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Fusion of multiple neuroimaging modalities using canonical correlation analysis.

机译:使用规范相关分析融合多种神经影像模态。

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摘要

Biomedical studies frequently collect multiple measurements such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), electroencephalography (EEG), behavioral scores, and symptomatic measures from the same subject. Each modality has its own advantages and limitations, which quite often are complementary to those of other modalities. Instead of studying each dataset separately, a combined analysis promises to piece together different aspects of brain structure and function. This novel information could provide new insights to help early detection and treatment of diseases, especially diseases like schizophrenia, which impact many aspects of the brain, such as structure, function, and networks.;We develop a data fusion framework to investigate the associations across complementary modalities and to find the latent sources responsible for these associations. The proposed framework is a data driven approach that identifies cross-modality associations in the form of linear correlations across modalities. We use canonical correlation analysis (CCA) to identify these correlations. This fusion framework provides a means to jointly analyze the underlying sources of correlated information from different modalities, which when analyzed separately would only provide a limited view. We utilize the proposed fusion framework to identify two types of associations: feature-level inter-subject associations and associations across simultaneously collected data. We also propose an extension of our two-modality fusion framework to fuse multiple modalities based on their associations in the form of feature-level inter-subject correlations as well as to fuse simultaneously acquired modalities across multiple subjects (at the group level). We use the multi-dataset extension of CCA, multi-set CCA (M-CCA) to identify the associations across the multiple datasets.;Through the use of simulated data, we show that the proposed fusion framework provides an effective and less-constrained solution to the fusion problem. We have successfully applied the proposed methods to the fusion of a number of neuroimaging datasets: bi-modal feature-based fusion of fMRI and EEG as well as fMRI and sMRI data, multi-modal feature-based fusion of the fMRI, sMRI, and EEG, and finally fusion of simultaneously acquired fMRI and EEG data. The results show that our framework can identify cross-modality associations consistent to the task. It is also successful in identifying functional activation changes and differences in gray matter concentration due to disease. Due to their multivariate nature, the proposed methods can identify connectivity across different areas in the brain. More importantly we show that with the addition of more modalities, the specificity of these inferences increases. The findings on the simultaneous data show the usefulness of the method for identifying activation patterns in the form of amplitude modulations that are common to both fMRI and EEG data. The presented framework is flexible and can be applicable to other modalities as well as to different domains for discovering relationships among multiple types of measurements.
机译:生物医学研究经常收集来自同一受试者的多项测量结果,例如功能磁共振成像(fMRI),结构MRI(sMRI),脑电图(EEG),行为评分和对症指标。每种方式都有其自身的优势和局限性,而这些优势和局限性通常是对其他方式的补充。与其单独研究每个数据集,不如将它们组合起来分析,以将大脑结构和功能的不同方面拼凑起来。这些新颖的信息可以提供新的见解,以帮助早期发现和治疗疾病,特别是像精神分裂症这样的疾病,这些疾病会影响大脑的许多方面,例如结构,功能和网络。;我们开发了一个数据融合框架来研究跨学科的关联补充方式,并找到负责这些协会的潜在资源。所提出的框架是一种数据驱动的方法,该方法以跨模态的线性相关形式识别跨模态关联。我们使用规范相关分析(CCA)来识别这些相关。这种融合框架提供了一种方法,可以从不同的方式共同分析相关信息的潜在来源,当分别进行分析时,只能提供有限的观点。我们利用提出的融合框架来识别两种类型的关联:特征级对象间关联和跨同时收集的数据的关联。我们还提议扩展我们的两种模式融合框架,以基于特征级别的主题间关联的形式基于它们的关联来融合多个模态,以及在多个主题之间(组级别)融合同时获取的模态。我们使用CCA的多数据集扩展,多集CCA(M-CCA)来识别多个数据集之间的关联。;通过使用仿真数据,我们证明了所提出的融合框架提供了有效且受限制较少的融合框架解决融合问题。我们已成功地将提出的方法应用于许多神经影像数据集的融合:基于双峰特征的fMRI和EEG以及fMRI和sMRI数据融合,基于多峰特征的fMRI,sMRI和脑电图,最后融合同时采集的功能磁共振成像和脑电图数据。结果表明,我们的框架可以识别与任务一致的跨模式关联。它还成功地确定了由于疾病引起的功能性激活变化和灰质浓度差异。由于它们的多元性质,所提出的方法可以识别大脑不同区域之间的连通性。更重要的是,我们表明,随着更多形式的增加,这些推论的特异性也随之提高。在同时数据上的发现表明,该方法对于以fMRI和EEG数据所共有的幅度调制形式识别激活模式是有用的。所提出的框架是灵活的,并且可以适用于其他模态以及用于发现多种类型的测量之间的关系的不同领域。

著录项

  • 作者

    Correa, Nicolle M.;

  • 作者单位

    University of Maryland, Baltimore County.;

  • 授予单位 University of Maryland, Baltimore County.;
  • 学科 Biology Neuroscience.;Engineering Electronics and Electrical.;Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 127 p.
  • 总页数 127
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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