Functional and Structural Optical Brain Imaging

功能近红外光谱(FNIRS)是一种新兴的非侵入性脑功能成像技术。这项技术是非侵入性和便携的，因此适用于儿童和学步儿童的研究，特别是那些患有神经发育障碍的儿童。FNIRS测量是基于与大脑活动相关的脑血流动力学水平(氧合血红蛋白和脱氧血红蛋白)的局部变化。由于生物组织在近红外波长(700-900 nm)的光吸收较低，近红外光可以穿透到足够深的地方，探测到1-3厘米深的皮质区域。如上所述，组织的近红外吸收光谱对主要组织发色团的浓度变化很敏感，如血红蛋白。因此，测量背向散射光的时间变化可以捕捉到最外层皮质的功能诱发变化，并可用于评估大脑功能。然而，NIRS信号的变化与脑内潜在的生理过程，如脑自身调节有关，需要加以解决。简而言之，脑自动调节机制维持动脉血压范围内的血流量，由于神经元的高代谢需求，它成为大脑功能的重要过程。因此，设计一种新的数据处理方法来丰富fNIR测量特征的信息内容，对于进一步研究大脑功能和发育至关重要。 在这项初步研究中，我们评估了功能性近红外光谱(FNIRS)在测量18至36个月大的幼儿前额叶皮质(PFC)脑血流动力学方面的应用。此外，我们分析了具有典型发育的幼儿和一组有发育风险的儿童在香草基准期内的fNIRS数据的组差异。分析包括根据左右前额叶皮质氧合血红蛋白的变化评估血流动力学激活，以及基于每个儿童大脑自动调节频率的氧饱和度变化的氧合变异性(OV)指数。在我们的先导研究中，我们发现，与TD组相比，AR参与者表现出更多的右侧激活(2(1)=4.2p=0.04，=0.38)和更高的左右激活差异(2(1)=12，p=0.001，=0.64)。此外，AR幼儿的总体OV指数在左侧(2(1)=9.45，p=0.002，=0.57)和右侧PFC(2(1)=9.15，p=.002，=0.56)中都显著低于典型发育儿童。值得一提的是，OV指数并不是脑自动调节的直接衡量标准。相反，它与与这一机制相关的频率有关，并用于量化这些频率中的振荡。虽然这项研究表明，由于早期语言延迟而有发育迟缓风险的幼儿，前额叶血流动力学的某些特征可能会有所不同，但需要在这个年龄段进行更多的研究，以澄清这些差异的特殊性。这些初步发现表明，在典型的幼儿和发育迟缓的儿童中使用fNIRS是可行的，这样做可以支持未来在更大样本中进行的研究。 我们探索了潜在的前额叶血流动力学生物标志物，通过使用多变量机器学习方法和引入一种新的与任务相关的血流动力学响应检测，然后启发式搜索最佳血流动力学特征集，来表征创伤性脑损伤(TBI)受试者。为了实现这一目标，记录了31名健康对照组和30名慢性脑外伤受试者在执行一项复杂任务时的血流动力学反应。为了确定最佳的血流动力学特征，我们考虑了11个特征及其组合来表征脑外伤受试者。我们利用机器学习分类算法来根据预测能力对所有可能的特征组合进行评分，从而研究了特征的重要性。 识别出的最佳特征元素的分类准确率、敏感度和特异度分别为85%、85%和84%。通过特征组合实现了对TBI主题分类的分类改进。它表明了多变量分析相对于通常使用的单变量分析的主要优势，表明在描述数据时，单独无关的特征在组合使用时可能会变得相关。我们还进行了时空分类，以确定前额叶皮质(PFC)内有助于区分脑外伤和健康受试者的区域。正如预期的那样，PFC内的Brodmann区(BA)10被隔离为健康受试者(不同于脑外伤受试者)在高复杂性任务中表现出主要血流动力学活动的区域。总体而言，我们的结果表明，根据PFC的血流动力学活动确定的时间和空间-时间特征是分类脑外伤患者的有希望的生物标志物。

Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive imaging technique to assess the brain function. The technique is non-invasive and portable and therefore applicable in studies of children and toddlers especially those with neurodevelopmental disorders. fNIRS measurements are based on the local changes in cerebral hemodynamic levels (oxy-hemoglobin and deoxy-hemoglobin) associated with brain activity. Due to the low optical absorption of biological tissues at NIR wavelengths (700-900 nm), NIR light can penetrate deep enough to probe the cortical regions up to 1-3 cm deep. As mentioned above, the NIR absorption spectrum of the tissue is sensitive to changes in the concentration of major tissue chromophores, such as hemoglobin. Therefore, measurements of temporal variations of backscattered light can capture functionally evoked changes in the outermost cortex and can be used to assess the brain function. However, there is a need to address the changes in NIRS signal in relation with underlying physiological processes in brain such as cerebral autoregulation. In short, the mechanism of cerebral autoregulation maintains the blood flow over the range of arterial blood pressure and due to high metabolic demand of neurons it becomes a vital process for a brain function. Devising a novel method of data processing to enrich informational content of measured characteristics from fNIRS is therefore crucial for further studies of brain function and development. In this pilot study, we evaluated the utility of functional near infrared spectroscopy (fNIRS) in measuring cerebral hemodynamics in the prefrontal cortex (PFC) in toddlers between 18 and 36 months of age. Further, we analyzed group differences in fNIRS data in toddlers with typical development and a group of children at risk for developmental during a vanilla baseline period. The analysis includes assessment of the hemodynamic activation, bases on changes in oxy-hemoglobin, in left and right prefrontal cortex, and the oxygenation variability (OV) index based on variability in oxygen saturation at frequencies attributed to cerebral autoregulation for each child. In our pilot study we found that AR participants showed preliminary evidence for both more right-sided activation (2 (1)=4.2, p=0.04, =0.38) and higher differences between left and right activation (2 (1)=12, p=0.001, =0.64) compared to the TD group. In addition, AR toddlers had a significant lower overall OV index compared to children with typical development in both the left (2 (1)=9.45, p=.002, =0.57) and right PFC (2 (1)=9.15, p=.002, =0.56). It is worth mentioning that the OV index is not a direct measure of cerebral autoregulation. Rather, it is associated with frequencies related to this mechanism and serves to quantify oscillations in those frequencies. While this study suggests that some features of prefrontal hemodynamics may vary in toddlers at risk for developmental delays due to early language delay, more research in this age group is required to clarify the specificity of these differences. These preliminary findings show the feasibility of using fNIRS in typical toddlers and those with delayed development and in doing so support future studies in larger samples. We have explored the potential prefrontal hemodynamic biomarkers to characterize subjects with Traumatic Brain Injury (TBI) by employing a multivariate machine learning approach and introducing a novel task-related hemodynamic response detection followed by a heuristic search for optimum set of hemodynamic features. To achieve this goal, the hemodynamic response from a group of 31 healthy controls and 30 chronic TBI subjects were recorded as they performed a complexity task. To determine the optimum hemodynamic features, we considered 11 features and their combinations in characterizing TBI subjects. We investigated the significance of the features by utilizing a machine learning classification algorithm to score all the possible combinations of features according to their predictive power. The identified optimum feature elements resulted in classification accuracy, sensitivity, and specificity of 85%, 85%, and 84%, respectively. Classification improvement was achieved for TBI subject classification through feature combination. It signified the major advantage of the multivariate analysis over the commonly used univariate analysis suggesting that the features that are individually irrelevant in characterizing the data may become relevant when used in combination. We also conducted a spatio-temporal classification to identify regions within the prefrontal cortex (PFC) that contribute in distinguishing between TBI and healthy subjects. As expected, Brodmann areas (BA) 10 within the PFC were isolated as the region that healthy subjects (unlike subjects with TBI), showed major hemodynamic activity in response to the High Complexity task. Overall, our results indicate that identified temporal and spatio-temporal features from PFC's hemodynamic activity are promising biomarkers in classifying subjects with TBI.