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Article: Robust spinal cord resting-state fMRI using independent component analysis-based nuisance regression noise reduction

TitleRobust spinal cord resting-state fMRI using independent component analysis-based nuisance regression noise reduction
Authors
Keywordsspinal cord
nuisance regression
physiological noise
resting-state fMRI
robustness
Issue Date2018
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/1053-1807/
Citation
Journal of Magnetic Resonance Imaging, 2018, v. 48 n. 5, p. 1421-1431 How to Cite?
AbstractBACKGROUND: Physiological noise reduction plays a critical role in spinal cord (SC) resting-state fMRI (rsfMRI). PURPOSE: To reduce physiological noise and increase the robustness of SC rsfMRI by using an independent component analysis (ICA)-based nuisance regression (ICANR) method. STUDY TYPE: Retrospective. SUBJECTS: Ten healthy subjects (female/male = 4/6, age = 27 ± 3 years, range 24-34 years). FIELD STRENGTH/SEQUENCE: 3T/gradient-echo echo planar imaging (EPI). ASSESSMENT: We used three alternative methods (no regression [Nil], conventional region of interest [ROI]-based noise reduction method without ICA [ROI-based], and correction of structured noise using spatial independent component analysis [CORSICA]) to compare with the performance of ICANR. Reduction of the influence of physiological noise on the SC and the reproducibility of rsfMRI analysis after noise reduction were examined. The correlation coefficient (CC) was calculated to assess the influence of physiological noise. Reproducibility was calculated by intraclass correlation (ICC). STATISTICAL TESTS: Results from different methods were compared by one-way analysis of variance (ANOVA) with post-hoc analysis. RESULTS: No significant difference in cerebrospinal fluid (CSF) pulsation influence or tissue motion influence were found (P = 0.223 in CSF, P = 0.2461 in tissue motion) in the ROI-based (CSF: 0.122 ± 0.020; tissue motion: 0.112 ± 0.015), and Nil (CSF: 0.134 ± 0.026; tissue motion: 0.124 ± 0.019). CORSICA showed a significantly stronger influence of CSF pulsation and tissue motion (CSF: 0.166 ± 0.045, P = 0.048; tissue motion: 0.160 ± 0.032, P = 0.048) than Nil. ICANR showed a significantly weaker influence of CSF pulsation and tissue motion (CSF: 0.076 ± 0.007, P = 0.0003; tissue motion: 0.081 ± 0.014, P = 0.0182) than Nil. The ICC values in the Nil, ROI-based, CORSICA, and ICANR were 0.669, 0.645, 0.561, and 0.766, respectively. Data Conclusion: ICANR more effectively reduced physiological noise from both tissue motion and CSF pulsation than three alternative methods. ICANR increases the robustness of SC rsfMRI in comparison with the other three methods.
Persistent Identifierhttp://hdl.handle.net/10722/259119
ISSN
2017 Impact Factor: 3.612
2015 SCImago Journal Rankings: 1.683
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHu, Y-
dc.contributor.authorJin, R-
dc.contributor.authorLi, G-
dc.contributor.authorLuk, KDK-
dc.contributor.authorWu, EX-
dc.date.accessioned2018-09-03T04:01:46Z-
dc.date.available2018-09-03T04:01:46Z-
dc.date.issued2018-
dc.identifier.citationJournal of Magnetic Resonance Imaging, 2018, v. 48 n. 5, p. 1421-1431-
dc.identifier.issn1053-1807-
dc.identifier.urihttp://hdl.handle.net/10722/259119-
dc.description.abstractBACKGROUND: Physiological noise reduction plays a critical role in spinal cord (SC) resting-state fMRI (rsfMRI). PURPOSE: To reduce physiological noise and increase the robustness of SC rsfMRI by using an independent component analysis (ICA)-based nuisance regression (ICANR) method. STUDY TYPE: Retrospective. SUBJECTS: Ten healthy subjects (female/male = 4/6, age = 27 ± 3 years, range 24-34 years). FIELD STRENGTH/SEQUENCE: 3T/gradient-echo echo planar imaging (EPI). ASSESSMENT: We used three alternative methods (no regression [Nil], conventional region of interest [ROI]-based noise reduction method without ICA [ROI-based], and correction of structured noise using spatial independent component analysis [CORSICA]) to compare with the performance of ICANR. Reduction of the influence of physiological noise on the SC and the reproducibility of rsfMRI analysis after noise reduction were examined. The correlation coefficient (CC) was calculated to assess the influence of physiological noise. Reproducibility was calculated by intraclass correlation (ICC). STATISTICAL TESTS: Results from different methods were compared by one-way analysis of variance (ANOVA) with post-hoc analysis. RESULTS: No significant difference in cerebrospinal fluid (CSF) pulsation influence or tissue motion influence were found (P = 0.223 in CSF, P = 0.2461 in tissue motion) in the ROI-based (CSF: 0.122 ± 0.020; tissue motion: 0.112 ± 0.015), and Nil (CSF: 0.134 ± 0.026; tissue motion: 0.124 ± 0.019). CORSICA showed a significantly stronger influence of CSF pulsation and tissue motion (CSF: 0.166 ± 0.045, P = 0.048; tissue motion: 0.160 ± 0.032, P = 0.048) than Nil. ICANR showed a significantly weaker influence of CSF pulsation and tissue motion (CSF: 0.076 ± 0.007, P = 0.0003; tissue motion: 0.081 ± 0.014, P = 0.0182) than Nil. The ICC values in the Nil, ROI-based, CORSICA, and ICANR were 0.669, 0.645, 0.561, and 0.766, respectively. Data Conclusion: ICANR more effectively reduced physiological noise from both tissue motion and CSF pulsation than three alternative methods. ICANR increases the robustness of SC rsfMRI in comparison with the other three methods.-
dc.languageeng-
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.interscience.wiley.com/jpages/1053-1807/-
dc.relation.ispartofJournal of Magnetic Resonance Imaging-
dc.rightsThis is the peer reviewed version of the following article: Journal of Magnetic Resonance Imaging, 2018, v. 48 n. 5, p. 1421-1431, which has been published in final form at https://doi.org/10.1002/jmri.26048. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.subjectspinal cord-
dc.subjectnuisance regression-
dc.subjectphysiological noise-
dc.subjectresting-state fMRI-
dc.subjectrobustness-
dc.titleRobust spinal cord resting-state fMRI using independent component analysis-based nuisance regression noise reduction-
dc.typeArticle-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.emailLuk, KDK: hrmoldk@HKUCC-COM.hku.hk-
dc.identifier.emailWu, EX: ewu@eee.hku.hk-
dc.identifier.authorityHu, Y=rp00432-
dc.identifier.authorityLuk, KDK=rp00333-
dc.identifier.authorityWu, EX=rp00193-
dc.description.naturepostprint-
dc.identifier.doi10.1002/jmri.26048-
dc.identifier.pmid29659087-
dc.identifier.hkuros289698-
dc.identifier.volume48-
dc.identifier.issue5-
dc.identifier.spage1421-
dc.identifier.epage1431-
dc.identifier.isiWOS:000448081300028-
dc.publisher.placeUnited States-

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