Multimodal Functional Imaging Laboratory, Biomedical Engineering Department, McGill University, 3801 Rue University 751, Montreal, QC, H3A2B4, Canada.
Department of Physics and PERFORM center, Concordia University, Montreal, Canada.
Sci Rep. 2021 Mar 16;11(1):5964. doi: 10.1038/s41598-021-85386-0.
In functional near infrared spectroscopy (fNIRS), deconvolution analysis of oxy and deoxy-hemoglobin concentration changes allows estimating specific hemodynamic response functions (HRF) elicited by neuronal activity, taking advantage of the fNIRS excellent temporal resolution. Diffuse optical tomography (DOT) is also becoming the new standard reconstruction procedure as it is more accurate than the modified Beer Lambert law approach at the sensor level. The objective of this study was to assess the relevance of HRF deconvolution after DOT constrained along the cortical surface. We used local personalized fNIRS montages which consists in optimizing the position of fNIRS optodes to ensure maximal sensitivity to subject specific target brain regions. We carefully evaluated the accuracy of deconvolution when applied after DOT, using realistic simulations involving several HRF models at different signal to noise ratio (SNR) levels and on real data related to motor and visual tasks in healthy subjects and from spontaneous pathological activity in one patient with epilepsy. We demonstrated that DOT followed by deconvolution was able to accurately recover a large variability of HRFs over a large range of SNRs. We found good performances of deconvolution analysis for SNR levels usually encountered in our applications and we were able to reconstruct accurately the temporal dynamics of HRFs in real conditions.
在功能性近红外光谱 (fNIRS) 中，通过去卷积分析氧合血红蛋白和脱氧血红蛋白浓度的变化，可以利用 fNIRS 出色的时间分辨率来估计神经元活动引起的特定血液动力学响应函数 (HRF)。扩散光学断层扫描 (DOT) 也正在成为新的重建程序标准，因为它比传感器级别的修正 Beer Lambert 定律方法更准确。本研究的目的是评估在沿皮质表面进行 DOT 约束后进行 HRF 去卷积的相关性。我们使用局部个性化的 fNIRS 蒙太奇，通过优化 fNIRS 光导位置来确保对特定于主体的目标脑区的最大灵敏度。我们使用涉及不同信噪比 (SNR) 水平下几种 HRF 模型的真实模拟以及与健康受试者的运动和视觉任务相关的真实数据以及一名癫痫患者自发病理活动的真实数据，仔细评估了 DOT 后应用去卷积的准确性。我们证明，DOT 后进行去卷积能够在很大的 SNR 范围内准确地恢复大量的 HRF 变化。我们发现去卷积分析在我们的应用中通常遇到的 SNR 水平下具有良好的性能，并且能够在真实条件下准确地重建 HRF 的时间动态。
