检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

489 篇 期刊文献
166 篇 会议

馆藏范围

655 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

265 篇 医学
- 216 篇 临床医学
- 98 篇 基础医学(可授医学...
- 42 篇 公共卫生与预防医...
- 29 篇 药学(可授医学、理...
- 15 篇 特种医学
- 6 篇 中西医结合
- 6 篇 医学技术（可授医学...
181 篇 理学
- 108 篇 生物学
- 27 篇 数学
- 21 篇 物理学
- 15 篇 统计学（可授理学、...
- 12 篇 化学
169 篇 工学
- 68 篇 生物医学工程（可授...
- 63 篇 计算机科学与技术...
- 62 篇 软件工程
- 45 篇 光学工程
- 39 篇 生物工程
- 12 篇 信息与通信工程
- 11 篇 电气工程
- 10 篇 电子科学与技术（可...
- 9 篇 化学工程与技术
- 8 篇 控制科学与工程
- 6 篇 仪器科学与技术
- 5 篇 建筑学
- 5 篇 土木工程
30 篇 管理学
- 13 篇 公共管理
- 10 篇 图书情报与档案管...
- 7 篇 管理科学与工程(可...
26 篇 农学
- 5 篇 作物学
17 篇 教育学
- 14 篇 心理学(可授教育学...
3 篇 法学
3 篇 文学

主题

48 篇 functional magne...
32 篇 neuroimaging
30 篇 brain modeling
21 篇 magnetic resonan...
20 篇 biomedical imagi...
19 篇 deep learning
19 篇 independent comp...
17 篇 correlation
16 篇 feature extracti...
15 篇 schizophrenia
14 篇 engineering in m...
13 篇 predictive model...
13 篇 visualization
13 篇 data models
12 篇 biomarkers
12 篇 electroencephalo...
11 篇 depression
11 篇 signal processin...
11 篇 biological syste...
11 篇 accuracy

机构

22 篇 tri-institutiona...
19 篇 tri-institutiona...
11 篇 department of me...
10 篇 department of in...
10 篇 georgia state un...
9 篇 tri-institutiona...
8 篇 department of tw...
8 篇 university of gl...
8 篇 department of co...
7 篇 national institu...
7 篇 stanley center f...
7 篇 university of pi...
7 篇 institute for ca...
7 篇 imim barcelona
7 篇 wellcome centre ...
7 篇 center for genom...
7 篇 emory university...
7 篇 tri-institutiona...
7 篇 tri-institutiona...
7 篇 harvard medical ...

作者

89 篇 vince d. calhoun
31 篇 armin iraji
22 篇 vince d calhoun
22 篇 calhoun vince d.
21 篇 vince calhoun
17 篇 zening fu
16 篇 robyn l. miller
14 篇 jingyu liu
12 篇 anees abrol
10 篇 tülay adali
10 篇 charles a. ellis
9 篇 kari stefansson
9 篇 jing sui
8 篇 bakas spyridon
8 篇 caroline hayward
8 篇 shile qi
8 篇 rongtao jiang
7 篇 timothy m frayli...
7 篇 mark i mccarthy
7 篇 henrik zetterber...

语言

637 篇 英文
12 篇 其他
6 篇 中文

检索条件"机构=Center for Translational Research in Neuroimaging and Data Science"

共 655 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Correlation of Correlation Networks: High-Order Interactions in the Topology of Brain Networks

Correlation of Correlation Networks: High-Order Interactions...

引用

IEEE International Symposium on Biomedical Imaging

作者： Qiang Li Jingyu Liu Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech and Emory University Atlanta GA United States

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

To understand collective network behavior in the complex human brain, pairwise correlation networks alone are insufficient for capturing the high-order interactions that extend beyond pairwise interactions and play a crucial role in brain network dynamics. These interactions often reveal intricate relationships among multiple brain networks, significantly influencing cognitive processes. In this study, we explored the correlation of correlation networks and topological network analysis with resting-state fMRI to gain deeper insights into these higher-order interactions and their impact on the topology of brain networks, ultimately enhancing our under-standing of brain function. We observed that the correlation of correlation networks highlighted network connections while preserving the topological structure of correlation networks. Our findings suggested that the correlation of correlation networks surpassed traditional correlation networks, showcasing considerable potential for applications in various areas of network science. Moreover, after applying topological network analysis to the correlation of correlation networks, we observed that some high-order interaction hubs predominantly occurred in primary and high-level cognitive areas, such as the visual and fronto-parietal regions. These high-order hubs played a crucial role in information integration within the human brain.

关键词： Visualization Correlation Pairwise error probability Network topology Cognitive processes Network analyzers Functional magnetic resonance imaging Topology Behavioral sciences Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Dynamic Fusion: Merging Structural and Functional Connectivity Dynamics Via Joint CMICA

Dynamic Fusion: Merging Structural and Functional Connectivi...

引用

IEEE International Symposium on Biomedical Imaging

作者： Lei Wu Marlena Duda Armin Iraji Vince D. Calhoun Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

This study introduces ‘dynamic fusion’, a novel framework that bridges structural and functional connectivity in the context of dynamic reconfiguration, to illustrate how brain networks adapt over time. By combining insights from functional MRI data on functional networks with diffusion MRI measurements of anatomical connections, dynamic fusion employs a joint analysis method, ‘joint cmICA’, to analyze their relationship over time. Our approach gauges the interplay between these joint components across various temporal states, aiming to discover both static and transient features of brain connectivity. Applied to empirical MRI data, our approach reveals how intricately structural connectivity patterns adapt to support functional dynamics and demands. Overall, dynamic fusion is an innovative tool for integrating connectivity data, advancing our understanding into fundamental principles of brain organization and operation.

关键词： Bridges Magnetic resonance imaging Merging data integration Organizations Biomedical measurement Time measurement Transient analysis Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Dynamic Convergence of Multiple Overlapping Brain States as a Biomarker for Mental Disorders

Dynamic Convergence of Multiple Overlapping Brain States as ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Najme Soleimani Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Exploring the biomarkers linked to various psychiatric disorders is essential for improving diagnostic accuracy and monitoring disease progression. Recent developments in neuroimaging techniques have shifted the emphasis from conventional univariate approaches, which evaluate isolated brain areas, to sophisticated multivariate models that examine intricate interactions across multiple brain networks. Dynamic functional network connectivity (dFNC), the coupling among independent component fluctuations, has become an important method in this landscape, providing a sophisticated understanding of how connectivity fluctuates over time between brain regions. In this research, we aim to uncover unique connectivity profiles and their transitions that are characteristic of disorders such as schizophrenia (SCZ), autism spectrum disorder(ASD), bipolar disorder (BPD) and major depressive disorder (MDD) by leveraging FNC-based biomarkers. The results reveal distinct patterns of convergence and divergence in functional connectivity across clinical groups, highlighting increased connectivity variability in ASD and SCZ, stable but rigid patterns in MDD, and moderate divergence in BPD, which together provide insights into disorder-specific neurobiological signatures.

关键词： neuroimaging Couplings Fluctuations Mental disorders Independent component analysis Biomarkers Schizophrenia Depression Monitoring Convergence

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification

Multi-Modal Imaging Genomics Transformer: Attentive Integrat...

引用

IEEE International Symposium on Biomedical Imaging

作者： Nagur Shareef Shaik Teja Krishna Cherukuri Vince Calhoun Dong Hye Ye Department of Computer Science Georgia State University Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech Emory

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors. Its complex symptoms and overlap with other psychiatric conditions complicate diagnosis, necessitating advanced systems. Existing research studies have mostly focused on imaging data, such as structural and functional MRI, for SZ diagnosis. There has been less focus on the integration of genomic features despite their potential in identifying heritable SZ traits. This study introduces a Multi-modal Imaging Genomics Transformer (MIGTrans) that integrates genomics with structural and functional imaging to identify SZ-related abnormalities. MIGTrans improved SZ classification accuracy to 86.05%, offering clear interpretations and identifying key genomic and brain morphological/connectivity patterns linked to SZ.

关键词： Brain Accuracy Magnetic resonance imaging Decision making Genomics Imaging Schizophrenia Transformers Bioinformatics Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

[18F]Florbetapir Pet Uptake Differences in White Matter Across the Alzheimer's Disease Continuum: From Cognitively Normal to MCI and Dementia

[18F]Florbetapir Pet Uptake Differences in White Matter Acro...

引用

IEEE International Symposium on Biomedical Imaging

作者： Nigar Khasayeva Kyle M. Jensen Cyrus Eierud Helen Petropoulos Vince D. Calhoun Armin Iraji Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Alzheimer's disease (AD) is widely recognized for hallmark gray matter (GM) changes, including extracellular amyloid plaques and neurofibrillary tangles. However, recent neuroimaging research suggests that white matter (WM) aberrations may also play a significant role in AD risk and development. This study investigates AD dementia by examining regional covariance patterns of WM changes, expanding beyond the conventional focus on GM. Using [18F]Florbetapir ([18F]FBP) Positron Emission Tomography (PET) data from 716 participants in the Alzheimer's Disease neuroimaging Initiative (ADNI), we analyzed inter-subject covarying patterns to reveal latent WM sources associated with AD dementia. Employing a model order of 100 and independent component analysis (ICA), we identified 13 highly reliable and distinct WM components, along with 80 GM components, enabling a comparative analysis of group effects across the AD continuum and highlighting the differential significance of WM integrity. We assessed the discrimination ability of WM components between cognitively normal (CN), mild cognitive impairment (MCI), and AD dementia diagnostic groups. Statistical analysis on 13 WM component weights highlighted significant diagnostic contrasts, with 11 components differentiating AD dementia from CN, 10 distinguishing AD dementia from MCI, and 8 distinguishing MCI from CN. Notably, results were replicable, reinforcing the robustness of latent WM sources. Our findings underscore the importance of WM integrity in AD pathology, suggesting WM degeneration as a potentially integral feature of the AD continuum. This work demonstrates the potential of combining ICA with [18F]FBP PET to illuminate nuanced neurobiological mechanisms underlying AD-related WM degeneration, offering valuable insights that may inform future diagnostic approaches across the AD continuum.

关键词： neuroimaging Pathology Statistical analysis Grey matter Independent component analysis Robustness Positron emission tomography Alzheimer's disease White matter Extracellular

来源：评论

学校读者我要写书评

暂无评论

Sex Differences in Coupled Dynamic Functional Connectivity and Structural Brain Morphology: Insights from the ABCD Study

Sex Differences in Coupled Dynamic Functional Connectivity a...

引用

IEEE International Symposium on Biomedical Imaging

作者： Aline Kotoski Sir-Lord Wiafe Vince Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta USA Neuroscience Institute Georgia State University Atlanta USA Department of Computer Science Georgia State University Atlanta USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

This study investigates sex-based differences in brain structure-function coupling using a novel dynamic intermodality source coupling (dIMSC) method, which we use to integrate dynamic functional network connectivity (dFNC) and structural brain morphometry (SBM) data from the ABCD study. This study aims to examine the coupling between dFNC and sMRI gray matter volume, comparing the coupling in relation to sex differences across brain regions. By examining the coupling between dFNC and SBM matrices, we identified distinct functional-structural coupling patterns across sexes in various brain regions. Females showed stronger correlations in areas previously associated with social and language processing, such as the inferior frontal gyrus and superior parietal lobule, while males exhibited stronger correlations in regions that have previously been linked to spatial processing, including the superior temporal gyrus, lingual gyrus, and cerebellum. These findings underscore nuanced sex-specific variations in brain structure-function relationships, offering insights into distinct patterns of the brain.

关键词： Couplings Cerebellum Correlation Limiting Head Magnetic resonance imaging Morphology Grey matter Magnetic heads Hippocampus

来源：评论

学校读者我要写书评

暂无评论

SELF-Clustering Graph Transformer Approach to Model Resting State Functional Brain Activity

SELF-Clustering Graph Transformer Approach to Model Resting ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Bishal Thapaliya Esra Akbas Ram Sapkota Bhaskar Ray Vince Calhoun Jingyu Liu Department of Computer Science Georgia State University Atlanta USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes unlike uniform node updates like vanilla graph transformers, further allowing us to learn and interpret the sub clusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method, and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures.

关键词： Biometrics Correlation Cognitive processes Biological system modeling Organizations Predictive models Functional magnetic resonance imaging Transformers Brain modeling Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Adaptive Constrained ICA with Mixing Matrix Column Constraints: Application to fMRI data

Adaptive Constrained ICA with Mixing Matrix Column Constrain...

引用

Annual Conference on Information sciences and Systems (CISS)

作者： Chunying Jia Weixin Wang Trung Vu Hanlu Yang Ben Gabrielson Vince D. Calhoun Tülay Adali University of Maryland Baltimore County Baltimore MD Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Independent Component Analysis (ICA) is a powerful data-driven method that has been widely applied in functional magnetic resonance imaging (fMRI) data analysis to uncover underlying sources. An attractive way to boost ICA performance is via constraints to guide ICA factors to be similar to user-supplied "references", allowing incorporation of prior-knowledge into the factorization. However, most of existing constrained ICA methods typically only impose source constraints and are unable to impose constraints on the mixing matrix. With multi-subject medical imaging datasets, constraining the mixing matrix with subjects’ symptom-related measurements, such as clinical scores or cognitive variables, enhances the algorithm’s ability to identify brain activities associated with these symptoms. This offers a novel perspective for understanding the pathologies underlying various psychiatric disorders. Therefore, to overcome the limitations of existing constrained ICA algorithms, we introduce a new constrained ICA algorithm: adaptive-reverse constrained matrix entropy bound minimization (arc-M-EBM), which imposes constraints on the mixing matrix and uses adaptive-reverse thresholding to avoid overfitting or underfitting. This approach ensures flexibility and leads to more accurate and interpretable source separation. Simulations demonstrate that arc-M-EBM outperforms traditional ICA methods. Application to resting-state fMRI data from 176 subjects from healthy controls and patients reveals significant relationships between constrained components and clinical measures, enhancing our understanding of brain-behavior relationships.

关键词： Adaptation models Thresholding (Imaging) Source separation Correlation Loading Independent component analysis Functional magnetic resonance imaging Brain modeling data models Context modeling

来源：评论

学校读者我要写书评

暂无评论

Investigating the Impact of Habitual Sleep Quality on Episodic Memory Performance: An Eeg-Based Representational Similarity Analysis

Investigating the Impact of Habitual Sleep Quality on Episod...

引用

IEEE International Symposium on Biomedical Imaging

作者： Masoud Seraji Soroush Mirjalili Audrey Duarte Vince D Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University/Georgia Institute of Technology/Emory University Atlanta GA USA School of Psychology University of Texas at Austin Austin USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Sleep is crucial for episodic memory consolidation, yet the impact of habitual sleep quality on memory performance remains underexplored. This study investigates the relationship between sleep quality and episodic memory retrieval using EEG-based representational similarity analysis (RSA). Thirty-six participants wore wrist accelerometers for one week to capture habitual sleep patterns, including total sleep time and restlessness. Memory performance was assessed through a paired associate learning task, with EEG data recorded during encoding and retrieval phases. RSA was applied to EEG oscillatory power across time-frequency windows to examine the neural similarity between encoding and retrieval. The results showed both positive and negative correlations between sleep metric and memory performance, with sleep restlessness being linked to both increases and decreases in neural similarity across specific clusters. These findings emphasize the important role of sleep quality in shaping the neural processes underlying episodic memory retrieval, indicating a strong connection between sleep patterns and memory function.

关键词： Wrist Accelerometers Time-frequency analysis Correlation Sleep Electroencephalography Encoding Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Genetics Encoded Joint Embedding of Multimodal Connectomes with Explainable Graph Neural Network for Schizophrenia Classification

Genetics Encoded Joint Embedding of Multimodal Connectomes w...

引用

IEEE International Symposium on Biomedical Imaging

作者： Badhan Mazumder Lei Wu Vince D. Calhoun Dong Hye Ye Department of Computer Science Georgia State University Atlanta GA USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Investigating structural and functional brain connectivity is crucial for understanding neuropsychiatric disorders like schizophrenia (SZ), where genetic markers such as SNPs (single nucleotide polymorphisms) also play a significant role. Graph Neural Networks (GNNs) offer powerful tools for learning complex patterns from connectome data, yet their use across multimodal connectomes remains under-explored. In this study, we developed an explainable multiview GNN framework for SZ classification, integrating genetic markers with connectome features by leveraging message-passing scheme of GNN with Deep Canonical Correlation Analysis (DCCA) for multimodal fusion. Experiments on clinical dataset confirmed the robustness of the framework, highlighting potential clinical biomarkers.

关键词： Correlation Transfer learning Genomics Schizophrenia Biomarkers Graph neural networks Robustness Bioinformatics Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共66页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：