{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T12:18:51Z","timestamp":1791807531351,"version":"4.3.4"},"reference-count":35,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD).<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To evaluate the sensitivity of FW\u2010DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW\u2010DTI indices to predict amyloid\u2010beta (A\u03b2) positivity in mild cognitive impairment (MCI) subtypes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>Thirty\u2010eight A\u03b2\u2010negative cognitively healthy (CH) controls (68.74\u2009\u00b1\u20098.28\u2009years old, 55% female), 15 A\u03b2\u2010negative MCI patients (MCI\u2010n) (68.87\u2009\u00b1\u20098.83\u2009years old, 60% female), 29 A\u03b2\u2010positive MCI patients (MCI\u2010p) (73.03\u2009\u00b1\u20097.05\u2009years old, 52% female), and 29 A\u03b2\u2010positive AD patients (72.93\u2009\u00b1\u20099.11\u2009years old, 55% female).<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>3.0T; DTI, <jats:italic>T<\/jats:italic><jats:sub>1<\/jats:sub>\u2010weighted, <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub>\u2010weighted, <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub> star\u2010weighted angiography, and A\u03b2 PET (<jats:sup>18<\/jats:sup>F\u2010florbetaben or <jats:sup>11<\/jats:sup>C\u2010PIB).<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>FW\u2010corrected and standard diffusion indices were analyzed using trace\u2010based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM).<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Chi\u2010squared test, one\u2010way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A <jats:italic>P<\/jats:italic> value &lt;0.05 was considered statistically significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Compared with CH\/MCI\u2010n\/MCI\u2010p, AD showed significant change in tissue compartment indices of FW\u2010DTI. No difference was found in the FW index among pair\u2010wise group comparisons (the minimum FWE\u2010corrected <jats:italic>P<\/jats:italic>\u2009=\u20090.114). There was a significant association between FW\u2010DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC\u2009=\u20090.91), and the classifying accuracy of FW\u2010DTI was all over 89.89%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>FW\u2010DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW\u2010DTI indices has good accuracy and could help early diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>4<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29189","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T18:56:19Z","timestamp":1702666579000},"page":"1458-1469","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Free Water <scp>MR<\/scp> Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease"],"prefix":"10.1002","volume":"60","author":[{"given":"Xuan","family":"Sun","sequence":"first","affiliation":[{"name":"Medical School of Chinese PLA  Beijing China"},{"name":"Department of Geriatric Neurology The Second Medical Centre, Chinese PLA General Hospital  Beijing China"},{"name":"National Clinical Research Center of Geriatric Diseases Chinese PLA General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence Beijing University of Posts and Telecommunications  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si\u2010Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"Medical School of Chinese PLA  Beijing China"},{"name":"Department of Geriatric Neurology The Second Medical Centre, Chinese PLA General Hospital  Beijing China"},{"name":"National Clinical Research Center of Geriatric Diseases Chinese PLA General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, The First Medical Centre Chinese PLA 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