Diamond Solomon Gilbert, Perdue Katherine L, Boas David A
Thayer School of Engineering at Dartmouth, 8000 Cummings Hall, Hanover, NH 03755, USA.
Math Biosci. 2009 Aug;220(2):102-17. doi: 10.1016/j.mbs.2009.05.002. Epub 2009 May 13.
Functional neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) can be used to isolate an evoked response to a stimulus from significant background physiological fluctuations. Data analysis approaches typically use averaging or linear regression to remove this physiological baseline with varying degrees of success. Biophysical model-based analysis of the functional hemodynamic response has also been advanced previously with the Balloon and Windkessel models. In the present work, a biophysical model of systemic and cerebral circulation and gas exchange is applied to resting state NIRS neuroimaging data from 10 human subjects. The model further includes dynamic cerebral autoregulation, which modulates the cerebral arteriole compliance to control cerebral blood flow. This biophysical model allows for prediction, from noninvasive blood pressure measurements, of the background hemodynamic fluctuations in the systemic and cerebral circulations. Significantly higher correlations with the NIRS data were found using the biophysical model predictions compared to blood pressure regression and compared to transfer function analysis (multifactor ANOVA, p<0.0001). This finding supports the further development and use of biophysical models for removing baseline activity in functional neuroimaging analysis. Future extensions of this work could model changes in cerebrovascular physiology that occur during development, aging, and disease.
功能神经成像技术，如功能磁共振成像（fMRI）和近红外光谱（NIRS），可用于从显著的背景生理波动中分离出对刺激的诱发反应。数据分析方法通常使用平均或线性回归来去除这种生理基线，但成功程度各不相同。基于生物物理模型的功能血流动力学反应分析此前也已通过球囊模型和Windkessel模型取得进展。在本研究中，一个全身和脑循环及气体交换的生物物理模型被应用于10名人类受试者的静息态NIRS神经成像数据。该模型还进一步包括动态脑自动调节，它调节脑小动脉顺应性以控制脑血流量。这个生物物理模型允许从无创血压测量中预测全身和脑循环中的背景血流动力学波动。与血压回归和传递函数分析相比（多因素方差分析，p<0.0001），使用生物物理模型预测发现与NIRS数据的相关性显著更高。这一发现支持了生物物理模型在功能神经成像分析中去除基线活动方面的进一步发展和应用。这项工作未来的扩展可以模拟在发育、衰老和疾病过程中发生的脑血管生理变化。
