Enguix Vicente, Kenley Jeanette, Luck David, Cohen-Adad Julien, Lodygensky Gregory Anton
Department of Pediatrics, CHU Sainte-Justine, University of Montreal, Montreal, QC, Canada.
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
Front Neuroinform. 2022 Jun 17;16:843114. doi: 10.3389/fninf.2022.843114. eCollection 2022.
Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can't be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.
静息态功能磁共振成像（rsfMRI）已被证明是研究大脑内在功能连接性并评估其在大脑发育过程中完整性的一种很有前景的工具。在新生儿中，功能磁共振成像仅限于极少数范式，rsfMRI被证明是探索脑网络区域间相互作用的一种相关工具。然而，为了识别静息态网络，需要对数据进行仔细处理，以减少影响结果解释的伪影。由于新生儿不配合的特性、大脑大小的差异以及与成年人相比因髓鞘形成导致的对比度反转，现有的成人处理流程无法处理新生儿数据，因为它们并不适用。因此，我们开发了NeoRS，一种用于新生儿的rsfMRI处理流程。该流程依赖于流行的神经成像工具（FSL、AFNI和SPM），并针对新生儿大脑进行了优化。主要处理步骤包括图像配准到图谱、颅骨剥离、组织分割、切片定时和头部运动校正以及对影响功能数据解释的混杂因素进行回归分析。为了解决新生儿脑成像的特殊性，我们特别关注了配准，包括新生儿图谱类型和参数，如大脑大小变化以及与成年人相比的对比度差异。此外，由于头部运动是处理新生儿rsfMRI数据时的一个主要问题，我们对其进行了仔细审查并优化了运动管理。该流程包括使用视觉评估检查点进行质量控制。为了评估NeoRS处理步骤的有效性，我们使用了来自婴儿连接组项目数据集的新生儿数据，总共10名新生儿。NeoRS设计用于多频段和单频段采集，适用于较小的数据集。NeoRS还包括流行的功能连接性分析功能，如种子到种子或种子到体素的相关性分析。对语言、默认模式、背侧注意、视觉、腹侧注意、运动和额顶网络进行了评估。拓扑结构发现，不同分析网络与之前发表的新生儿研究结果一致。NeoRS用Matlab编码，并允许并行计算以减少计算时间；它是开源的，可在GitHub（https://github.com/venguix/NeoRS）上获取。NeoRS能够对新生儿rsfMRI数据进行稳健的图像处理，并且可以很容易地针对不同数据集进行定制。
