Just like a skilled detective analyzing hidden clues, scientists have developed NeoRS, a state-of-the-art toolkit to unravel the mysteries of the neonatal brain. Using resting state functional MRI (rsfMRI), researchers can delve into the intricate web of connections in a baby’s brain and gain insights into cerebral development. But here’s the catch: neonatal brains are unique, with different sizes and myelination patterns compared to adult brains. That’s why existing adult pipelines don’t work for them. Enter NeoRS! This brand-new rsfMRI pipeline, optimized for neonates, carefully processes data to reduce artifacts that could muddy the results. With its specialized techniques like image registration, skull stripping, tissue segmentation, and motion correction, NeoRS ensures accurate interpretation and analysis of neonatal brain connectivity. And you won’t believe it – NeoRS goes beyond preprocessing! It also offers cutting-edge features like seed-to-seed and seed-to-voxel correlations, allowing scientists to investigate specific brain networks in these tiny subjects. Results from the Baby Connectome Project using NeoRS showed strong agreement with previous studies. So, if you want to join the quest to understand the wonders of the neonatal brain, check out NeoRS on GitHub. It’s time to unlock the secrets and pave the way for better neonatal care!

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.

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