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dc.contributor.authorBano, Wajiha
dc.contributor.authorPulli, Elmo
dc.contributor.authorCantonas, Lucia
dc.contributor.authorSorsa, Aino
dc.contributor.authorHämäläinen, Jarmo
dc.contributor.authorKarlsson, Hasse
dc.contributor.authorKarlsson, Linnea
dc.contributor.authorSaukko, Ekaterina
dc.contributor.authorSainio, Teija
dc.contributor.authorPeuna, Arttu
dc.contributor.authorKorja, Riikka
dc.contributor.authorAro, Mikko
dc.contributor.authorLeppänen, Paavo H.T.
dc.contributor.authorTuulari, Jetro J
dc.contributor.authorMerisaari, Harri
dc.date.accessioned2024-06-27T11:51:13Z
dc.date.available2024-06-27T11:51:13Z
dc.date.issued2024
dc.identifier.citationBano, W., Pulli, E., Cantonas, L., Sorsa, A., Hämäläinen, J., Karlsson, H., Karlsson, L., Saukko, E., Sainio, T., Peuna, A., Korja, R., Aro, M., Leppänen, P. H., Tuulari, J. J., & Merisaari, H. (2024). Implementing ABCD study ® MRI sequences for multi-site cohort studies : practical guide to necessary steps, preprocessing methods, and challenges. <i>MethodsX</i>, <i>12</i>, Article 102789. <a href="https://doi.org/10.1016/j.mex.2024.102789" target="_blank">https://doi.org/10.1016/j.mex.2024.102789</a>
dc.identifier.otherCONVID_216098467
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/96189
dc.description.abstractLarge multi-site studies that combine magnetic resonance imaging (MRI) data across research sites present exceptional opportunities to advance neuroscience research. However, scanner or site variability and non-standardised image acquisition protocols, data processing and analysis pipelines can adversely affect the reliability and repeatability of MRI derived brain measures. We implemented a standardised MRI protocol based on that used in the Adolescent Brain Cognition Development (ABCD)® study in two sites, and across four MRI scanners. Twice repeated measurements of a single healthy volunteer were obtained in two sites and in four 3T MRI scanners (vendors: Siemens, Philips, and GE). Imaging data included anatomical scans (T1 weighted, T2 weighted), diffusion weighted imaging (DWI) and resting state functional MRI (rs-fMRI). Standardised containerized pipelines were utilised to pre-process the data and different image quality metrics and test-retest variability of different brain metrics were evaluated. The implementation of the MRI protocols was possible with minor adjustments in acquisition (e.g. repetition time (TR), higher b-values) and exporting (DICOM formats) of images due to different technical performance of the scanners. This study provides practical insights into the implementation of standardised sequences and data processing for multisite studies, showcase the benefits of containerized preprocessing tools, and highlights the need for careful optimisation of multisite image acquisition.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesMethodsX
dc.rightsCC BY 4.0
dc.subject.otherneuroimaging
dc.subject.otherMRI
dc.subject.otherMulti-Center studies
dc.subject.otherreproducibility
dc.subject.othermulti-scanner
dc.subject.othermulti-vendor
dc.titleImplementing ABCD study ® MRI sequences for multi-site cohort studies : practical guide to necessary steps, preprocessing methods, and challenges
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202406275031
dc.contributor.laitosKasvatustieteiden laitosfi
dc.contributor.laitosPsykologian laitosfi
dc.contributor.laitosDepartment of Educationen
dc.contributor.laitosDepartment of Psychologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn2215-0161
dc.relation.volume12
dc.type.versionpublishedVersion
dc.rights.copyright© 2024 Published by Elsevier B.V.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber346119
dc.subject.ysoaivotutkimus
dc.subject.ysomittausmenetelmät
dc.subject.ysoluotettavuus
dc.subject.ysoneurotieteet
dc.subject.ysotoistettavuus
dc.subject.ysomagneettikuvaus
dc.subject.ysokuvantaminen
dc.subject.ysotutkimusmenetelmät
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p23705
jyx.subject.urihttp://www.yso.fi/onto/yso/p20083
jyx.subject.urihttp://www.yso.fi/onto/yso/p1629
jyx.subject.urihttp://www.yso.fi/onto/yso/p18502
jyx.subject.urihttp://www.yso.fi/onto/yso/p26295
jyx.subject.urihttp://www.yso.fi/onto/yso/p12131
jyx.subject.urihttp://www.yso.fi/onto/yso/p3532
jyx.subject.urihttp://www.yso.fi/onto/yso/p415
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.mex.2024.102789
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramCentre of Excellence, AoFen
jyx.fundingprogramHuippuyksikkörahoitus, SAfi
jyx.fundinginformationThe research was funded by The Centre of Excellence for Learning Dynamics and Intervention Research (InterLearn CoE) in the Academy of Finland's Center of Excellence Programme (2022-2029) (grants 346119, 346119, 346121).”
dc.type.okmA1


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