A complex way to compute fMRI activation
A complex way to compute fMRI activation
复制标题
DOI：
10.1016/j.neuroimage.2004.06.042
复制
发表时间：
2004-11-01
期刊：
影响因子：
5.7
通讯作者：
Logan, BR
中科院分区：
文献类型：
--
作者：
Rowe, DB;Logan, BR
在功能磁共振成像中，由于磁场不均匀性导致的相位缺陷，傅里叶或非傅里叶“图像重建”之后的体素时间过程是复值。几乎所有功能磁共振成像研究都是基于体素时间过程得出功能“激活”[Bandettini, P., Jesmanowiez, A., Wong, E., Hyde, J.S., 1993。人脑功能 MRI 中时间过程数据集的处理策略。马格。共振。医学。 30 (2): 161-173 和 Cox, R.W.、Jesmanowicz, A.、Hyde, J.S., 1995。实时功能磁共振成像。马格。共振。医学。 33（2）：230-2361。在这里，我们建议直接对整个复杂或双变量数据进行建模，而不仅仅是仅对幅度数据进行建模。使用非线性多元回归模型对复杂信号的激活进行建模，并导出似然比测试以确定每个体素中的激活。我们研究了模型在真实数据集上的性能，然后在具有不同激活对比度效果的模拟研究中比较不同信噪比下的纯幅度模型和复杂模型。 (C) 2004 Elsevier Inc. 保留所有权利。
In functional magnetic resonance imaging, voxel time courses after Fourier or non-Fourier "image reconstruction" are complex valued as a result of phase imperfections due to magnetic field inhomogeneities. Nearly all fMRI studies derive functional "activation" based on magnitude voxel time courses [Bandettini, P., Jesmanowiez, A., Wong, E., Hyde, J.S., 1993. Processing strategies for time-course data sets in functional MRI of the human brain. Magn. Reson. Med. 30 (2): 161-173 and Cox, R.W., Jesmanowicz, A., Hyde, J.S., 1995. Real-time functional magnetic resonance imaging. Magn. Reson. Med. 33 (2): 230-2361. Here, we propose to directly model the entire complex or bivariate data rather than just the magnitude-only data. A nonlinear multiple regression model is used to model activation of the complex signal, and a likelihood ratio test is derived to determine activation in each voxel. We investigate the performance of the model on a real dataset, then compare the magnitude-only and complex models under varying signal-to-noise ratios in a simulation study with varying activation contrast effects. (C) 2004 Elsevier Inc. All rights reserved.