{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:58:45Z","timestamp":1790791125260,"version":"4.1.0"},"posted":{"date-parts":[[2026,9,27]]},"group-title":"Neuroscience","reference-count":40,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T00:00:00Z","timestamp":1790467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.biorxiv.org\/about\/FAQ#license"}],"funder":[{"name":"National Institutes of Health","award":["R01NS136227"],"award-info":[{"award-number":["R01NS136227"]}]},{"name":"National Institutes of Health","award":["R24NS104098"],"award-info":[{"award-number":["R24NS104098"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>There is increasing demand for quantitative measurements in neuroimaging research, including absolute quantification of regional myelin density. Fast whole-brain macromolecular proton fraction (MPF) imaging enables acquisition and reconstruction of quantitative maps of MPF, a biomarker of myelin, and has been used in many research applications. To date, no standard protocols have been published for parcellating MPF maps. FreeSurfer is a widely used software package for automated brain parcellation. It is designed for use with high-resolution T1-weighted anatomical images, typically acquired with an MPRAGE sequence. MPF maps inherently provide high contrast between white and gray matter and can potentially be used as input images for parcellation, enabling simultaneous quantitative tissue characterization and volumetric assessment. However, it is unclear how the use of MPF maps as input images with FreeSurfer software affect reliability and repeatability of regional MPF and volume estimates. We evaluated three FreeSurfer parcellation workflows for this purpose. The workflows differed in the amount of neuroimaging data required and computational intensity. MPF and MPRAGE data were acquired in two separate sessions for 11 adults. Reliability was evaluated using mean relative differences in estimates, Bland-Altman analysis, and intra-class correlation coefficients (ICCs). Repeatability was assessed using within-subject coefficients of variation (CVws). All three workflows produced similarly high within-subject repeatability across scans. However, reliability in regional MPF estimates was lower in gray matter than in white matter for workflows that used MPF maps as input. Workflows that use MPF maps as FreeSurfer input may be adequate for estimating mean MPF in GM and WM of cortical parcels and in subcortical regions. However, the MPRAGE-based workflow is recommended when reliable individual-level estimates or volume estimates are required.<\/jats:p>","DOI":"10.64898\/2026.09.21.753351","type":"posted-content","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T04:45:15Z","timestamp":1790570715000},"source":"Crossref","is-referenced-by-count":0,"title":["Optimizing an Automated Processing Pipeline for Regional Macromolecular Proton Fraction and Volume Measurements in the Brain"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4080-5212","authenticated-orcid":false,"given":"Neva M.","family":"Corrigan","sequence":"first","affiliation":[{"name":"Institute for Learning & Brain Sciences, University of Washington, Seattle WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel S.","family":"Hippe","sequence":"additional","affiliation":[{"name":"Fred Hutch Cancer Center, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasily L.","family":"Yarnykh","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Washington, Seattle WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026093010351359000_2026.09.21.753351v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.09.025"},{"key":"2026093010351359000_2026.09.21.753351v1.2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"issue":"8476","key":"2026093010351359000_2026.09.21.753351v1.3","first-page":"307","article-title":"Statistical methods for assessing agreement between two methods of clinical measurement","volume":"1","year":"1986","journal-title":"The Lancet"},{"key":"2026093010351359000_2026.09.21.753351v1.4","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-018-0438-y"},{"key":"2026093010351359000_2026.09.21.753351v1.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117678"},{"key":"2026093010351359000_2026.09.21.753351v1.6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119641"},{"key":"2026093010351359000_2026.09.21.753351v1.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.021"},{"key":"2026093010351359000_2026.09.21.753351v1.8","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A8545"},{"key":"2026093010351359000_2026.09.21.753351v1.9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.01.021"},{"key":"2026093010351359000_2026.09.21.753351v1.10","unstructured":"FreeSurfer. 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