School of Physics, University of Sydney, New South Wales 2006, Australia; Queensland Institute of Medical Research, Herston, Queensland 4006, Australia; Brain Dynamics Center, Sydney Medical School Western, University of Sydney, New South Wales 2145, Australia.
School of Physics, University of Sydney, New South Wales 2006, Australia; Brain Dynamics Center, Sydney Medical School Western, University of Sydney, New South Wales 2145, Australia; Cooperative Research Center for Alertness, Safety & Productivity, Notting Hill, Victoria 3168, Australia; Center for Integrated Research & Understanding of Sleep, Glebe, New South Wales 2037, Australia.
Neuroimage. 2014 Jul 1;94:203-215. doi: 10.1016/j.neuroimage.2014.03.001. Epub 2014 Mar 12.
Functional magnetic resonance imaging (fMRI) is a powerful and broadly used means of non-invasively mapping human brain activity. However fMRI is an indirect measure that rests upon a mapping from neuronal activity to the blood oxygen level dependent (BOLD) signal via hemodynamic effects. The quality of estimated neuronal activity hinges on the validity of the hemodynamic model employed. Recent work has demonstrated that the hemodynamic response has non-separable spatiotemporal dynamics, a key property that is not implemented in existing fMRI analysis frameworks. Here both simulated and empirical data are used to demonstrate that using a physiologically based model of the spatiotemporal hemodynamic response function (stHRF) results in a quantitative improvement of the estimated neuronal response relative to unphysical space-time separable forms. To achieve this, an integrated spatial and temporal deconvolution is established using a recently developed stHRF. Simulated data allows the variation of key parameters such as noise and the spatial complexity of the neuronal drive, while knowing the neuronal input. The results demonstrate that the use of a spatiotemporally integrated HRF can avoid "ghost" neuronal responses that can otherwise be falsely inferred. Applying the spatiotemporal deconvolution to high resolution fMRI data allows the recovery of neuronal responses that are consistent with independent electrophysiological measures.
功能磁共振成像（fMRI）是一种强大且广泛应用的无创性人脑活动映射方法。然而，fMRI 是一种间接测量方法，它通过血液动力学效应从神经元活动映射到血氧水平依赖（BOLD）信号。估计神经元活动的质量取决于所采用的血流动力学模型的有效性。最近的工作表明，血流动力学响应具有不可分离的时空动力学，这是一个在现有 fMRI 分析框架中未实现的关键特性。在这里，使用模拟和经验数据来证明，使用时空血流动力学响应函数（stHRF）的生理基础模型可相对于非物理时空可分离形式提高估计的神经元响应的定量精度。为了实现这一点，使用最近开发的 stHRF 建立了集成的时空解卷积。模拟数据允许对关键参数（如噪声和神经元驱动的空间复杂性）进行变化，同时了解神经元输入。结果表明，使用时空集成的 HRF 可以避免可能错误推断的“幽灵”神经元响应。将时空解卷积应用于高分辨率 fMRI 数据可恢复与独立电生理测量一致的神经元响应。
