Reliability and comparability of human brain structural covariance networks
DC Element | Wert | Sprache |
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dc.contributor.author | Carmon, Jona | |
dc.contributor.author | Heege, Jil | |
dc.contributor.author | Necus, Joe H. | |
dc.contributor.author | Owen, Thomas W. | |
dc.contributor.author | Pipa, Gordon | |
dc.contributor.author | Kaiser, Marcus | |
dc.contributor.author | Taylor, Peter N. | |
dc.contributor.author | Wang, Yujiang | |
dc.date.accessioned | 2021-12-23T16:22:06Z | - |
dc.date.available | 2021-12-23T16:22:06Z | - |
dc.date.issued | 2020 | |
dc.identifier.issn | 10538119 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/14159 | - |
dc.description.abstract | Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n >> 30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled. | |
dc.description.sponsorship | NIH Institutes and CentersUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [1U54MH091657]; McDonnell Center for Systems Neuroscience at Washington University; UK Biotechnology and Biological Sciences Research CouncilUK Research & Innovation (UKRI)Biotechnology and Biological Sciences Research Council (BBSRC) [BB/H008217/1]; UK Medical Research CouncilUK Research & Innovation (UKRI)Medical Research Council UK (MRC); University of Cambridge, UKUniversity of Cambridge; Wellcome TrustWellcome TrustEuropean Commission [102037, 208940/Z/17/Z, 210109/Z/18/Z]; Reece Foundation; Engineering and Physical Sciences Research CouncilUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [NS/A000026/1, EP/N031962/1]; Medical Research CouncilUK Research & Innovation (UKRI)Medical Research Council UK (MRC)European Commission [MR/T004347/1]; Guangci Professorship Program of Ruijin Hospital (Shanghai Jiao Tong Univ.); EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/N031962/1] Funding Source: UKRI; MRCUK Research & Innovation (UKRI)Medical Research Council UK (MRC) [MR/T004347/1] Funding Source: UKRI; Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. Another part of the data for this project was provided by the Cambridge Centre for Ageing and Neuroscience (CamCAN). CamCAN funding was provided by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), together with support from the UK Medical Research Council and University of Cambridge, UK.; We thank members of the CNNP lab (www.cnnp-lab.com) for discussions on the analysis and manuscript. P.N.T. and Y.W. gratefully acknowledge funding from Wellcome Trust (208940/Z/17/Z and 210109/Z/18/Z). J.H.N. was supported by the Reece Foundation. M.K. was supported by Wellcome Trust (102037), Engineering and Physical Sciences Research Council (NS/A000026/1, EP/N031962/1), Medical Research Council (MR/T004347/1), and the Guangci Professorship Program of Ruijin Hospital (Shanghai Jiao Tong Univ.). The authors declare no conflict of interest. The funders played no role in the design of the study. | |
dc.language.iso | en | |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | |
dc.relation.ispartof | NEUROIMAGE | |
dc.subject | AUTISM | |
dc.subject | CORTICAL THICKNESS | |
dc.subject | FUNCTIONAL CONNECTIVITY | |
dc.subject | HEALTH | |
dc.subject | INTERCORRELATIONS | |
dc.subject | MRI | |
dc.subject | Neuroimaging | |
dc.subject | Neurosciences | |
dc.subject | Neurosciences & Neurology | |
dc.subject | ORGANIZATION | |
dc.subject | Radiology, Nuclear Medicine & Medical Imaging | |
dc.subject | REGIONS | |
dc.subject | SURFACE-AREA | |
dc.title | Reliability and comparability of human brain structural covariance networks | |
dc.type | journal article | |
dc.identifier.doi | 10.1016/j.neuroimage.2020.117104 | |
dc.identifier.isi | ISI:000579184700053 | |
dc.description.volume | 220 | |
dc.contributor.orcid | 0000-0002-4654-3110 | |
dc.contributor.orcid | 0000-0003-3748-6473 | |
dc.contributor.orcid | 0000-0003-2144-9838 | |
dc.contributor.researcherid | A-7166-2008 | |
dc.contributor.researcherid | E-8927-2011 | |
dc.identifier.eissn | 10959572 | |
dc.publisher.place | 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA | |
dcterms.isPartOf.abbreviation | Neuroimage | |
dcterms.oaStatus | Green Submitted, gold | |
crisitem.author.dept | Institut für Kognitionswissenschaft | - |
crisitem.author.deptid | institute28 | - |
crisitem.author.orcid | 0000-0002-3416-2652 | - |
crisitem.author.parentorg | FB 08 - Humanwissenschaften | - |
crisitem.author.grandparentorg | Universität Osnabrück | - |
crisitem.author.netid | PiGo340 | - |
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geprüft am 19.05.2024