{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T10:03:13Z","timestamp":1789639393797,"version":"4.0.0"},"reference-count":49,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG054159"],"award-info":[{"award-number":["R01AG054159"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Diffusion MRI (dMRI) methods including diffusion tensor imaging (DTI) and more advanced models including diffusional kurtosis imaging (DKI) and fiber ball white matter modeling (FBWM) probe various aspects of white matter (WM) microstructure. However, the associations between these variables and myelin content remain largely unknown. We examined this issue using ViSTa myelin water imaging (MWI), which estimates the proportion of tissue water associated with myelin using the apparent myelin water fraction (aMWF) and examined correlations with dMRI variables across heterogeneous deep WM regions. Four healthy adults underwent anatomical MRI, multishell dMRI, and ViSTa\u2010MWI. We computed DTI, DKI, FBWM, and aMWF metrics within a binarized deep WM atlas and callosal subregions (the genu and splenium) and evaluated associations between dMRI and aMWF using voxel\u2010wise block\u2010permutation Spearman correlations. Across WM regions, axonal water fraction (\n                    <jats:italic>\u03c1<\/jats:italic>\n                    \u2009=\u20090.68), mean kurtosis (\n                    <jats:italic>\u03c1<\/jats:italic>\n                    \u2009=\u20090.63), and radial kurtosis (\n                    <jats:italic>\u03c1<\/jats:italic>\n                    \u2009=\u20090.62) had stronger correlations with aMWF than conventional DTI metrics. Correlations for dMRI variables were consistently higher within the splenium (mean |\n                    <jats:italic>\u03c1<\/jats:italic>\n                    |\u2009=\u20090.68) compared to the genu (mean |\n                    <jats:italic>\u03c1<\/jats:italic>\n                    |\u2009=\u20090.56), reflecting stronger associations with aMWF in an earlier\u2010myelinating region with denser axonal packing and reduced fiber dispersion. DTI, DKI, and FBWM variables all correspond with aMWF. Select DKI and FBWM variables demonstrated the strongest associations with aMWF and were more sensitive than conventional DTI metrics. Correlations for all metric families were stronger within the splenium compared to the genu, presumably reflecting the high density of myelination in this region. These results help to clarify the biophysical interpretation of dMRI microstructural parameters by determining how strongly they are influenced by myelin content. This preliminary examination helps to clarify the roles of dMRI variables for future studies of WM integrity and reinforces the use of DKI and FBWM.\n                  <\/jats:p>","DOI":"10.1002\/nbm.70377","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T13:08:39Z","timestamp":1787836119000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Association of Apparent Myelin Water Fraction With Diffusional Kurtosis and Biophysical Modeling Parameters"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8262-804X","authenticated-orcid":false,"given":"Hunter G.","family":"Moss","sequence":"first","affiliation":[{"name":"Center for Biomedical Imaging Medical University of South Carolina  Charleston South Carolina USA"},{"name":"Department of Neuroscience Medical University of South Carolina  Charleston South Carolina USA"},{"name":"Department of Pediatrics Medical University of South Carolina  Charleston South Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael A.","family":"Sugarman","sequence":"additional","affiliation":[{"name":"Department of Neurology Medical University of South Carolina  Charleston South Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9485-5434","authenticated-orcid":false,"given":"Jongho","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Seoul National University  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5027-6422","authenticated-orcid":false,"given":"Andrew A.","family":"Chen","sequence":"additional","affiliation":[{"name":"Center for Biomedical Imaging Medical University of South Carolina  Charleston South Carolina USA"},{"name":"Department of Public Health Sciences Medical University of South Carolina  Charleston South Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3219-4287","authenticated-orcid":false,"given":"Jens H.","family":"Jensen","sequence":"additional","affiliation":[{"name":"Center for Biomedical Imaging Medical University of South Carolina  Charleston South Carolina USA"},{"name":"Department of Neuroscience Medical University of South Carolina  Charleston South Carolina USA"},{"name":"Department of Radiology Medical University of South Carolina  Charleston South Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8294-7809","authenticated-orcid":false,"given":"Andreana","family":"Benitez","sequence":"additional","affiliation":[{"name":"Center for Biomedical Imaging Medical University of South Carolina  Charleston South Carolina USA"},{"name":"Department of Neurology Medical University of South Carolina  Charleston South Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0006-3495(94)80775-1"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.1518"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.09.049"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.04.025"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.06.006"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.03.072"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2005.03.042"},{"key":"e_1_2_10_9_1","first-page":"28","article-title":"Design and Validation of Diffusion MRI Models of White Matter","volume":"5","author":"Jelescu I. 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