基于ICA和PLS-A数据驱动的fMRI数据集分割
Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach
摘要：
基于标准的一般线性模型（GLM）和谱聚类的功能性磁共振成像（fMRI）数据的受试者间分割最近被提出作为一种手段来缓解与fMRI中的空间归一化相关的问题。然而，对于其所有的吸引力，基于GLM的分割方法引入了其自身的偏差，其形式为关于血流动力学反应函数（HRF）的形状和任务相关信号变化的先验知识，或者关于任务期间的受试者行为。在本文中，我们介绍了一个数据驱动的版本的谱聚类parcellation，独立成分分析（ICA）和偏最小二乘（PLS）的基础上，而不是GLM。首先，自动选择多个独立组件。种子体素，然后从相关联的ICA地图，我们计算的PLS潜在变量之间的fMRI信号的种子体素（其中包括区域变化的HRF）和所有体素的信号的主成分。最后，我们用PLS潜变量的谱聚类对所有被试数据进行了分组。我们提出的方法在单学科和多学科fMRI数据集上的应用结果。初步的实验结果，GLM的t值和PLS导出的t值的包裹内方差进行评估，表明这种数据驱动的方法提供了改进的包裹精度GLM为基础的技术。
英文摘要：
Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM)and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.
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