Berlin Institute of Technology, Machine Learning Group, Franklinstr 28/29, 10587 Berlin, Germany.
Neuroimage. 2012 Jul 16;61(4):1031-42. doi: 10.1016/j.neuroimage.2012.04.015. Epub 2012 Apr 14.
The goal of most functional Magnetic Resonance Imaging (fMRI) analyses is to investigate neural activity. Many fMRI analysis methods assume that the temporal dynamics of the hemodynamic response function (HRF) to neural activation is separable from its spatial dynamics. Although there is empirical evidence that the HRF is more complex than suggested by space-time separable canonical HRF models, it is difficult to assess how much information about neural activity is lost when assuming space-time separability. In this study we directly test whether spatiotemporal variability in the HRF that is not captured by separable models contains information about neural signals. We predict intracranially measured neural activity from simultaneously recorded fMRI data using separable and non-separable spatiotemporal deconvolutions of voxel time series around the recording electrode. Our results show that abandoning the spatiotemporal separability assumption consistently improves the decoding accuracy of neural signals from fMRI data. We compare our findings with results from optical imaging and fMRI studies and discuss potential implications for classical fMRI analyses without invasive electrophysiological recordings.
大多数功能磁共振成像 (fMRI) 分析的目标是研究神经活动。许多 fMRI 分析方法假设，对神经激活的血流动力学响应函数 (HRF) 的时间动态与其空间动态是可分离的。尽管有经验证据表明 HRF 比时空可分离的典型 HRF 模型所建议的更为复杂，但很难评估在假设时空可分离性时会丢失多少关于神经活动的信息。在这项研究中，我们直接测试了不可分离模型捕获的 HRF 的时空可变性是否包含有关神经信号的信息。我们使用记录电极周围体素时间序列的可分离和不可分离时空反卷积，从同时记录的 fMRI 数据中预测颅内测量的神经活动。我们的结果表明，放弃时空可分离性假设始终可以提高从 fMRI 数据解码神经信号的准确性。我们将我们的发现与光学成像和 fMRI 研究的结果进行了比较，并讨论了对没有侵入性电生理记录的经典 fMRI 分析的潜在影响。
