Adaptive independent vector analysis for multi-subject complex-valued fMRI data
Adaptive independent vector analysis for multi-subject complex-valued fMRI data
复制标题
多对象复值功能磁共振成像数据的自适应独立向量分析
DOI：
10.1016/j.jneumeth.2017.01.017
复制
发表时间：
2017
影响因子：
3
通讯作者：
Calhoun Vince D.
中科院分区：
文献类型：
--
作者：
Kuang Lidan;Lin Qiuhua;Gong Xiaofeng;Cong Fengyu;Calhoun Vince D.
背景复值功能磁共振成像数据可以提供超出仅幅度数据的额外见解。然而，独立矢量分析（IVA）在对仅幅度的 fMRI 数据进行群体分析方面表现出了巨大的潜力，但很少应用于复数值的 fMRI 数据。该应用中的主要挑战包括源分量向量 (SCV) 分布的极高噪声性质和巨大变化性。新方法为了解决这些挑战，我们提出了一种自适应定点 IVA 算法，用于分析多对象复值 fMRI 数据。我们利用基于多元广义高斯分布 (MGGD) 的非线性函数来匹配不同的 SCV 分布，其中使用最大似然估计来估计 MGGD 形状参数。为了实现我们的去噪目标，我们更新了主导 SCV 子空间中基于 MGGD 的非线性，并采用了基于 IVA 估计中的相位信息的后 IVA 去噪策略。我们还将 fMRI 数据的伪协方差矩阵纳入算法中，以强调复值 fMRI 源的非圆性。结果模拟和实验 fMRI 数据的结果证明了我们方法的有效性。与现有方法相比，我们的方法比典型的复值 IVA 算法表现出显着改进，特别是在较高的噪声水平和较大的空间和时间变化期间。正如预期的那样，对于任务相关（393％）和默认模式（301％）空间图，所提出的复值IVA算法比仅幅度方法检测到更连续和合理的激活。结论所提出的方法适合分解多主体复值fMRI数据，并且在捕获额外主体变异性方面具有巨大潜力。
BackgroundComplex-valued fMRI data can provide additional insights beyond magnitude-only data. However, independent vector analysis (IVA), which has exhibited great potential for group analysis of magnitude-only fMRI data, has rarely been applied to complex-valued fMRI data. The main challenges in this application include the extremely noisy nature and large variability of the source component vector (SCV) distribution.New methodTo address these challenges, we propose an adaptive fixed-point IVA algorithm for analyzing multiple-subject complex-valued fMRI data. We exploited a multivariate generalized Gaussian distribution (MGGD)- based nonlinear function to match varying SCV distributions in which the MGGD shape parameter was estimated using maximum likelihood estimation. To achieve our de-noising goal, we updated the MGGD-based nonlinearity in the dominant SCV subspace, and employed a post-IVA de-noising strategy based on phase information in the IVA estimates. We also incorporated the pseudo-covariance matrix of fMRI data into the algorithm to emphasize the noncircularity of complex-valued fMRI sources.ResultsResults from simulated and experimental fMRI data demonstrated the efficacy of our method.Comparison with existing method(s)Our approach exhibited significant improvements over typical complex-valued IVA algorithms, especially during higher noise levels and larger spatial and temporal changes. As expected, the proposed complex-valued IVA algorithm detected more contiguous and reasonable activations than the magnitude-only method for task-related (393%) and default mode (301%) spatial maps.ConclusionsThe proposed approach is suitable for decomposing multi-subject complex-valued fMRI data, and has great potential for capturing additional subject variability.