Centre for Brain and Cognitive Development, Birkbeck College, University of London, United Kingdom.
Centre for Brain and Cognitive Development, Birkbeck College, University of London, United Kingdom.
Neuroimage. 2018 Jul 15;175:413-424. doi: 10.1016/j.neuroimage.2018.04.022. Epub 2018 Apr 12.
Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions. DCM has recently been applied to adult fNIRS, but not to infants. The present paper provides a proof-of-principle for the application of this method to infant fNIRS data and a demonstration of the robustness of this method using a simultaneously recorded fMRI-fNIRS single case study, thereby allowing the use of this technique in future infant studies. fMRI and fNIRS were simultaneously recorded from a 6-month-old sleeping infant, who was presented with auditory stimuli in a block design. Both fMRI and fNIRS data were preprocessed using SPM, and analysed using a general linear model approach. The main challenges that adapting DCM for fNIRS infant data posed included: (i) the import of the structural image of the participant for spatial pre-processing, (ii) the spatial registration of the optodes on the structural image of the infant, (iii) calculation of an accurate 3-layer segmentation of the structural image, (iv) creation of a high-density mesh as well as (v) the estimation of the NIRS optical sensitivity functions. To assess our results, we compared the values obtained for variational Free Energy (F), Bayesian Model Selection (BMS) and Bayesian Model Average (BMA) with the same set of possible models applied to both the fMRI and fNIRS datasets. We found high correspondence in F, BMS, and BMA between fMRI and fNIRS data, therefore showing for the first time high reliability of DCM applied to infant fNIRS data. This work opens new avenues for future research on effective connectivity in infancy by contributing a data analysis pipeline and guidance for applying DCM to infant fNIRS data.
从婴儿期到儿童期，追踪大脑的连通性是一个日益受到关注的研究领域，近红外光谱（fNIRS）为研究婴儿大脑提供了一种理想的方法，因为它紧凑、安全且对运动具有鲁棒性。然而，与 fMRI 可用的方法相比，fNIRS 的数据分析方法仍在发展中。动态因果建模（DCM）是一种为 fMRI 数据开发的高级连通性技术，旨在估计大脑区域之间的耦合以及实验条件变化如何对此进行调制。DCM 最近已应用于成人 fNIRS，但不适用于婴儿。本文提供了将该方法应用于婴儿 fNIRS 数据的原理证明，并通过同时记录 fMRI-fNIRS 的单个案例研究证明了该方法的稳健性，从而允许在未来的婴儿研究中使用该技术。fMRI 和 fNIRS 同时从 6 个月大的睡眠婴儿中记录，该婴儿以块设计接受听觉刺激。fMRI 和 fNIRS 数据均使用 SPM 进行预处理，并使用广义线性模型方法进行分析。为了使 DCM 适应婴儿的 fNIRS 数据，需要克服以下主要挑战：（i）将参与者的结构图像导入空间预处理，（ii）将光极在婴儿结构图像上的空间配准，（iii）准确计算结构图像的 3 层分割，（iv）创建高密度网格，以及（v）估计 NIRS 光学灵敏度函数。为了评估我们的结果，我们将 fNIRS 数据的变分自由能（F）、贝叶斯模型选择（BMS）和贝叶斯模型平均（BMA）的值与应用于 fMRI 和 fNIRS 数据集的相同模型集进行了比较。我们发现 fMRI 和 fNIRS 数据的 F、BMS 和 BMA 之间具有高度一致性，因此首次证明了 DCM 应用于婴儿 fNIRS 数据的高度可靠性。这项工作通过为应用 DCM 到婴儿 fNIRS 数据提供数据分析管道和指导，为婴儿期有效连通性的未来研究开辟了新途径。
