Finding common task‐related regions in fMRI data from multiple subjects by periodogram clustering and clustering ensemble
Finding common task‐related regions in fMRI data from multiple subjects by periodogram clustering and clustering ensemble
复制标题
通过周期图聚类和聚类集成在多个受试者的功能磁共振成像数据中查找常见的任务相关区域
作者：
Jun Ye;Yehua Li;N. Lazar;D. Schaeffer;J. McDowell
我们提出了一种创新且实用的聚类方法，以找到对同一组刺激做出反应的不同受试者之间常见的与任务相关的大脑区域。使用功能磁共振成像（fMRI）时间序列数据，我们首先逐个体素地对每个受试者内的体素进行聚类。为了从噪声数据中提取信号，我们使用多锥化和低秩样条平滑估计每个体素的新周期图，然后使用周期图作为聚类的主要特征。我们将分裂层次聚类算法应用于单个受试者内的估计周期图，并将任务相关区域识别为具有与刺激序列的峰值频率相匹配的周期图的体素簇。最后，我们应用一种称为聚类集成的机器学习技术来查找不同主题之间常见的任务相关区域。通过模拟研究和真实的功能磁共振成像数据集说明了所提出方法的有效性。版权所有 © 2016 约翰·威利父子有限公司
We propose an innovative and practically relevant clustering method to find common task‐related brain regions among different subjects who respond to the same set of stimuli. Using functional magnetic resonance imaging (fMRI) time series data, we first cluster the voxels within each subject on a voxel by voxel basis. To extract signals out of noisy data, we estimate a new periodogram at each voxel using multi‐tapering and low‐rank spline smoothing and then use the periodogram as the main feature for clustering. We apply a divisive hierarchical clustering algorithm to the estimated periodograms within a single subject and identify the task‐related region as the cluster of voxels that have periodograms with a peak frequency matching that of the stimulus sequence. Finally, we apply a machine learning technique called clustering ensemble to find common task‐related regions across different subjects. The efficacy of the proposed approach is illustrated via a simulation study and a real fMRI data set. Copyright © 2016 John Wiley & Sons, Ltd.
DOI：
10.1007/978-1-4899-7591-1
发表时间：
2015-09
期刊：
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影响因子：
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作者：
K. Uludağ;K. Uğurbil;L. Berliner
通讯作者：
K. Uludağ;K. Uğurbil;L. Berliner