PDF(Pubmed)
Abstract：
Functional Near Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are commonly employed neuroimaging methods in developmental neuroscience. Since they offer complementary strengths and their simultaneous recording is relatively easy, combining them is highly desirable. However, to date, very few infant studies have been conducted with NIRS-EEG, partly because analyzing and interpreting multimodal data is challenging. In this work, we propose a framework to carry out a multivariate pattern analysis that uses an NIRS-EEG feature matrix, obtained by selecting EEG trials presented within larger NIRS blocks, and combining the corresponding features. Importantly, this classifier is intended to be sensitive enough to apply to individual-level, and not group-level data. We tested the classifier on NIRS-EEG data acquired from five newborn infants who were listening to human speech and monkey vocalizations. We evaluated how accurately the model classified stimuli when applied to EEG data alone, NIRS data alone, or combined NIRS-EEG data. For three out of five infants, the classifier achieved high and statistically significant accuracy when using features from the NIRS data alone, but even higher accuracy when using combined EEG and NIRS data, particularly from both hemoglobin components. For the other two infants, accuracies were lower overall, but for one of them the highest accuracy was still achieved when using combined EEG and NIRS data with both hemoglobin components. We discuss how classification based on joint NIRS-EEG data could be modified to fit the needs of different experimental paradigms and needs.
摘要：
功能近红外光谱（fNIRS）和脑电图（EEG）是发育神经科学中常用的神经成像方法。由于它们具有互补的优势，并且同时录制相对容易，将它们结合起来是非常可取的。然而,到目前为止,很少有婴儿研究用NIRS-EEG进行，部分原因是分析和解释多模态数据具有挑战性。在这项工作中,我们提出了一个使用NIRS-EEG特征矩阵进行多元模式分析的框架，通过选择在较大的NIRS块中呈现的EEG试验获得，并结合相应的特征。重要的是,这个分类器的目的是足够敏感，以适用于个人水平，而不是组级数据。我们在从五个正在听人类语音和猴子发声的新生婴儿获得的NIRS-EEG数据上测试了分类器。我们评估了模型在单独应用于EEG数据时对刺激进行分类的准确性，仅NIRS数据，或合并NIRS-EEG数据。对于五分之三的婴儿来说，当单独使用NIRS数据的特征时，分类器获得了很高的统计上显著的准确性，但是当使用合并的EEG和NIRS数据时，精度更高，特别是两种血红蛋白成分。对于另外两个婴儿，总体准确度较低，但是对于其中之一,当使用具有两种血红蛋白成分的联合EEG和NIRS数据时,仍然可以达到最高的准确性.我们讨论了如何修改基于联合NIRS-EEG数据的分类以适应不同实验范式和需求的需求。
