Liu Ning, Cui Xu, Bryant Daniel M, Glover Gary H, Reiss Allan L
Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA 94305, USA ; Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, CA 94305, USA ; Equally contributed to this study.
Symbolic Systems Program, Stanford University, Stanford, CA 94305, USA.
Biomed Opt Express. 2015 Feb 27;6(3):1074-89. doi: 10.1364/BOE.6.001074. eCollection 2015 Mar 1.
Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying brain function because it is non-invasive, non-irradiating and relatively inexpensive. Further, fNIRS potentially allows measurement of hemodynamic activity with high temporal resolution (milliseconds) and in naturalistic settings. However, in comparison with other imaging modalities, namely fMRI, fNIRS has a significant drawback: limited sensitivity to hemodynamic changes in deep-brain regions. To overcome this limitation, we developed a computational method to infer deep-brain activity using fNIRS measurements of cortical activity. Using simultaneous fNIRS and fMRI, we measured brain activity in 17 participants as they completed three cognitive tasks. A support vector regression (SVR) learning algorithm was used to predict activity in twelve deep-brain regions using information from surface fNIRS measurements. We compared these predictions against actual fMRI-measured activity using Pearson's correlation to quantify prediction performance. To provide a benchmark for comparison, we also used fMRI measurements of cortical activity to infer deep-brain activity. When using fMRI-measured activity from the entire cortex, we were able to predict deep-brain activity in the fusiform cortex with an average correlation coefficient of 0.80 and in all deep-brain regions with an average correlation coefficient of 0.67. The top 15% of predictions using fNIRS signal achieved an accuracy of 0.7. To our knowledge, this study is the first to investigate the feasibility of using cortical activity to infer deep-brain activity. This new method has the potential to extend fNIRS applications in cognitive and clinical neuroscience research.
功能近红外光谱技术（fNIRS）是一种在研究脑功能方面越来越受欢迎的技术，因为它具有非侵入性、无辐射且相对成本较低的特点。此外，fNIRS有可能以高时间分辨率（毫秒级）并在自然环境中测量血液动力学活动。然而，与其他成像方式（即功能磁共振成像，fMRI）相比，fNIRS有一个显著的缺点：对脑深部区域血液动力学变化的敏感性有限。为了克服这一限制，我们开发了一种计算方法，利用皮质活动的fNIRS测量来推断脑深部活动。我们使用同步的fNIRS和fMRI，在17名参与者完成三项认知任务时测量他们的脑活动。使用支持向量回归（SVR）学习算法，利用来自表面fNIRS测量的信息来预测十二个脑深部区域的活动。我们使用皮尔逊相关性将这些预测结果与实际fMRI测量的活动进行比较，以量化预测性能。为了提供一个比较基准，我们还使用fMRI测量的皮质活动来推断脑深部活动。当使用来自整个皮质的fMRI测量活动时，我们能够预测梭状回皮质中的脑深部活动，平均相关系数为0.80，在所有脑深部区域的平均相关系数为0.67。使用fNIRS信号的前15%的预测准确率达到了0.7。据我们所知，本研究是首次调查利用皮质活动推断脑深部活动的可行性。这种新方法有可能扩展fNIRS在认知和临床神经科学研究中的应用。
