Coupled canonical polyadic decomposition of multi-group fMRI data with spatial reference and orthonormality constraints
Coupled canonical polyadic decomposition of multi-group fMRI data with spatial reference and orthonormality constraints
复制标题
具有空间参考和正交性约束的多组 fMRI 数据的耦合正则多元分解
DOI：
10.1016/j.bspc.2022.104232
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发表时间：
2023-02
影响因子：
5.1
通讯作者：
Feng Li
中科院分区：
文献类型：
--
作者：
Li-Dan Kuang;Zhi-Ming He;Jianming Zhang;Feng Li
多组fMRI数据可能具有不同类型的对象、任务、扫描等。幸运的是，耦合的规范多元分解(CCPD)需要多个张量数据集来共享一个或多个因子矩阵。考虑到空间变异性一般小于时间变异性，我们尝试CCPD将多组fMRI数据分解为共享空间地图(SM)、组特定时间进程(TCS)和受试者强度。由于感兴趣组件的空间参考是普遍可用的，并且空间正交性可以减少组件间的串扰，因此通过添加空间参考和正交性约束，提出了一种新的CCPD。具体地说，基于加速交替最小二乘法，我们进一步更新了两次共享SM：1)利用正交化的Prorustes解对共享SM分量进行正交化；2)通过最大化共享SM与空间参考之间的Pearson相关系数来确定感兴趣的分量后，通过最小化归一化共享SM的幅值部分与对应的归一化空间参考之间的平方误差来更新感兴趣的共享SM。对两组模拟和实验任务相关的fMRI数据以及24名健康人(HCS)和24名精神分裂症患者(SZ)的静态fMRI数据的实验结果表明，该方法的性能优于非约束CCPD、空间正交性约束的CCPD、广泛使用的张量独立成分分析(ICA)和半盲组信息指导ICA在仅幅度分析和复值分析中的性能。此外，通过AdaBoost，该方法估计的静息状态组特有的TCS显著地显示出较大的组差异，特别是对于感觉运动网络，从而为精神分裂症提供了一个潜在的生物标志物。
Multi-group fMRI data may possess different types of subjects, tasks, scans, etc. Fortunately, coupled canonical polyadic decomposition (CCPD) requires multiple tensor datasets to share one or more factor matrices. Considering that spatial variability is generally smaller than temporal variability, we attempt CCPD to decompose multi-group fMRI data into shared spatial maps (SMs), group-specific time courses (TCs) and subject intensities. As spatial references of interested components are generally available and the spatial orthonormality can reduce crosstalk among components, we propose a novel CCPD by adding spatial reference and orthonormality constraints. Specifically, based on accelerated alternating least squares, we further update shared SMs twice: 1) we orthonormalize shared SM components by orthogonal Procrustes solution; 2) after identifying the interested components by maximizing Pearson correlation coefficients between shared SMs and spatial references, we update interested shared SMs by minimizing the square error between magnitude part of normalized shared SMs and corresponding normalized spatial references. The results of two-group simulated and experimental task-related fMRI data as well as resting-state fMRI data with 24 healthy controls (HCs) and 24 schizophrenia patients (SZs) all show outperformed performance for the proposed method compared with unconstrained CCPD, CCPD with a spatial orthonormality constraint, widely-used tensor independent component analysis (ICA) and semi-blind group information guide ICA in both magnitude-only analysis and complex-valued analysis. Moreover, by using AdaBoost, resting-state group-specific TCs estimated by the proposed method significantly exhibit larger group differences, especially for the sensorimotor network, and thus provide a potential biomarker for schizophrenia.
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DOI：
10.32604/cmc.2020.06130
发表时间：
2020
期刊：
Computers Materials & Continua
影响因子：
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DOI：
10.1109/jstsp.2020.3003891
发表时间：
2020-10
影响因子：
7.5
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Adalı T
影响因子：
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影响因子：
8.2
作者：
Bu Yuanyang;Zhao Yongqiang;Xue Jize;Jonathan Cheung-Wai Chan;Seong G. Kong;Yi Chen;Wen Jinhuan
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影响因子：
5.7
作者：
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通讯作者：
Leahy RM