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

153 篇 会议
90 篇 期刊文献

馆藏范围

243 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

61 篇 医学
- 50 篇 临床医学
- 29 篇 基础医学(可授医学...
- 11 篇 药学(可授医学、理...
- 10 篇 特种医学
- 7 篇 公共卫生与预防医...
- 4 篇 医学技术（可授医学...
47 篇 理学
- 35 篇 生物学
- 12 篇 数学
- 10 篇 物理学
- 5 篇 统计学（可授理学、...
- 4 篇 化学
- 1 篇 系统科学
46 篇 工学
- 30 篇 生物医学工程（可授...
- 25 篇 光学工程
- 22 篇 计算机科学与技术...
- 22 篇 软件工程
- 15 篇 生物工程
- 7 篇 电气工程
- 7 篇 信息与通信工程
- 6 篇 电子科学与技术（可...
- 3 篇 机械工程
- 3 篇 控制科学与工程
- 2 篇 交通运输工程
14 篇 管理学
- 5 篇 管理科学与工程(可...
- 5 篇 公共管理
- 4 篇 图书情报与档案管...
- 3 篇 工商管理
4 篇 教育学
- 4 篇 心理学(可授教育学...
4 篇 农学
- 3 篇 作物学
1 篇 经济学
- 1 篇 应用经济学

主题

47 篇 functional magne...
30 篇 brain modeling
27 篇 neuroimaging
20 篇 biomedical imagi...
19 篇 independent comp...
18 篇 magnetic resonan...
16 篇 feature extracti...
16 篇 correlation
15 篇 deep learning
15 篇 schizophrenia
13 篇 engineering in m...
13 篇 predictive model...
13 篇 visualization
13 篇 data models
12 篇 electroencephalo...
11 篇 biomarkers
11 篇 signal processin...
10 篇 couplings
10 篇 biological syste...
10 篇 accuracy

机构

25 篇 tri-institutiona...
21 篇 tri-institutiona...
12 篇 georgia state un...
10 篇 tri-institutiona...
9 篇 department of co...
9 篇 department of co...
7 篇 tri-institutiona...
7 篇 tri-institutiona...
6 篇 georgia state un...
6 篇 tri-institutiona...
6 篇 emory university...
6 篇 school of comput...
6 篇 tri-institutiona...
6 篇 school of electr...
5 篇 massachusetts ge...
5 篇 department of ps...
5 篇 school of electr...
5 篇 tri-institutiona...
5 篇 school of inform...
5 篇 wallace h. coult...

作者

96 篇 vince d. calhoun
30 篇 armin iraji
26 篇 calhoun vince d.
22 篇 vince d calhoun
21 篇 vince calhoun
19 篇 zening fu
18 篇 robyn l. miller
14 篇 jingyu liu
13 篇 charles a. ellis
11 篇 anees abrol
10 篇 tülay adali
8 篇 jing sui
8 篇 shile qi
8 篇 rongtao jiang
8 篇 bishal thapaliya
8 篇 mohammad s.e. se...
7 篇 elaheh zendehrou...
7 篇 ashkan faghiri
7 篇 tulay adali
7 篇 ellis charles a.

语言

240 篇 英文
3 篇 其他

检索条件"机构=Center for Translational Research in Neuroimaging and Data Science: Georgia State University"

共 243 条记录，以下是121-130 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：

REGRESSION-ASSISTED INDEPENDENT VECTOR ANALYSIS: A SOLUTION TO LARGE-SCALE FMRI data ANALYSIS

REGRESSION-ASSISTED INDEPENDENT VECTOR ANALYSIS: A SOLUTION ...

引用

Asilomar Conference on Signals, Systems & Computers

作者： H. Yang B. Gabrielson V. D. Calhoun T. Adali Dept. of CSEE University of Maryland Baltimore County Baltimore USA Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta USA

Multi-subject fMRI data is instrumental in understanding the brain function and studying different brain disorders. It is desirable to analyze fMRI datasets jointly to leverage the cross information that exists across multiple datasets. Independent Vector Analysis (IVA) is a powerful solution that can effectively leverage statistical dependence across multiple datasets, and is an attractive solution for fMRI data analysis. However, the computational costs of IVA can be intractable when dealing with a large number of datasets. In this paper, we propose an efficient method for large-scale fMRI analysis with the assistance of multilinear regression. As we demonstrate with results from resting state fMRI data, the proposed method achieves similar estimation performance to IVA on all available datasets, but with significantly improved efficiency and reliable performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary Learning

New Interpretable Patterns and Discriminative Features from ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： F. Ghayem H. Yang F. Kantar S.-J. Kim V. D. Calhoun T. Adali Dept. of CSEE University of Maryland Baltimore County Baltimore USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta USA

Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data has proven useful in providing a fully multivariate summary that can be used for multiple purposes. ICA can identify patterns that can discriminate between healthy controls (HC) and patients with various mental disorders such as schizophrenia (Sz). Temporal functional network connectivity (tFNC) obtained from ICA can effectively explain the interactions between brain networks. On the other hand, dictionary learning (DL) enables the discovery of hidden information in data using learnable basis signals through the use of sparsity. In this paper, we present a new method that leverages ICA and DL for the identification of directly interpretable patterns to discriminate between the HC and Sz groups. We use multi-subject resting-state fMRI data from 358 subjects and form subject-specific tFNC feature vectors from ICA results. Then, we learn sparse representations of the tFNCs and introduce a new set of sparse features as well as new interpretable patterns from the learned atoms. Our experimental results show that the new representation not only leads to effective classification between HC and Sz groups using sparse features, but can also identify new interpretable patterns from the learned atoms that can help understand the complexities of mental diseases such as schizophrenia.

关键词： Mental disorders Machine learning Independent component analysis Functional magnetic resonance imaging Signal processing Acoustics Complexity theory

来源：评论

学校读者我要写书评

暂无评论

A New Hypergraph Clustering Method For Exploring Transdiagnostic Biotypes In Mental Illnesses: Application To Schizophrenia And Psychotic Bipolar Disorder

A New Hypergraph Clustering Method For Exploring Transdiagno...

引用

IEEE International Symposium on Biomedical Imaging

作者： Yuhui Du Ju Niu Vince D Calhoun School of Computer and Information Technology Shanxi University Taiyuan China 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: (纸本)9781665412469;9781665429474

It is difficult to distinguish schizophrenia (SZ) and bipolar disorder with psychosis (BPP) due to their overlapping symptoms. Indeed, there has been evidence supporting different subtypes within them. data-driven clustering approaches are commonly used to explore biologically meaningful biotypes using neuroimaging features. However, previous studies typically consider pair-wise subject relationships. Here, we propose a hypergraph clustering method to explore biotypes. Our method extracts high-order features via hyperedges sampling, measures similarity and then regroups subjects using community detection. We applied it to identify biotypes of 100 BPP and 100 SZ patients using brain functional connectivity estimated from resting-state fMRI data, and compared with solutions from K-means and normalized cut (Ncut). Two reliable biotypes were identified and had greater differences in functional connectivity than groups determined by clinical diagnosis. Our method also outperformed K-means and Ncut for the clustering ability and computation efficiency. In summary, the proposed method is promising for developing biotypes, targeting accurate clinical diagnosis for psychosis.

关键词： neuroimaging Clustering methods Functional magnetic resonance imaging Biomedical measurement Feature extraction Biology Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI data

Constrained Independent Component Analysis Based on Entropy ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： H. Yang F. Ghayem B. Gabrielson M. A. B. S. Akhonda V. D. Calhoun T. Adali Dept. of CSEE University of Maryland Baltimore County Baltimore USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta USA

Identification of subgroups of subjects homogeneous functional networks is a key step for precision medicine. Independent vector analysis (IVA) is shown to be effective for this task, however, it has a substantial computing cost. We propose a constrained independent component analysis algorithm based on minimizing the entropy bound (c-EBM) to overcome the computational complexity limitation of IVA. A set of spatial maps used as constraints provides a connection across the datasets, provides alignment across subject-wise ICA analyses and serves as a foundation for subgroup identification. The approach makes use of the available prior knowledge while allowing flexible density modeling without an orthogonality requirement for the demixing matrix. Synthetic data and large scale multi-subject resting state fMRI data have both been used to evaluate the performance of the new algorithm, c-EBM. The findings demonstrate that c-EBM is adaptable in terms of various settings for the constraint parameter on the synthetic data. With multi-subject resting state fMRI data, c-EBM can effectively identify subgroups and discover meaningful brain networks that show significant group differences between subgroups.

关键词： Precision medicine Signal processing algorithms Independent component analysis Functional magnetic resonance imaging Signal processing Minimization Entropy

来源：评论

学校读者我要写书评

暂无评论

How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA Method

How Does Aging Affect Whole-brain Functional Network Connect...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yuhui Du Yating Guo Vince D Calhoun School of Computer and Information Technology Shanxi University Taiyuan China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

Many studies have shown that changes in the functional connectivity are diverse along with aging. However, few studies have addressed how aging affects connectivity among large-scale brain networks, and it is challenging to examine gradual aging trajectories from middle adulthood to old age. In this work, based on large-sample fMRI data from 6300 subjects aged between 49 to 73 years, we apply an independent component analysis-based method called NeuroMark to extract brain functional networks and their connectivity (i.e., functional network connectivity (FNC)), and then propose a two-level statistical analysis method to explore robust aging-related changes in functional network connectivity. We found that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network and cerebellum. Our results emphasize the diversity of brain aging and provide new evidence for non-pathological aging of the whole *** Relevance—This provides new evidence for non-pathological aging of functional network connectivity in the whole brain.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A New Semi-Supervised Non-Negative Matrix Factorization Method For Brain Dynamic Functional Connectivity Analysis

A New Semi-Supervised Non-Negative Matrix Factorization Meth...

引用

IEEE International Symposium on Biomedical Imaging

作者： Yuhui Du Xingyu He Vince D Calhoun School of Computer and Information Technology Shanxi University Taiyuan China 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: (纸本)9781665412469;9781665429474

To overcome the shortcoming of static brain functional connectivity analysis, recent studies have analyzed brain functional connectivity from a time-varying view to identify subtle group differences as potential biomarkers. However, how to obtain reliable functional connectivity states from dynamic functional connectivity (DFC) is challenging, since the resulting states are often sensitive to the cluster or component number. In our present work, we propose a new semi-supervised non-negative matrix factorization method combining with a community detection technique, which can automatically estimate reliable functional connectivity states from DFC without a need of setting a cluster or component number. We applied our method to fMRI data of 36 schizophrenia patients and 49 healthy controls to investigate brain connectivity changes in schizophrenia. We found significant and meaningful group differences in four of five connectivity states. Our findings suggest that our method can help understand the mechanism of mental illness in a more effective way.

关键词： Functional magnetic resonance imaging Biomarkers Reliability Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders

An Adaptive Semi-Supervised Deep Clustering and Its Applicat...

引用

IEEE International Symposium on Biomedical Imaging

作者： Yuhui Du Fulin Wu Ju Niu Vince D Calhoun School of Computer and Information Technology Shanxi University Taiyuan China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

Symptoms overlap among different types of psychiatric disorder, which makes diagnosis challenging. There has been evidence that exploring new biotypes may help solve the problem. Using neuroimaging data, clustering technique has been applied to identify biotypes, however deep learning has not been well studied for this goal. Here, we propose a new semi-supervised deep clustering method, which takes full advantage of slight amount of labeled samples to adaptively extract useful information from unlabeled samples with high confidence level to facilitate the semi-supervised clustering. We validate our method using the Fashion MNIST data and then apply it to 137 patients with autism spectrum disorder (ASD) and 137 patients with schizophrenia (SZ) to explore psychiatric biotypes by using functional magnetic resonance imaging (fMRI) data. Compared with traditional methods, our method obtains better clustering performance for the Fashion MNIST data and identifies interesting transdiagnostic biotypes in ASD and SZ. In summary, our proposed semi-supervised deep clustering method is promising in extracting biotypes of psychiatric disorders.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal neuroimaging data

A Deep Learning Approach for Psychosis Spectrum Label Noise ...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Hooman Rokham Haleh Falakshahi 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 USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Understanding the structural and functional mechanisms of the brain is challenging for mood and mental disorders. Many neuroimaging techniques are widely used to reveal hidden patterns from different brain imaging modalities. However, these findings are bounded by the limitation of each modality. In addition, the lack of validity of current psychosis nosology created more complications in understanding biomarkers. In this study, we introduced a deep convolutional framework to classify and identify label noises using structural and functional magnetic resonance imaging data. We applied our method to functional and structural MRI data from a schizophrenia dataset and evaluated the model’s performance in a cross-validated form. In addition, we introduced a noise criterion to distinguish a potentially noisy subject for each modality. Our results show the learned model using resting-state functional MRI data is more informative and has higher performance in comparison with structural MRI data. Lastly, based on the noise level, we investigated potential borderline subjects as possible subtypes and made a statistical analysis to distinguish differences between resting-state static functional connectivity *** Relevance— Results show schizophrenia patients are separable from the healthy control group based on their neuroimaging data and resting-state functional MRI data is more informative than structural MRI data and hence contains less label noise.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evaluating Trade-Offs in IVA of Multimodal neuroimaging using Cross-Platform Multidataset Independent Subspace Analysis

Evaluating Trade-Offs in IVA of Multimodal Neuroimaging usin...

引用

IEEE International Symposium on Biomedical Imaging

作者： Xinhui Li Daniel Khosravinezhad Vince D. Calhoun Rogers F. Silva 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 Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Multidataset independent subspace analysis (MISA) unifies multiple linear blind source separation methods to analyze joint and unique information across multiple datasets. MISA can jointly analyze large multimodal neuroimaging datasets to advance our understanding of the brain from multiple perspectives. However, a systematic evaluation of the trade-offs between problem scale and sample size is still absent in the literature. Aiming to support flexibility and replicability of deep latent variable modeling, and equip practitioners with crucial tools and usage guidelines, we developed a MISA PyTorch module incorporating the linked multi-network architecture and loss function of the original MISA MATLAB. We then investigated critical performance trade-offs between latent space and sample sizes in independent vector analysis (IVA) problems. Both platforms were highly similar in hundreds of simulation settings, demonstrating successful replication of the original framework and flexibility to evaluate multiple configurations. We observed that a larger sample size, fewer datasets and fewer sources can lead to better IVA model performance. We then performed an IVA experiment on a large multimodal neuroimaging dataset and observed high cross-modal correlation linkage among the identified sources in both platforms, supporting MISA’s effectiveness for replicable multimodal linkage detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Network Differential in Gaussian Graphical Models from Multimodal neuroimaging data*

Network Differential in Gaussian Graphical Models from Multi...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Haleh Falakshahi Hooman Rokham Robyn Miller Jean Liu 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 USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Multimodal brain network analysis has the potential to provide insights into the mechanisms of brain disorders. Most previous studies have analyzed either unimodal brain graphs or focused on local/global graphic metrics with little consideration of details of disrupted paths in the patient group. As we show, the combination of multimodal brain graphs and disrupted path-based analysis can be highly illuminating to recognize path-based disease biomarkers. In this study, we first propose a way to estimate multimodal brain graphs using static functional network connectivity (sFNC) and gray matter features using a Gaussian graphical model of schizophrenia versus controls. Next, applying the graph theory approach we identify disconnectors or connectors in the patient group graph that create additional paths or cause absent paths compared to the control graph. Results showed several edges in the schizophrenia group graph that trigger missing or additional paths. Identified edges associated with these disrupted paths were identified both within and between dFNC and gray matter which highlights the importance of considering multimodal studies and moving beyond pairwise edges to provide a more comprehensive understanding of brain *** Relevance— We identified a path-based biomarker in schizophrenia, by imitating the structure of paths in a multimodal (sMIR+fMRI) brain graph of the control group. Identified cross-modal edges associated with disrupted paths were related to the middle temporal gyrus and cerebellar regions.

关键词：

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25页 << < 9 10 11 12 13 14 15 16 17 18 > >>

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：