Departamento de Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid, Leganés, Madrid, Spain.
Med Image Anal. 2012 Feb;16(2):451-8. doi: 10.1016/j.media.2011.11.002. Epub 2011 Nov 25.
Neuroimaging plays a fundamental role in the study of human cognitive neuroscience. Functional magnetic resonance imaging (fMRI), based on the Blood Oxygenation Level Dependent signal, is currently considered as a standard technique for a system level understanding of the human brain. The problem of identifying regionally specific effects in neuroimaging data is usually solved by applying Statistical Parametric Mapping (SPM). Here, a mutual information (MI) criterion is used to identify regionally specific effects produced by a task. In particular, two MI estimators are presented for its use in fMRI data. The first one uses a Parzen probability density estimation, and the second one is based on a K Nearest Neighbours (KNN) estimation. Additionally, a statistical measure has been introduced to automatically detect the voxels which are relevant to the fMRI task. Experiments demonstrate the advantages of MI estimators over SPM maps; firstly, providing more significant differences between relevant and irrelevant voxels; secondly, presenting more focalized activation; and, thirdly, detecting small areas related to the task. These findings, and the improved performance of KNN MI estimator in multisubject and multistimuli studies, make the proposed methods a good alternative to SPM.
神经影像学在人类认知神经科学的研究中起着至关重要的作用。基于血氧水平依赖信号的功能磁共振成像（fMRI）目前被认为是理解人类大脑系统水平的标准技术。在神经影像学数据中识别区域特异性效应的问题通常通过应用统计参数映射（SPM）来解决。在这里，互信息（MI）准则用于识别任务产生的区域特异性效应。特别是，提出了两种 MI 估计器用于 fMRI 数据。第一种使用 Parzen 概率密度估计，第二种基于 K 最近邻（KNN）估计。此外，还引入了一种统计度量来自动检测与 fMRI 任务相关的体素。实验证明了 MI 估计器相对于 SPM 图的优势；首先，在相关和不相关体素之间提供更显著的差异；其次，呈现更集中的激活；第三，检测与任务相关的小区域。这些发现，以及 KNN MI 估计器在多主体和多刺激研究中的改进性能，使得所提出的方法成为 SPM 的一个很好的替代方案。
