Aarabi Ardalan, Osharina Victoria, Wallois Fabrice
Faculty of Medicine, University of Picardie Jules Verne, Amiens 80036, France; GRAMFC-Inserm U1105, University Research Center (CURS), University Hospital, Amiens, 80054 France.
GRAMFC-Inserm U1105, University Research Center (CURS), University Hospital, Amiens, 80054 France.
Neuroimage. 2017 Jul 15;155:25-49. doi: 10.1016/j.neuroimage.2017.04.048. Epub 2017 Apr 25.
Slow and rapid event-related designs are used in fMRI and functional near-infrared spectroscopy (fNIRS) experiments to temporally characterize the brain hemodynamic response to discrete events. Conventional averaging (CA) and the deconvolution method (DM) are the two techniques commonly used to estimate the Hemodynamic Response Function (HRF) profile in event-related designs. In this study, we conducted a series of simulations using synthetic and real NIRS data to examine the effect of the main confounding factors, including event sequence timing parameters, different types of noise, signal-to-noise ratio (SNR), temporal autocorrelation and temporal filtering on the performance of these techniques in slow and rapid event-related designs. We also compared systematic errors in the estimates of the fitted HRF amplitude, latency and duration for both techniques. We further compared the performance of deconvolution methods based on Finite Impulse Response (FIR) basis functions and gamma basis sets. Our results demonstrate that DM was much less sensitive to confounding factors than CA. Event timing was the main parameter largely affecting the accuracy of CA. In slow event-related designs, deconvolution methods provided similar results to those obtained by CA. In rapid event-related designs, our results showed that DM outperformed CA for all SNR, especially above -5 dB regardless of the event sequence timing and the dynamics of background NIRS activity. Our results also show that periodic low-frequency systemic hemodynamic fluctuations as well as phase-locked noise can markedly obscure hemodynamic evoked responses. Temporal autocorrelation also affected the performance of both techniques by inducing distortions in the time profile of the estimated hemodynamic response with inflated t-statistics, especially at low SNRs. We also found that high-pass temporal filtering could substantially affect the performance of both techniques by removing the low-frequency components of HRF profiles. Our results emphasize the importance of characterization of event timing, background noise and SNR when estimating HRF profiles using CA and DM in event-related designs.
在功能磁共振成像（fMRI）和功能近红外光谱（fNIRS）实验中，慢事件相关设计和快事件相关设计用于从时间上表征大脑对离散事件的血液动力学反应。传统平均法（CA）和反卷积法（DM）是事件相关设计中常用的两种估计血液动力学反应函数（HRF）轮廓的技术。在本研究中，我们使用合成和真实的近红外光谱数据进行了一系列模拟，以检验主要混杂因素的影响，这些因素包括事件序列时间参数、不同类型的噪声、信噪比（SNR）、时间自相关和时间滤波对这些技术在慢事件相关设计和快事件相关设计中的性能的影响。我们还比较了这两种技术在拟合HRF幅度、潜伏期和持续时间估计中的系统误差。我们进一步比较了基于有限脉冲响应（FIR）基函数和伽马基集的反卷积方法的性能。我们的结果表明，与CA相比，DM对混杂因素的敏感性要低得多。事件时间是在很大程度上影响CA准确性的主要参数。在慢事件相关设计中，反卷积方法得到的结果与CA得到的结果相似。在快事件相关设计中，我们的结果表明，对于所有信噪比，尤其是在-5 dB以上，无论事件序列时间和背景近红外光谱活动的动态如何，DM都优于CA。我们的结果还表明，周期性的低频全身血液动力学波动以及锁相噪声会明显掩盖血液动力学诱发反应。时间自相关也会影响这两种技术的性能，通过在估计的血液动力学反应的时间轮廓中引入扭曲并使t统计量膨胀，尤其是在低信噪比时。我们还发现，高通时间滤波可以通过去除HRF轮廓的低频成分而显著影响这两种技术的性能。我们的结果强调了在事件相关设计中使用CA和DM估计HRF轮廓时，表征事件时间、背景噪声和信噪比的重要性。
