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首页> 外文会议>SPIE Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Prediction of fMRI time series of a single voxel using Radial BasisFunction Neural Network
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Prediction of fMRI time series of a single voxel using Radial BasisFunction Neural Network

机译：径向基础功能神经网络预测单个体素的FMRI时间序列

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A great deal of current literature regarding functional neuroimaging has elucidated the relationships of neurons distributed all over the brain. Modern neuroimaging techniques, such as the functional MRI (fMRI), provide a convenient tool for people to study the correlation among different voxels as well as the spatio-temporal patterns of brain activity. In this study, we present a computational model using radial basis function neural network (RBF-NN) to predict the fMRI voxel activation with the activation of other voxels acquired at the same time. The fMRI data from a visual images stimuli presentation experiment was separated into two sets; one was used to train the model, and the other to validate the accuracy or generalizability of the model. In the visual stimuli presentation experiment, the subject did simple one-back-repetition tasks when four categories of stimuli (houses, faces, cars, and cats) were presented. Voxel sets A and B were selected from fMRI data by two different voxel selection criterion: (1) Voxel set A are those activated for any kind of object stronger than the other three objects in regions of interest (ROIs) without correction (P=0.001); (2) Voxel set B are those activated for at least one of the categories of stimuli within the ROIs (FWE correction, P=0.05). RBF-NN regression models construct the nonlinear relationship between the activation of voxels in A and B. Our test results showed that RBF-NN can capture the nonlinear relationship existing in neurons and reveal the relationship between voxel's activation from different brain regions.
机译：关于功能性神经模仿的大量目前的文献阐述了神经元在大脑上分布的神经元的关系。现代神经影像技术，如功能性MRI（FMRI），为人们提供了一种方便的工具，用于研究不同体素之间的相关性以及大脑活动的时空模式。在这项研究中，我们使用径向基函数神经网络（RBF-NN）呈现计算模型，以预测与同时获取的其他体素的激活的FMRI体素激活。来自视觉图像刺激呈现实验的FMRI数据分为两组;一个用于训练模型，另一个用于验证模型的准确性或概括性。在视觉刺激演示实验中，当呈现四类刺激（房屋，面部，汽车和猫）时，该主题是简单的一次重复任务。通过两个不同的体素选择标准从FMRI数据中选择了Voxel Set A和B：（1）Voxel Set a是那些对任何类型的物体激活的物体比其他三个物体（Rois）中的其他三个物体（Rois）的其他三个物体为激活的那些（P = 0.001 ）; （2）Voxel Set B是用于ROI内的至少一个刺激类别的那些（FWE校正，P = 0.05）。 RBF-NN回归模型构建A和B中体素激活之间的非线性关系。我们的测试结果表明，RBF-NN可以捕获神经元中存在的非线性关系，并揭示Voxel从不同脑区激活之间的关系。

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