Shared and Subject-Specific Dictionary Learning (ShSSDL) Algorithm for Multisubject fMRI Data Analysis
Shared and Subject-Specific Dictionary Learning (ShSSDL) Algorithm for Multisubject fMRI Data Analysis
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DOI：
10.1109/tbme.2018.2806958
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发表时间：
2018-11-01
影响因子：
4.6
通讯作者：
Adali, Tulay
中科院分区：
文献类型：
--
作者：
Iqbal, Asif;Seghouane, Abd-Krim;Adali, Tulay
目的：功能磁共振成像（fMRI）数据的分析，从多个主题是在许多医学成像研究的核心，和基于字典学习（DL）的方法，最近注意到有前途的解决方案的问题。然而，DL为基础的方法提出的功能磁共振成像分析的日期不自然地扩展到多学科分析。在本文中，我们提出了一个DL算法的多学科fMRI数据分析。研究方法：所提出的算法[命名为共享和特定主题的字典学习（ShSSDL）]是基于时间级联，这是特别有吸引力的多学科任务相关的功能磁共振成像数据集的分析。它不同于现有的DL算法在其稀疏编码和字典更新阶段，并具有学习的字典共享的所有科目，以及一组特定于主题的字典的优势。结果：使用模拟和真实的fMRI数据集说明了所提出的DL算法的性能。结果表明，它可以成功地提取共享以及特定主题的潜在成分。结论：除了提供一个新的DL方法，当应用于多学科的功能磁共振成像数据分析，该算法生成一个组的水平，以及一组特定主题的空间地图。重要性：该算法具有同时学习多个字典的优点，为我们提供了一个共享的以及歧视性的信息源分析的功能磁共振成像数据集。
Objective: Analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects is at the heart of many medical imaging studies, and approaches based on dictionary learning (DL) are recently noted as promising solutions to the problem. However, the DL-based methods for fMRI analysis proposed to date do not naturally extend to multisubject analysis. In this paper, we propose a DL algorithm for multisubject fMRI data analysis. Methods: The proposed algorithm [named shared and subject-specific dictionary learning (ShSSDL)] is derived based on a temporal concatenation, which is particularly attractive for the analysis of multisubject task-related fMRI datasets. It differs from existing DL algorithms in both its sparse coding and dictionary update stages and has the advantage of learning a dictionary shared by all subjects as well as a set of subject-specific dictionaries. Results: The performance of the proposed DL algorithm is illustrated using simulated and real fMRI datasets. The results show that it can successfully extract shared as well as subject-specific latent components. Conclusion: In addition to offering a new DL approach, when applied on multisubject fMRI data analysis, the proposed algorithm generates a group level as well as a set of subject-specific spatial maps. Significance: The proposed algorithm has the advantage of learning simultaneously multiple dictionaries providing us with a shared as well discriminative source of information about the analyzed fMRI datasets.