Modeling Voxel Connectivity for Brain Decoding

Itir Önal, M. Ozay, F. Yarman-Vural
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引用次数: 17

Abstract

The massively dynamic nature of human brain cannot be represented by considering only a collection of voxel intensity values obtained from fMRI measurements. It has been observed that the degree of connectivity among voxels provide important information for modeling cognitive activities. Moreover, spatially close voxels act together to generate similar BOLD responses to the same stimuli. In this study, we propose a local mesh model, called Local Mesh Model with Temporal Measurements (LMM-TM), to first estimate spatial relationship among a set of voxels using spatial and temporal data measured at each voxel, and then employ the relationship for the construction of a connectivity model for brain decoding. For this purpose, we first construct a local mesh around each voxel (called seed voxel) by connecting it to its spatially nearest neighbors. Then, we represent the BOLD response of each seed voxel in terms of linear combination of the BOLD responses of its p-nearest neighbors. The relationship between a seed voxel and its neighbors is estimated by solving a linear regression problem. The estimated mesh arc weights are used to model local connectivity among the voxels that reside in a spatial neighborhood. Using these weights as features, we train Support Vector Machines and k-Nearest Neighbor classifiers. We test our model on a visual object recognition experiment. In the experimental analysis, we observe that classifiers that employ our features perform better than classifiers that employ raw voxel intensity values, local mesh model weights and features extracted using distance metrics such as Euclidean distance, cosine similarity and Pearson correlation.
脑解码体素连接建模
人类大脑的大规模动态特性不能仅通过考虑从fMRI测量中获得的体素强度值的集合来表示。人们已经观察到,体素之间的连接程度为建模认知活动提供了重要的信息。此外,空间上接近的体素一起行动,对相同的刺激产生类似的BOLD反应。在本研究中,我们提出了一种局部网格模型,称为local mesh model with Temporal Measurements (LMM-TM),该模型首先利用在每个体素上测量的时空数据来估计一组体素之间的空间关系,然后利用这种关系构建大脑解码的连接模型。为此,我们首先围绕每个体素(称为种子体素)构建一个局部网格,将其连接到空间上最近的邻居。然后,我们将每个种子体素的BOLD响应表示为其p近邻的BOLD响应的线性组合。通过求解线性回归问题估计种子体素与其相邻体素之间的关系。估计的网格弧权用于模拟驻留在空间邻域中的体素之间的局部连通性。使用这些权重作为特征,我们训练支持向量机和k近邻分类器。我们在一个视觉对象识别实验上测试了我们的模型。在实验分析中,我们观察到使用我们的特征的分类器比使用原始体素强度值、局部网格模型权重和使用距离度量(如欧几里得距离、余弦相似度和Pearson相关性)提取的特征的分类器表现更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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