Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Neuroimage. 2012 Dec;63(4):1864-89. doi: 10.1016/j.neuroimage.2012.08.055. Epub 2012 Aug 24.
This study proposes an iterative dual-regression (DR) approach with sparse prior regularization to better estimate an individual's neuronal activation using the results of an independent component analysis (ICA) method applied to a temporally concatenated group of functional magnetic resonance imaging (fMRI) data (i.e., Tc-GICA method). An ordinary DR approach estimates the spatial patterns (SPs) of neuronal activation and corresponding time courses (TCs) specific to each individual's fMRI data with two steps involving least-squares (LS) solutions. Our proposed approach employs iterative LS solutions to refine both the individual SPs and TCs with an additional a priori assumption of sparseness in the SPs (i.e., minimally overlapping SPs) based on L(1)-norm minimization. To quantitatively evaluate the performance of this approach, semi-artificial fMRI data were created from resting-state fMRI data with the following considerations: (1) an artificially designed spatial layout of neuronal activation patterns with varying overlap sizes across subjects and (2) a BOLD time series (TS) with variable parameters such as onset time, duration, and maximum BOLD levels. To systematically control the spatial layout variability of neuronal activation patterns across the "subjects" (n=12), the degree of spatial overlap across all subjects was varied from a minimum of 1 voxel (i.e., 0.5-voxel cubic radius) to a maximum of 81 voxels (i.e., 2.5-voxel radius) across the task-related SPs with a size of 100 voxels for both the block-based and event-related task paradigms. In addition, several levels of maximum percentage BOLD intensity (i.e., 0.5, 1.0, 2.0, and 3.0%) were used for each degree of spatial overlap size. From the results, the estimated individual SPs of neuronal activation obtained from the proposed iterative DR approach with a sparse prior showed an enhanced true positive rate and reduced false positive rate compared to the ordinary DR approach. The estimated TCs of the task-related SPs from our proposed approach showed greater temporal correlation coefficients with a reference hemodynamic response function than those of the ordinary DR approach. Moreover, the efficacy of the proposed DR approach was also successfully demonstrated by the results of real fMRI data acquired from left-/right-hand clenching tasks in both block-based and event-related task paradigms.
本研究提出了一种迭代双回归（DR）方法，结合稀疏先验正则化，以更好地估计个体的神经活动，该方法使用独立成分分析（ICA）方法的结果应用于时间串联的功能磁共振成像（fMRI）数据（即 Tc-GICA 方法）。普通的 DR 方法通过两步涉及最小二乘（LS）解，估计个体 fMRI 数据的神经活动的空间模式（SPs）和相应的时间历程（TCs）。我们提出的方法采用迭代 LS 解来改进个体 SPs 和 TCs，同时基于 L(1)-范数最小化对 SPs 施加稀疏性的额外先验假设（即最小重叠 SPs）。为了定量评估该方法的性能，使用静息态 fMRI 数据创建了半人工 fMRI 数据，考虑了以下因素：（1）具有不同重叠大小的神经元激活模式的人工设计的空间布局；（2）BOLD 时间序列（TS）的变量参数，如起始时间、持续时间和最大 BOLD 水平。为了系统地控制跨“受试者”（n=12）的神经元激活模式的空间布局变异性，在基于块和事件相关任务范式的 100 个体素大小的任务相关 SPs 中，所有受试者之间的空间重叠度从最小 1 个体素（即 0.5 体素立方半径）到最大 81 个体素（即 2.5 体素半径）变化。此外，对于每个空间重叠大小，使用了几个最大 BOLD 强度百分比（即 0.5、1.0、2.0 和 3.0%）的水平。结果表明，与普通 DR 方法相比，使用稀疏先验的提出的迭代 DR 方法获得的个体神经活动 SPs 的估计具有更高的真阳性率和更低的假阳性率。我们提出的方法从任务相关 SPs 获得的 TC 与参考血液动力学响应函数具有更大的时间相关系数。此外，通过基于块和事件相关任务范式的左手/右手紧握任务中从真实 fMRI 数据获得的结果，还成功证明了所提出的 DR 方法的有效性。
