Functional connectivity MRI quality control procedures in CONN
<jats:p>Quality control (QC) for functional connectivity magnetic resonance imaging (FC-MRI) is critical to ensure the validity of neuroimaging studies. Noise confounds are common in MRI data and, if not accounted for, may introduce biases in functional measures affecting the validity, replica...
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| Format: | Article |
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Frontiers Media SA
2023
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| Online Access: | https://hdl.handle.net/1721.1/150448 |
| _version_ | 1811097175222910976 |
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| author | Morfini, Francesca Whitfield-Gabrieli, Susan Nieto-Castañón, Alfonso |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Morfini, Francesca Whitfield-Gabrieli, Susan Nieto-Castañón, Alfonso |
| author_sort | Morfini, Francesca |
| collection | MIT |
| description | <jats:p>Quality control (QC) for functional connectivity magnetic resonance imaging (FC-MRI) is critical to ensure the validity of neuroimaging studies. Noise confounds are common in MRI data and, if not accounted for, may introduce biases in functional measures affecting the validity, replicability, and interpretation of FC-MRI study results. Although FC-MRI analysis rests on the assumption of adequate data processing, QC is underutilized and not systematically reported. Here, we describe a quality control pipeline for the visual and automated evaluation of MRI data implemented as part of the CONN toolbox. We analyzed publicly available resting state MRI data (<jats:italic>N</jats:italic> = 139 from 7 MRI sites) from the FMRI Open QC Project. Preprocessing steps included realignment, unwarp, normalization, segmentation, outlier identification, and smoothing. Data denoising was performed based on the combination of scrubbing, motion regression, and aCompCor – a principal component characterization of noise from minimally eroded masks of white matter and of cerebrospinal fluid tissues. Participant-level QC procedures included visual inspection of raw-level data and of representative images after each preprocessing step for each run, as well as the computation of automated descriptive QC measures such as average framewise displacement, average global signal change, prevalence of outlier scans, MNI to anatomical and functional overlap, anatomical to functional overlap, residual BOLD timeseries variability, effective degrees of freedom, and global correlation strength. Dataset-level QC procedures included the evaluation of inter-subject variability in the distributions of edge connectivity in a 1,000-node graph (FC distribution displays), and the estimation of residual associations across participants between functional connectivity strength and potential noise indicators such as participant’s head motion and prevalence of outlier scans (QC-FC analyses). QC procedures are demonstrated on the reference dataset with an emphasis on visualization, and general recommendations for best practices are discussed in the context of functional connectivity and other fMRI analysis. We hope this work contributes toward the dissemination and standardization of QC testing performance reporting among peers and in scientific journals.</jats:p> |
| first_indexed | 2024-09-23T16:55:32Z |
| format | Article |
| id | mit-1721.1/150448 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:55:32Z |
| publishDate | 2023 |
| publisher | Frontiers Media SA |
| record_format | dspace |
| spelling | mit-1721.1/1504482024-01-31T21:16:46Z Functional connectivity MRI quality control procedures in CONN Morfini, Francesca Whitfield-Gabrieli, Susan Nieto-Castañón, Alfonso Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences McGovern Institute for Brain Research at MIT General Neuroscience <jats:p>Quality control (QC) for functional connectivity magnetic resonance imaging (FC-MRI) is critical to ensure the validity of neuroimaging studies. Noise confounds are common in MRI data and, if not accounted for, may introduce biases in functional measures affecting the validity, replicability, and interpretation of FC-MRI study results. Although FC-MRI analysis rests on the assumption of adequate data processing, QC is underutilized and not systematically reported. Here, we describe a quality control pipeline for the visual and automated evaluation of MRI data implemented as part of the CONN toolbox. We analyzed publicly available resting state MRI data (<jats:italic>N</jats:italic> = 139 from 7 MRI sites) from the FMRI Open QC Project. Preprocessing steps included realignment, unwarp, normalization, segmentation, outlier identification, and smoothing. Data denoising was performed based on the combination of scrubbing, motion regression, and aCompCor – a principal component characterization of noise from minimally eroded masks of white matter and of cerebrospinal fluid tissues. Participant-level QC procedures included visual inspection of raw-level data and of representative images after each preprocessing step for each run, as well as the computation of automated descriptive QC measures such as average framewise displacement, average global signal change, prevalence of outlier scans, MNI to anatomical and functional overlap, anatomical to functional overlap, residual BOLD timeseries variability, effective degrees of freedom, and global correlation strength. Dataset-level QC procedures included the evaluation of inter-subject variability in the distributions of edge connectivity in a 1,000-node graph (FC distribution displays), and the estimation of residual associations across participants between functional connectivity strength and potential noise indicators such as participant’s head motion and prevalence of outlier scans (QC-FC analyses). QC procedures are demonstrated on the reference dataset with an emphasis on visualization, and general recommendations for best practices are discussed in the context of functional connectivity and other fMRI analysis. We hope this work contributes toward the dissemination and standardization of QC testing performance reporting among peers and in scientific journals.</jats:p> 2023-04-06T18:38:26Z 2023-04-06T18:38:26Z 2023-03-23 Article http://purl.org/eprint/type/JournalArticle 1662-453X https://hdl.handle.net/1721.1/150448 Morfini, Francesca, Whitfield-Gabrieli, Susan and Nieto-Castañón, Alfonso. 2023. "Functional connectivity MRI quality control procedures in CONN." 17. 10.3389/fnins.2023.1092125 Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Frontiers Media SA Frontiers |
| spellingShingle | General Neuroscience Morfini, Francesca Whitfield-Gabrieli, Susan Nieto-Castañón, Alfonso Functional connectivity MRI quality control procedures in CONN |
| title | Functional connectivity MRI quality control procedures in CONN |
| title_full | Functional connectivity MRI quality control procedures in CONN |
| title_fullStr | Functional connectivity MRI quality control procedures in CONN |
| title_full_unstemmed | Functional connectivity MRI quality control procedures in CONN |
| title_short | Functional connectivity MRI quality control procedures in CONN |
| title_sort | functional connectivity mri quality control procedures in conn |
| topic | General Neuroscience |
| url | https://hdl.handle.net/1721.1/150448 |
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