康对照组该模型的MAE=3.77,r=0.85,R^(2)=0.73。两组肝硬化患者NCT-A用时均长于健康对照组,而DST得分则均低于健康对照组,差异均有统计学意义(均P<0.001)。健康对照组Brain-PAD为(0.8±4.5)岁,无HE组Brain-PAD为(6.9±8.1)岁,伴HE组为(10.2±7.7)岁,3组间差异有统计学意义(P<0.001),且两两组间比较差异亦均有统计学意义(均P<0.05)。肝硬化患者视觉网络、躯体运动网络的预测重要性占比增加,伴HE组较无HE组占比进一步增加。结论肝硬化患者的认知功能减低,大脑加速衰老,这些改变在HE患者中更加明显。各脑区在预测脑年龄中的重要性差异为识别肝硬化和HE加速大脑衰老的特定损伤区域提供了新思路。

关键词：肝硬化肝性脑病磁共振成像脑龄深度学习

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Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer’s Disease

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Neuroscience Bulletin 2023年第10期39卷 1533-1543页

作者： Yida Qu Pan Wang Hongxiang Yao Dawei Wang Chengyuan Song Hongwei Yang Zengqiang Zhang Pindong Chen Xiaopeng Kang Kai Du lingzhong fan Bo Zhou Tong Han Chunshui Yu Xi Zhang Nianming Zuo Tianzi Jiang Yuying Zhou Bing Liu Ying Han Jie Lu Yong Liu Multi-Center Alzheimer’s Disease Imaging(MCADI)Consortium Brainnetome Center and National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Neurology Tianjin Huanhu HospitalTianjin UniversityTianjin 300222China Department of Radiology Department of Epidemiology and Health StatisticsSchool of Public HealthQilu Hospital of Shandong UniversityJi’nan 250063China Department of Neurology Qilu Hospital of Shandong UniversityJi’nan 250063China Department of Radiology Xuanwu Hospital of Capital Medical UniversityBeijing 100053China Branch of Chinese PLA General HospitalSanya 572022China Department of Neurology The Second Medical CentreNational Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijing 100089China Department of Radiology Tianjin Huanhu HospitalTianjin 300222China Department of Radiology Tianjin Medical University General HospitalTianjin 300052China Department of Neurology Xuanwu Hospital of Capital Medical UniversityBeijing 100053China Beijing Institute of Geriatrics Beijing 100053China National Clinical Research Center for Geriatric Disorders Beijing 100053China Center of Alzheimer’s Disease Beijing Institute for Brain DisordersBeijing 100053China State Key Lab of Cognition Neuroscience&Learning Beijing Normal UniversityBeijing 100091China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China 不详

Alzheimer’s disease(AD)is associated with the impairment of white matter(WM)tracts.The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets[321 patients with AD,265 patients with mild cognitive impairment(MCI),279 normal controls(NC)],a unified pipeline,and independent site cross-validation.Automated fiber quantification was used to extract diffusion profiles along tracts.Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC.Machine learning models using tract-based features showed good generalizability among independent site cross-validation.The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups.We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.

关键词： Alzheimer’s disease Diffusion tensor imaging White matter tracts Cross-validation

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