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个人信息Personal Information
教授
博士生导师
硕士生导师
主要任职：人力资源处处长（党委教师工作部部长、党委人才办公室主任）【兼党委组织部副部长】
性别：男
毕业院校：上海交通大学
学位：博士
所在单位：生物医学工程学院
学科：生物医学工程. 信号与信息处理. 模式识别与智能系统
电子邮箱：cong@dlut.edu.cn
Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition
点击次数：
论文类型：期刊论文
发表时间：2015-12-30
发表刊物：JOURNAL OF NEUROSCIENCE METHODS
收录刊物：SCIE、PubMed、Scopus
卷号：256
页面范围：127-140
ISSN号：0165-0270
关键字：Canonical polyadic decomposition (CPD); Independent component analysis (ICA); Multi-subject fMRI data; Inter-subject variability; Tensor PICA; Shift-invariant CP (SCP)
摘要：Background: Canonical polyadic decomposition (CPD) may face a local optimal problem when analyzing multi-subject fMRI data with inter-subject variability. Beckmann and Smith proposed a tensor PICA approach that incorporated an independence constraint to the spatial modality by combining CPD with ICA, and alleviated the problem of inter-subject spatial map (SM) variability.
New method: This study extends tensor PICA to incorporate additional inter-subject time course (TC) variability and to connect CPD and ICA in a new way. Assuming multiple subjects share common TCs but with different time delays, we accommodate subject-dependent TC delays into the CF model based on the idea of shift-invariant CF (SCP). We use ICA as an initialization step to provide the aggregating mixing matrix for shift-invariant CPD to estimate shared TCs with subject-dependent delays and intensities. We then estimate shared SMs using a least-squares fit post shift-invariant CPD.
Results: Using simulated fMRI data as well as actual fMRI data we demonstrate that the proposed approach improves the estimates of the shared SMs and TCs, and the subject-dependent TC delays and intensities. The default mode component illustrates larger TC delays than the task-related component.
Comparison with existing method(s): The proposed approach shows improvements over tensor PICA in particular when TC delays are large, and also outperforms SCP with SM orthogonality constraint and SCP with ICA-based SM initialization.
Conclusions: TCs with subject-dependent delays conform to the true situation of multi-subject fMRI data. The proposed approach is suitable for decomposing multi-subject fMRI data with large inter-subject temporal and spatial variability. (C) 2015 Elsevier B.V. All rights reserved.