Rangaprakash D, David Olivier, Barry Robert L, Deshpande Gopikrishna
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.
Université Grenoble Alpes, Inserm, U1216, Grenoble Institute of Neuroscience, F-38000, Grenoble, France.
bioRxiv. 2023 Oct 27:2023.02.27.530359. doi: 10.1101/2023.02.27.530359.
Resting-state functional MRI (rs-fMRI) is a popular technology that has enriched our understanding of brain and spinal cord functioning, including how different regions communicate (connectivity). But fMRI is an indirect measure of neural activity capturing blood hemodynamics. The hemodynamic response function (HRF) interfaces between the unmeasured neural activity and measured fMRI time series. The HRF is variable across brain regions and individuals, and is modulated by non-neural factors. Ignoring this HRF variability causes errors in FC estimates. Hence, it is crucial to reliably estimate the HRF from rs-fMRI data. Robust techniques have emerged to estimate the HRF from fMRI time series. Although such techniques have been validated non-invasively using simulated and empirical fMRI data, thorough invasive validation using simultaneous electrophysiological recordings, the gold standard, has been elusive. This report addresses this gap in the literature by comparing HRFs derived from invasive intracranial electroencephalogram recordings with HRFs estimated from simultaneously acquired fMRI data in six epileptic rats. We found that the HRF shape parameters (HRF amplitude, latency and width) were not significantly different (>0.05) between ground truth and estimated HRFs. In the single pathological region, the HRF width was marginally significantly different (=0.03). Our study provides preliminary invasive validation for the efficacy of the HRF estimation technique in reliably estimating the HRF non-invasively from rs-fMRI data directly. This has a notable impact on rs-fMRI connectivity studies, and we recommend that HRF deconvolution be performed to minimize HRF variability and improve connectivity estimates.
静息态功能磁共振成像（rs-fMRI）是一种广受欢迎的技术，它丰富了我们对脑和脊髓功能的理解，包括不同区域如何进行交流（连通性）。但功能磁共振成像是对神经活动的一种间接测量，捕捉的是血液动力学。血液动力学响应函数（HRF）是未测量的神经活动与测量的功能磁共振成像时间序列之间的接口。HRF在不同脑区和个体之间存在差异，并且会受到非神经因素的调节。忽略这种HRF的变异性会导致功能连接（FC）估计出现误差。因此，从rs-fMRI数据中可靠地估计HRF至关重要。已经出现了一些稳健的技术来从功能磁共振成像时间序列中估计HRF。尽管这些技术已通过模拟和实证功能磁共振成像数据进行了非侵入性验证，但使用同步电生理记录这一黄金标准进行的全面侵入性验证却难以实现。本报告通过比较六只癫痫大鼠颅内脑电图记录得出的HRF与同时采集的功能磁共振成像数据估计的HRF，填补了文献中的这一空白。我们发现，真实HRF与估计的HRF之间的HRF形状参数（HRF幅度、潜伏期和宽度）没有显著差异（>0.05）。在单个病理区域，HRF宽度略有显著差异（=0.03）。我们的研究为HRF估计技术从rs-fMRI数据中直接非侵入性可靠估计HRF的有效性提供了初步的侵入性验证。这对rs-fMRI连通性研究有显著影响，我们建议进行HRF反卷积以最小化HRF变异性并改善连通性估计。
