利用遗传算法提取功能磁共振成像数据的兴趣区域

S. Hiwa, Yuuki Kohri, Keisuke Hachisuka, T. Hiroyasu
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引用次数: 5

摘要

功能连通性是表征人类大脑状态最有用的指标之一,它通过大脑活动在不同脑区之间的时间过程相关性来表示。在功能连通性分析(FCA)中,基于解剖图谱将整个大脑划分为一定数量的区域,并计算大脑活动的平均时间序列。然后,对所有区域的组合重复计算两个区域的平均信号之间的相关性,最后得到整个大脑的相关矩阵。FCA让我们了解在特定刺激或任务中哪些区域是协同激活的。在这项研究中,我们尝试使用功能连接作为特征向量来表示人类大脑状态。由于大脑有许多区域,因此很难确定哪些区域是代表大脑状态的突出区域。因此,我们提出了一种自动感兴趣区域(ROI)提取方法来对人脑状态进行分类。通过功能磁共振成像(fMRI)测量时间序列脑活动,并进行FCA。将相关矩阵中的每个元素作为脑状态分类的特征向量,并使用监督学习方法学习元素特征。使用遗传算法自动确定作为特征向量的元素,即roi,以最大限度地提高大脑状态的分类精度。在两种情绪状态下测量的fMRI数据,即愉快和不愉快的情绪,被用来显示所提出的方法的有效性。数值实验表明,该方法可以提取出与愉快和不愉快情绪相关的额上回、眶额叶皮层、楔叶、小脑和小脑蚓部。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Region-of-interest extraction of fMRI data using genetic algorithms
Functional connectivity, which is indicated by time-course correlations of brain activities among different brain regions, is one of the most useful metrics to represent human brain states. In functional connectivity analysis (FCA), the whole brain is parcellated into a certain number of regions based on anatomical atlases, and the mean time series of brain activities are calculated. Then, the correlation between mean signals of two regions is repeatedly calculated for all combinations of regions, and finally, we obtain the correlation matrix of the whole brain. FCA allows us to understand which regions activate cooperatively during specific stimulus or tasks. In this study, we attempt to represent human brain states using functional connectivity as feature vectors. As there are a number of brain regions, it is difficult to determine which regions are prominent to represent the brain state. Therefore, we proposed an automatic region-of-interest (ROI) extraction method to classify human brain states. Time-series brain activities were measured by functional magnetic resonance imaging (fMRI), and FCA was performed. Each element of the correlation matrix was used as a feature vector for brain state classification, and element characteristics were learned using supervised learning methods. The elements used as feature vectors, i.e., ROIs, were determined automatically using a genetic algorithm to maximize the classification accuracy of brain states. fMRI data measured during two emotional conditions, i.e., pleasant and unpleasant emotions, were used to show the effectiveness of the proposed method. Numerical experiments revealed that the proposed method could extract the superior frontal gyrus, orbitofrontal cortex, cuneus, cerebellum, and cerebellar vermis as ROIs associated with pleasant and unpleasant emotions.
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