检索结果-内蒙古大学图书馆

Parsing disease heterogeneity in structural and functional MRI-derived measures using normative modeling and Generative Adversarial Networks (GANs)

Parsing disease heterogeneity in structural and functional M...

引用

Medical Imaging 2025: Computer-Aided Diagnosis

作者： Chintapalli, Sai Spandana Govindarajan, Sindhuja T. Shou, Haochang Fan, Yong Huang, Hao Davatzikos, Christos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia19104 United States Department of Radiology Children’s Hospital of Philadelphia Philadelphia19104 United States Department of Radiology University of Pennsylvania Philadelphia19104 United States

ISBN: (纸本)9781510685925

We present a preliminary analysis of a GAN-based normative modeling technique for capturing individual-level deviations in brain measures, addressing heterogeneity in neurological disorders. By leveraging self-supervised training on pseudo-synthetically simulated patient data, our method detects disease-related effects without the need for large, disease-specific datasets. We demonstrate the versatility of this approach by applying it to structural MRI and resting-state fMRI data, identifying neuroanatomical and functional connectivity deviations in Alzheimer’s disease (AD) and Traumatic Brain Injury (TBI). This model’s ability to accurately capture disease-related abnormalities in brain measures highlights its potential as a powerful tool for personalized diagnosis and the study of brain disorders, opening new avenues for research. © 2025 SPIE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Transcriptional and Neurochemical Signatures of Cerebral Blood Flow Alterations in Individuals With Schizophrenia or at Clinical High Risk for Psychosis

引用

Biological Psychiatry 2025年

作者： Knight, Samuel R. Abbasova, Leyla Zeighami, Yashar Hansen, Justine Y. Martins, Daniel Zelaya, Fernando Dipasquale, Ottavia Liu, Thomas Shin, David Bossong, Matthijs Azis, Matilda Antoniades, Mathilde Howes, Oliver D. Bonoldi, Ilaria Egerton, Alice Allen, Paul McGuire, Philip Modinos, Gemma Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Centre for Developmental Neurobiology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Medical Research Council Centre for Neurodevelopmental Disorders King's College London London United Kingdom Douglas Research Centre Department of Psychiatry McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Olea Medical La Ciotat France Centre for Functional MRI University of California San Diego San Diego CA United States Global MR Applications and Workflow GE Healthcare Menlo Park CA United States Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Department of Psychiatry Brain Center Rudoph Magnus University Medical Center Utrecht Utrecht Netherlands Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA United States Department of Psychiatry Oxford University Oxford United Kingdom

Background: The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behavior. Understanding relationships across these scales may be fundamental to advancing understanding of brain function in health and disease. Recent neuroimaging research has shown that functional brain alterations that are associated with schizophrenia spectrum disorders (SSDs) are already present in young adults at clinical high risk for psychosis (CHR-P), but the cellular and molecular determinants of these alterations remain unclear. Methods: Here, we used regional cerebral blood flow (rCBF) data from 425 individuals (122 with an SSD compared with 116 healthy control participants [HCs] and 129 individuals at CHR-P compared with 58 HCs) and applied a novel pipeline to integrate brainwide rCBF case-control maps with publicly available transcriptomic data (17,205 gene maps) and neurotransmitter atlases (19 maps) from 1074 healthy volunteers. Results: We identified significant correlations between astrocyte, oligodendrocyte, oligodendrocyte precursor cell, and vascular leptomeningeal cell gene modules for both SSD and CHR-P rCBF phenotypes. Additionally, endothelial cell genes were correlated in SSD, and microglia in CHR-P. Receptor distribution significantly predicted case-control rCBF differences, with dominance analysis highlighting dopamine (D₁, D₂, dopamine transporter), acetylcholine (VAChT, M₁), gamma-aminobutyric acid A (GABA_A), and glutamate (NMDA) receptors as key predictors for SSD (R²_adjusted = 0.58, false discovery rate [FDR]–corrected p <.05) and CHR-P (R²_adjusted = 0.6, p_FDR <.05) rCBF phenotypes. These associations were primarily localized in subcortical regions and implicate cell types involved in stress response and inflammation, alongside specific neuroreceptor systems, in shared and distinct rCBF phenotypes in psychos

关键词： Arterial spin labeling Biomarkers Gene expression Mental health Neuroimaging Neurotransmitters

来源：评论

学校读者我要写书评

暂无评论

Radiomic and proteomic signatures of body mass index on brain ageing and Alzheimer's-like patterns of brain atrophy

引用

eBioMedicine 2025年 116卷

作者： Filippos Anagnostakis Michail Kokkorakis Keenan A. Walker Ioanna Skampardoni Junhao Wen Guray Erus Duygu Tosun Vasiliki Tassopoulou Yuhan Cui Sindhuja T. Govindarajan Dhivya Srinivasan Randa Melhem Elizabeth Mamourian Haochang Shou Ilya M. Nasrallah Christos S. Mantzoros Christos Davatzikos Department of Medical and Surgical Sciences Alma Mater University of Bologna Bologna Italy Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Department of Medicine Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA Department of Clinical Pharmacy and Pharmacology University of Groningen University Medical Center Groningen Groningen the Netherlands Laboratory of Behavioral Neuroscience National Institute on Aging Intramural Research Program Baltimore MD USA Laboratory of AI and Biomedical Science (LABS) Columbia University New York NY USA Department of Radiology and Biomedical Imaging University of California San Francisco USA Department of Radiology University of Pennsylvania Philadelphia PA USA Section of Endocrinology Boston VA Healthcare System Harvard Medical School Boston MA USA

Background The impact of high body mass index (BMI) states and associated proteomic factors on brain ageing and Alzheimer's disease (AD) remains unclear. Methods We sought to evaluate machine learning (ML)-based neuroimaging markers of brain age and AD-like brain atrophy in participants with obesity or overweight without diagnosed cognitive impairment (WODCI), in a harmonised study of 46,288 participants in 15 studies (the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) consortium). We also assessed the association between cognition, serum proteins, and brain ageing indices. Data were acquired between 1999 and 2020 and analysed from November 2024 onwards. Findings The study comprised 46,288 participants, including 24,897 females and 21,391 males, with a mean age of 64.33 years (SD = 8.13) and a mean BMI of 26.81 kg/m 2 (SD = 4.49). The results demonstrate that the impact of obesity on brain ageing, and AD-like brain atrophy is weaker with increasing age and is significantly pronounced in males compared to females. Additionally, in males, obesity was significantly associated with approximately 2 additional years of brain ageing compared to normal weight and 1 additional year compared to the overweight group. Males with overweight also showed higher brain ageing values (8 additional months) than males with normal weight. Regarding AD-like brain atrophy, males with obesity displayed higher AD-like brain atrophy than males with normal weight, but females with normal weight showed higher AD-like brain atrophy than females with overweight. Sex differences within the same BMI categories were observed, with males exhibiting increased brain ageing compared to females, in obesity (1 additional year) and overweight groups (3 additional months). Higher AD-like brain atrophy was observed in males with overweight than in females with overweight. Females with normal weight displayed increased brain ageing (8 additional months) and AD-like brain

关键词： Ageing Alzheimer's Brain age gap Proteomics

来源：评论

学校读者我要写书评

暂无评论

Mutually-Constrained Cross-Sectional and Longitudinal Non-Negative Matrix Factorization: Application to Modeling Brain Aging Trajectories

Mutually-Constrained Cross-Sectional and Longitudinal Non-Ne...

引用

IEEE International Symposium on biomedical Imaging

作者： Ioanna Skampardoni Junhao Wen Erus Guray Haochang Shou Konstantina Nikita Christos Davatzikos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Laboratory of AI and Biomedical Science (LABS) Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.

关键词： Pathology Magnetic resonance imaging Precision medicine Sociology Aging Hazards Trajectory

来源：评论

学校读者我要写书评

暂无评论

Subtyping brain diseases from imaging data

arXiv

引用

arXiv 2022年

作者： Wen, Junhao Varol, Erdem Yang, Zhijian Hwang, Gyujoon Dwyer, Dominique Kazerooni, Anahita Fathi Lalousis, Paris Alexandros Davatzikos, Christos Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia United States Department of Statistics Center for Theoretical Neuroscience Zuckerman Institute Columbia University New York United States Department of Psychiatry and Psychotherapy Ludwig-Maximilian University Munich Germany Institute for Mental Health and Centre for Human Brain Health School of Psychology University of Birmingham Birmingham United Kingdom

The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment. Clinical neuroscience and cancer imaging have been two areas in which ML has offered particular promise. However, many neurologic and neuropsychiatric diseases, as well as cancer, are often heterogeneous in terms of their clinical manifestations, neuroanatomical patterns or genetic underpinnings. Therefore, in such cases, seeking a single disease signature might be ineffectual in delivering individualized precision diagnostics. The current chapter focuses on ML methods, especially semi-supervised clustering, that seek disease subtypes using imaging data. Work from Alzheimer's Disease and its prodromal stages, psychosis, depression, autism, and brain cancer are discussed. Our goal is to provide the readers with a broad overview in terms of methodology and clinical applications. © 2022, CC BY.

关键词： Neuroimaging

来源：评论

学校读者我要写书评

暂无评论

White Matter Connectometry In Patients With Disorders of Consciousness Revealed By 7-Tesla Magnetic Resonance Imaging

Research Square

引用

Research Square 2021年

作者： Tan, Xufei Zhou, Zhen Gao, Jian Wei, Ruili Zhang, Xiaotong Luo, Benyan Department of Clinical Medicine School of Medicine Zhejiang University City College Hangzhou China Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Handzhou Mingzhou Naokang Rehabilitation hospital Hangzhou China Department of Neurology & Brain Medical Centre The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China Second Affiliated Hospital of Zhejiang University School of Medicine Zhejiang University Hangzhou China

White matter disruption plays an important role in disorders of consciousness (DOC). The aim of this study was to analyze the connectometry between DOC patients and healthy controls and to explore the relationship between diffusion connectometry and levels of consciousness. Fourteen patients with DOC and 13 sex- and age-matched controls were included in this study. The participants underwent diffusion magnetic resonance imaging (MRI) and T1-weighted structural MRI at 7 Tesla. Diffusion MRI connectometry was performed to investigate the differences between groups, and to subsequently study the correlation between Coma Recovery Scale-Revised (CRS-R) scores and white matter integrity. In DOC patients, the quantitative anisotropy (QA) was significantly reduced in deep white matter tracts, whereas significantly higher QA values were found in the bilateral cerebellum compared with healthy controls. Moreover, the QA values in many tracts within the right hemisphere were higher in patients in a minimally conscious state compared to those in vegetative state/unresponsive wakefulness syndrome. In contrast, many tracts within the left hemisphere of the latter group showed higher QA than the former, which was reflected by the correlation between diffusion connectometry and CRS-R scores. These results indicate that the cerebellum may play an important role in DOC, and the lateralization of the cerebral hemisphere in affected patients may suggest neural compensation. © 2021, CC BY.

关键词： Anisotropy

来源：评论

学校读者我要写书评

暂无评论

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

arXiv

引用

arXiv 2019年

作者： Simpson, Amber L. Antonelli, Michela Bakas, Spyridon Bilello, Michel Farahani, Keyvan Van Ginneken, Bram Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc Golia-Pernicka, Jennifer Heckers, Stephan H. Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Vorontsov, Eugene Maier-Hein, Lena Jorge Cardoso, M. Department of Surgery Memorial Sloan Kettering Cancer Center Centre for Medical Image Computing University College London Center for Biomedical Image Computing and Analytics University of Pennsylvania Division of Cancer Treatment and Diagnosis National Cancer Institute Department of Pathology Radboud University Medical Center Division of Biostatistics German Cancer Research Center Department of Electrical Engineering and Computer Science Vanderbilt University Department of Informatics Technische Universität München Google DeepMind Imaging Biomarkers and Computer-aided Diagnosis Lab Radiology and Imaging Sciences National Institutes of Health Clinical Center Department of Radiology Memorial Sloan Kettering Cancer Center Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center Department of Radiology Stanford University Department of Computer Science and Software Engineering École Polytechnique de Montréal Division of Computer Assisted Medical Interventions German Cancer Research Center Department of Imaging and Biomedical Engineering King's College London

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical image computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Medial temporal lobe gray matter microstructure alterations provide improved sensitivity to detect neurodegeneration associated with Alzheimer’s disease biomarkers

引用

Alzheimer's & Dementia 2023年第S16期19卷

作者： Christopher Brown Sandhitsu R. Das Long Xie Ilya M. Nasrallah John A. Detre Corey T McMillan Paul A. Yushkevich David A. Wolk University of Pennsylvania Philadelphia PA USA Correspondece Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Department of Radiology University of Pennsylvania Philadelphia PA USA Penn FTD Center University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center Perelman School of Medicine Philadelphia PA USA Institute on Aging University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA

Background Despite the strengths conventional structural imaging measures to detect atrophy, cross-sectional relationships between neurodegeneration and Alzheimer’s disease (AD) biomarkers in early stages is limited. Neurite orientation dispersion and density imaging (NODDI) measures microstructural alterations that are unique from conventional structural metrics. Alterations in microstructure have been detected in individuals with AD compared to cognitively normal individuals, but there is sparse data regarding the relationship between these metrics and AD biomarkers. Here we sought to characterize how NODDI metrics in the medial temporal lobe (MTL) are associated with plasma AD biomarkers. Method One-hundred and sixty-seven cognitively normal (CN) individuals, 47 individuals with mild cognitive impairment, and 16 individuals with AD underwent diffusion and T1w imaging. T1w images were segmented using the ASHS-T1 protocol to form anterior and posterior MTL ROIs. Multi-shell DTI data were collected in 113 non-collinear directions with 1.5mm isotropic voxels, were pre-processed using a custom pipeline, and fit with voxel-wise NODDI models. ANTs was used to linearly register NODDI and T1w images to allow transformation of MTL ROIs to diffusion space. Stepwise-regression models examined relationships between anterior and posterior MTL NODDI metrics with plasma biomarkers in CN and impaired (MCI/AD) groups after accounting for MTL volume. Result NODDI metrics of intracellular volume fraction (icVf) and orientation dispersion index (ODI) were decreased, and isometric volume fraction (isoVf) was increased in cognitively impaired compared to CN. Amyloid positivity was associated with decreased ODI and increased isoVf in cognitively impaired individuals. While posterior MTL volume predicted plasma p-Tau-181, inclusion of NODDI metrics subsumed this effect (F-change>7.54, p <0.0067), and reduced ODI was the best predictor of plasma pTau-181 within cognitively impaired indivi

关键词：

来源：评论

学校读者我要写书评

暂无评论

Novel genomic loci shape two neuroanatomical dimensions of AD in non-demented populations

引用

Alzheimer's & Dementia 2023年第S10期19卷

作者： Junhao Wen Zhijian Yang Ioanna Skampardoni Marilena De Pian Guray Erus Elizabeth Mamourian Yuhan Cui Bingxin Zhao Ilya M. Nasrallah Arthur W. Toga Haochang Shou Li Shen Christos Davatzikos Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA University of Pennsylvania philadelphia PA USA Upenn Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Laboratory of Neuro Imaging (LONI) University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA Greece

Background Artificial intelligence has been in burgeoning demand to be applied to magnetic imaging resonance (MRI) data to parse the neuroanatomical heterogeneity of Alzheimer’s disease (AD) and/or mild cognitive impairment (MCI) (Wen et al., 2021a, 2021b; Yang et al., 2021). However, the genetic heterogeneity leading to this neuroanatomical heterogeneity, especially at the early asymptomatic stages, is not fully elucidated. Method A deep semi-supervised representation learning method, called Surreal-GAN (Yang et al., 2022), was applied to T1-weighted MRI from AD/MCI patients from ADNI to define the two neuroanatomical dimensions of AD at the symptomatic stages. The R1 dimension was characterized by widespread brain atrophy without an exclusive focus on the medial temporal lobe, whereas the R2 dimension showed focal medial temporal lobe atrophy (Wen et al., 2022). We then applied the trained model to 33,540 non-demented individuals from UK Biobank. Imputed genotyping data were analyzed in genome-wide association studies (GWAS). We performed multiple linear regressions controlling for confounding factors (e.g., age) via Plink (Purcell et al., 2007). We defined a genomic locus (represented by the top lead SNP) considering linkage disequilibrium. Finally, we manually queried all leading SNPs on GWAS Catalog (Buniello et al., 2019) to determine if the genomic loci were novel, provided they were not previously associated with any clinical traits. Result GWAS discovered 24 genomic loci, of which 7 were novel for R1 and R2, respectively (Fig. 1). For R1, 8 mapped genes, based on physical positions, eQTL, and chromatin interactions, were associated with AD-related traits in the literature. For example, the KDM2B gene from the genomic locus (rs9795600, P-value = 1.13e-08) was previously associated with AD (Gouveia et al., 2022). For R2, 13 mapped genes were associated with AD-related traits. For example, the TESC gene from the genomic locus (rs77956314, P-value = 9.99e-29) w

关键词：

来源：评论

学校读者我要写书评

暂无评论

Genetic variants underlying the neuro-anatomical signatures of hypertension measured on structural MRI

引用

Alzheimer's & Dementia 2023年第S10期19卷

作者： Sindhuja Tirumalai Govindarajan Elizabeth Mamourian Yuhan Cui Junhao Wen Guray Erus Ahmed Abdulkadir Randa Melhem R Nick Bryan Haochang Shou David A. Wolk Ilya M. Nasrallah Christos Davatzikos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Pennsylvania Health System Philadeplphia PA USA Department of Neurology University of Pennsylvania School of Medicine Philadelphia PA USA

Background Hypertension (HTN) is associated with gray matter (GM) atrophy and increased white matter hyperintensity (WMH) burden, increasing their susceptibility to Alzheimer’s disease and related dementias. We developed a machine learning based model to quantify spatial patterns of abnormality recognizing HTN-related brain changes (SPARE-HTN) from structural magnetic resonance images (sMRI). We performed a genome wide association study (GWAS) to identify the genetic variants and associated biological traits that underlie the “expression” of HTN-related brain changes, i.e., the individualized SPARE-HTN index. Method SPARE-HTN model was trained on the large multi-study iSTAGING dataset, with regional GM volumes from T1-weighted images and lobar WMH volumes from T2-weighted FLAIR images used as imaging features. GWAS was carried out in a subset of N = 7490 (46% Female, Age = 65.5±7 years) hypertensive participants from UK-Biobank. A GWAS of SPARE-HTN scores was run using linear regression with Plink, correcting for the top 40 genetic principal components, age, sex, and intracranial volume. Significant SNPs were identified and mapped to functional genes using the Functional Mapping and Annotation (FUMA) platform; and candidate SNPs were mapped to the GWAS catalog. Result SPARE-HTN was associated with regional atrophy most prominent in perisylvian and temporal cortex and increased WMH volumes, particularly in frontal white matter. SPARE-HTN scores showed better discriminative power between normal and stage 1 participants (Cohen’s-d effect size, d = 0.2) and stage 2 (d = 0.71) hypertensive participants when compared to total WMH volume (d = 0.04 and 0.38, respectively). GWAS identified N = 321 independently significant SNPs, of which a subset N = 46 candidate SNPs were mapped to 6 genes (ICA1L, CARF, WDR12, NBEAL1, KRT8P15, CYP20A1) in Chromosome 2 (Figure 2). These genes were previously reported to be associated with several cardiovascular conditions including coronary

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：