Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition
Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition
复制标题
通过独立成分分析和平移不变正则多元分解相结合的多受试者功能磁共振成像分析
DOI：
10.1016/j.jneumeth.2015.08.023
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发表时间：
2015
影响因子：
3
通讯作者：
Vince D. Calhoun
中科院分区：
文献类型：
--
作者：
Li-Dan Kuang;Qiu-Hua Lin;Xiao-Feng Gong;Fengyu Cong;Jing Sui;Vince D. Calhoun
背景当分析具有对象间可变性的多对象fMRI数据时，规范多进分解(CPD)可能面临局部最优问题。Beckmann和Smith提出了一种张量PICA方法，通过将CPD和ICA相结合，将独立约束融入到空间通道中，缓解了被试之间空间地图(SM)的变异性问题。假设多个受试者共享相同的TC，但具有不同的时延，基于平移不变CP(SCP)的思想，我们将依赖于受试者的TC时延纳入CP模型。我们使用ICA作为初始化步骤，为平移不变的CPD提供聚合混合矩阵，以估计具有与对象相关的时延和强度的共享TC。结果使用模拟的fMRI数据和实际的fMRI数据，我们证明了该方法改善了共享的sms和tcs的估计，以及与受试者相关的TC延迟和强度。与已有方法(S)相比，该方法在TC延迟较大时表现出了比张量PICA更好的性能，并且优于基于SM正交性约束的SCP和基于独立成分分析的SM初始化的SCP。结论具有受试者相关延迟的TCS符合多受试者fMRI数据的真实情况。该方法适用于具有较大时空变异性的多主体fMRI数据的分解。
BackgroundCanonical 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 methodThis 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 CP model based on the idea of shift-invariant CP (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.ResultsUsing 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.ConclusionsTCs 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.