{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T23:01:38Z","timestamp":1774047698888,"version":"3.50.1"},"reference-count":40,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2018,10,9]],"date-time":"2018-10-09T00:00:00Z","timestamp":1539043200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81471659"],"award-info":[{"award-number":["81471659"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81630046"],"award-info":[{"award-number":["81630046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2019,4]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Prediabetes is an intermediate state in which blood glucose is higher than normal but does not meet the diagnostic criteria for diabetes. Diffusion tensor imaging (DTI) is an MRI method that can sensitively detect microscopic lesions in the white matter of the brain, but most previous DTI studies only focused on patients with diabetes, and little is known about prediabetes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To investigate the changes in the microstructure of brain white matter in prediabetes patients using DTI and trace\u2010based spatial statistics (TBSS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Prospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>Sixty subjects (30 patients with prediabetes and 30 healthy volunteers) were enrolled.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>3.0T\/DTI\u2010MRI sequence with single\u2010shot echo\u2010planar imaging sequence (SE\u2010EPI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>DTI data were collected and analyzed using the TBSS method in the FMRIB software library.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>DTI using a two\u2010sample <jats:italic>t<\/jats:italic>\u2010test. Pearson correlation analysis was performed on DTI values and neuropsychology scale results (mini\u2010mental state examination [MMSE], Montreal cognitive assessment [MoCA], self\u2010rating anxiety scale [SAS], and self\u2010rating depression scale [SDS])<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Compared with the control group, the fractional anisotropy (FA) values in the right part of the corpus callosum body (bCC) (<jats:italic>P<\/jats:italic>\u2009=\u20090.035), the right superior longitudinal fasciculus (SLF.R) (<jats:italic>P<\/jats:italic>\u2009=\u20090.047), and the left superior longitudinal fasciculus (SLF.L) in the prediabetic group were reduced (<jats:italic>P<\/jats:italic>\u2009=\u20090.040).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>DTI as a noninvasive technique can assess early changes in the white matter microarchitecture of patients with prediabetes.<\/jats:p><jats:p><jats:bold>Level of Evidence<\/jats:bold>: 2<\/jats:p><jats:p><jats:bold>Technical Efficacy<\/jats:bold>: Stage 2<\/jats:p><jats:p>J. 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Imaging 2019;49:1105\u20131112.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.26290","type":"journal-article","created":{"date-parts":[[2018,10,10]],"date-time":"2018-10-10T05:16:28Z","timestamp":1539148588000},"page":"1105-1112","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Diffusion tensor imaging of white matter in patients with prediabetes by trace\u2010based spatial statistics"],"prefix":"10.1002","volume":"49","author":[{"given":"Minjie","family":"Liang","sequence":"first","affiliation":[{"name":"Medical Imaging Center, First Affiliated Hospital Jinan University  Guangzhou Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyi","family":"Cai","sequence":"additional","affiliation":[{"name":"Medical Imaging Center, First Affiliated Hospital Jinan University  Guangzhou Guangdong 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