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Quantifying differences in fMRI preprocessing pipelines via OGRE (One-step General Registration and Extraction)
Volumetric preprocessing methods continue to enjoy great popularity in the analysis of functional MRI (fMRI) data. Among these methods, the software packages FSL (FMRIB, Oxford, UK) and FreeSurfer (LCN, Charlestown, MA) are omnipresent throughout the field. However, it remains unknown what advantage...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10541115/ https://www.ncbi.nlm.nih.gov/pubmed/37781580 http://dx.doi.org/10.1101/2023.09.19.558290 |
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author | McAvoy, Mark P. Liu, Lei Zhou, Ruiwen Philip, Benjamin A. |
author_facet | McAvoy, Mark P. Liu, Lei Zhou, Ruiwen Philip, Benjamin A. |
author_sort | McAvoy, Mark P. |
collection | PubMed |
description | Volumetric preprocessing methods continue to enjoy great popularity in the analysis of functional MRI (fMRI) data. Among these methods, the software packages FSL (FMRIB, Oxford, UK) and FreeSurfer (LCN, Charlestown, MA) are omnipresent throughout the field. However, it remains unknown what advantages an integrated FSL+FreeSurfer preprocessing approach might provide over FSL alone. Here we developed the One-step General Registration and Extraction (OGRE) pipeline to combine FreeSurfer and FSL tools for brain extraction and registration, for FSL volumetric analysis of fMRI data. We compared preprocessing approaches in a dataset wherein adult human volunteers (N=26) performed a precision drawing task during fMRI scanning. OGRE’s preprocessing, compared to traditional FSL preprocessing, led to lower inter-individual variability across the brain, more precise brain extraction, and greater detected activation in sensorimotor areas contralateral to movement. This demonstrates that the introduction of FreeSurfer tools via OGRE preprocessing can improve fMRI data analysis, in the context of FSL’s volumetric analysis approach. The OGRE pipeline provides a turnkey method to integrate FreeSurfer-based brain extraction and registration with FSL analysis of task fMRI data. |
format | Online Article Text |
id | pubmed-10541115 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-105411152023-10-01 Quantifying differences in fMRI preprocessing pipelines via OGRE (One-step General Registration and Extraction) McAvoy, Mark P. Liu, Lei Zhou, Ruiwen Philip, Benjamin A. bioRxiv Article Volumetric preprocessing methods continue to enjoy great popularity in the analysis of functional MRI (fMRI) data. Among these methods, the software packages FSL (FMRIB, Oxford, UK) and FreeSurfer (LCN, Charlestown, MA) are omnipresent throughout the field. However, it remains unknown what advantages an integrated FSL+FreeSurfer preprocessing approach might provide over FSL alone. Here we developed the One-step General Registration and Extraction (OGRE) pipeline to combine FreeSurfer and FSL tools for brain extraction and registration, for FSL volumetric analysis of fMRI data. We compared preprocessing approaches in a dataset wherein adult human volunteers (N=26) performed a precision drawing task during fMRI scanning. OGRE’s preprocessing, compared to traditional FSL preprocessing, led to lower inter-individual variability across the brain, more precise brain extraction, and greater detected activation in sensorimotor areas contralateral to movement. This demonstrates that the introduction of FreeSurfer tools via OGRE preprocessing can improve fMRI data analysis, in the context of FSL’s volumetric analysis approach. The OGRE pipeline provides a turnkey method to integrate FreeSurfer-based brain extraction and registration with FSL analysis of task fMRI data. Cold Spring Harbor Laboratory 2023-10-07 /pmc/articles/PMC10541115/ /pubmed/37781580 http://dx.doi.org/10.1101/2023.09.19.558290 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article McAvoy, Mark P. Liu, Lei Zhou, Ruiwen Philip, Benjamin A. Quantifying differences in fMRI preprocessing pipelines via OGRE (One-step General Registration and Extraction) |
title | Quantifying differences in fMRI preprocessing pipelines via OGRE (One-step General Registration and Extraction) |
title_full | Quantifying differences in fMRI preprocessing pipelines via OGRE (One-step General Registration and Extraction) |
title_fullStr | Quantifying differences in fMRI preprocessing pipelines via OGRE (One-step General Registration and Extraction) |
title_full_unstemmed | Quantifying differences in fMRI preprocessing pipelines via OGRE (One-step General Registration and Extraction) |
title_short | Quantifying differences in fMRI preprocessing pipelines via OGRE (One-step General Registration and Extraction) |
title_sort | quantifying differences in fmri preprocessing pipelines via ogre (one-step general registration and extraction) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10541115/ https://www.ncbi.nlm.nih.gov/pubmed/37781580 http://dx.doi.org/10.1101/2023.09.19.558290 |
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