PMID- 35784189
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 1662-5196 (Print)
IS  - 1662-5196 (Electronic)
IS  - 1662-5196 (Linking)
VI  - 16
DP  - 2022
TI  - NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline.
PG  - 843114
LID - 10.3389/fninf.2022.843114 [doi]
LID - 843114
AB  - 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.
CI  - Copyright (c) 2022 Enguix, Kenley, Luck, Cohen-Adad and Lodygensky.
FAU - Enguix, Vicente
AU  - Enguix V
AD  - Department of Pediatrics, CHU Sainte-Justine, University of Montreal, Montreal, 
      QC, Canada.
AD  - NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, 
      Montreal, QC, Canada.
AD  - Canadian Neonatal Brain Platform, Montreal, QC, Canada.
FAU - Kenley, Jeanette
AU  - Kenley J
AD  - Washington University School of Medicine, St. Louis, MO, United States.
FAU - Luck, David
AU  - Luck D
AD  - Department of Pediatrics, CHU Sainte-Justine, University of Montreal, Montreal, 
      QC, Canada.
AD  - Canadian Neonatal Brain Platform, Montreal, QC, Canada.
FAU - Cohen-Adad, Julien
AU  - Cohen-Adad J
AD  - Department of Pediatrics, CHU Sainte-Justine, University of Montreal, Montreal, 
      QC, Canada.
AD  - NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, 
      Montreal, QC, Canada.
AD  - Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, QC, 
      Canada.
AD  - Mila - Quebec AI Institute, Montreal, QC, Canada.
FAU - Lodygensky, Gregory Anton
AU  - Lodygensky GA
AD  - Department of Pediatrics, CHU Sainte-Justine, University of Montreal, Montreal, 
      QC, Canada.
AD  - Canadian Neonatal Brain Platform, Montreal, QC, Canada.
LA  - eng
PT  - Journal Article
DEP - 20220617
PL  - Switzerland
TA  - Front Neuroinform
JT  - Frontiers in neuroinformatics
JID - 101477957
PMC - PMC9247272
OTO - NOTNLM
OT  - fMRI
OT  - neonates
OT  - pipeline
OT  - preprocessing
OT  - resting state
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/07/06 06:00
MHDA- 2022/07/06 06:01
PMCR- 2022/01/01
CRDT- 2022/07/05 10:21
PHST- 2021/12/24 00:00 [received]
PHST- 2022/05/27 00:00 [accepted]
PHST- 2022/07/05 10:21 [entrez]
PHST- 2022/07/06 06:00 [pubmed]
PHST- 2022/07/06 06:01 [medline]
PHST- 2022/01/01 00:00 [pmc-release]
AID - 10.3389/fninf.2022.843114 [doi]
PST - epublish
SO  - Front Neuroinform. 2022 Jun 17;16:843114. doi: 10.3389/fninf.2022.843114. 
      eCollection 2022.