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Opt. Express"],"published-print":{"date-parts":[[2022,3,1]]},"abstract":"<jats:p>\n                    White-matter injury leads to severe functional loss in many\n\t\t\t\t\tneurological diseases. Myelin staining on histological samples is the\n\t\t\t\t\tmost common technique to investigate white-matter fibers. However,\n\t\t\t\t\ttissue processing and sectioning may affect the reliability of 3D\n\t\t\t\t\tvolumetric assessments. The purpose of this study was to propose an\n\t\t\t\t\tapproach that enables myelin fibers to be mapped in the whole rodent\n\t\t\t\t\tbrain with microscopic resolution and without the need for strenuous\n\t\t\t\t\tstaining. With this aim, we coupled in-line (propagation-based) X-ray\n\t\t\t\t\tphase-contrast tomography (XPCT) to ethanol-induced brain sample\n\t\t\t\t\tdehydration. We here provide the proof-of-concept that this approach\n\t\t\t\t\tenhances myelinated axons in rodent and human brain tissue. In\n\t\t\t\t\taddition, we demonstrated that white-matter injuries could be detected\n\t\t\t\t\tand quantified with this approach, using three animal models: ischemic\n\t\t\t\t\tstroke, premature birth and multiple sclerosis. Furthermore, in\n\t\t\t\t\tanalogy to diffusion tensor imaging (DTI), we retrieved fiber\n\t\t\t\t\tdirections and DTI-like diffusion metrics from our XPCT data to\n\t\t\t\t\tquantitatively characterize white-matter microstructure. Finally, we\n\t\t\t\t\tshowed that this non-destructive approach was compatible with\n\t\t\t\t\tsubsequent complementary brain sample analysis by conventional\n\t\t\t\t\thistology. In-line XPCT might thus become a novel gold-standard for\n\t\t\t\t\tinvestigating white-matter injury in the intact brain. This is Part I\n\t\t\t\t\tof a series of two articles reporting the value of in-line XPCT for\n\t\t\t\t\tvirtual histology of the brain; Part II shows how in-line XPCT enables\n\t\t\t\t\tthe whole-brain 3D morphometric analysis of amyloid-\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n                        <mml:mrow class=\"MJX-TeXAtom-ORD\">\n                          <mml:mi>\n                            \u03b2\n                            \n                          <\/mml:mi>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    (A\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n                        <mml:mrow class=\"MJX-TeXAtom-ORD\">\n                          <mml:mi>\n                            \u03b2\n                            \n                          <\/mml:mi>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ) plaques.\n                  <\/jats:p>","DOI":"10.1364\/boe.438832","type":"journal-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T12:00:11Z","timestamp":1636113611000},"page":"1620","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Brain virtual histology with X-ray phase-contrast tomography Part I: whole-brain myelin mapping in white-matter injury models"],"prefix":"10.1364","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2282-6976","authenticated-orcid":true,"given":"Matthieu","family":"Chourrout","sequence":"first","affiliation":[{"name":"Lyon Neuroscience Research 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