Near infrared spectroscopy of deep brain structures in the neonate

新生儿深部脑结构的近红外光谱

• 批准号: RGPIN-2019-05128
• 负责人: Duerden, Emma
• 金额: $ 2.04万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737306
• 关键词: Near; infrared; spectroscopy; deep; brain

Functional neuroimaging technologies that are sensitive to haemodynamic changes in the brain including functional near infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) have advanced rapidly, providing insight into adult brain functional connectivity. However, using fNIRS and fMRI to study newborn brain development has been limited. The proposed set of experiments seeks to advance current fNIRS analytic methods, using fMRI as a validation method, in order to use fNIRS to study functional brain connectivity in newborns. fNIRS is an emerging neonatal brain-imaging technique that measures haemodynamic changes at the cortical surface. fMRI is a preferred method for measuring neonatal brain hemodynamics due to its ability to detect T2* signal throughout the brain. While fNIRS offers a number of advantages for assessing neonatal populations including its high temporal resolution (~100ms), low-cost and portability, its use for the study of subcortical-cortical connectivity has been limited due the technical challenges of obtaining haemodynamic changes below the cortical surface. In order to localize haemodynamic changes in deep brain structures in the neonatal brain, a novel machine-learning algorithm is proposed to expand upon applications of fNIRS to the measurement of subcortical and cortical haemodynamics underlying emerging cognitive and motor systems in the newborn. To achieve the long-term goal of utilizing fNIRS for the study of neonatal subcortical-cortical connectivity, we need to (1) develop a prediction method to extrapolate subcortical haemodynamics; (2) validate the prediction model based on established methods of cortical-subcortical connectivity using fMRI; and (3) apply the prediction method to fNIRS neonatal haemodynamic data. The development of fNIRS systems will offer new insights into how early network connectivity involving specific brain systems contributes to cognitive maturation in particular domains. By providing a better understanding of how individual differences in brain maturation may predict alterations in brain connectivity, this knowledge may lay the foundation for more effective early-intervention teaching methods calibrated to critical periods in the development of specific functional brain systems.

功能性神经成像技术，包括功能性近红外光谱（fNIRS）和功能性磁共振成像（fMRI）的大脑中的血流动力学变化是敏感的，已经迅速发展，提供洞察成人大脑功能连接。然而，使用fNIRS和fMRI来研究新生儿脑发育受到限制。拟议的一组实验旨在推进当前的fNIRS分析方法，使用fMRI作为验证方法，以便使用fNIRS研究新生儿的功能性脑连接。fNIRS是一种新兴的新生儿脑成像技术，可测量皮质表面的血流动力学变化。fMRI是测量新生儿脑血流动力学的优选方法，因为其能够检测整个脑中的T2* 信号。虽然fNIRS在评估新生儿群体方面具有许多优势，包括其高时间分辨率（约100 ms）、低成本和便携性，但由于获得皮质表面以下血流动力学变化的技术挑战，其用于皮质下-皮质连接研究的用途受到限制。 为了本地化的新生儿大脑深部脑结构的血液动力学变化，提出了一种新的机器学习算法，以扩大应用fNIRS测量皮层下和皮层血流动力学的新生儿新兴的认知和运动系统。为了实现利用fNIRS研究新生儿皮层下-皮层连接的长期目标，我们需要（1）开发一种预测方法来外推皮层下血流动力学;（2）使用fMRI验证基于已建立的皮层-皮层下连接方法的预测模型;（3）将预测方法应用于fNIRS新生儿血流动力学数据。 fNIRS系统的发展将为涉及特定大脑系统的早期网络连接如何促进特定领域的认知成熟提供新的见解。通过更好地了解大脑成熟的个体差异如何预测大脑连接的变化，这些知识可以为更有效的早期干预教学方法奠定基础，这些方法可以校准到特定功能大脑系统发展的关键时期。