Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/25636
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dc.contributor.authorMito, Remika-
dc.contributor.authorDhollander, Thijs-
dc.contributor.authorXia, Ying-
dc.contributor.authorRaffelt, David-
dc.contributor.authorSalvado, Olivier-
dc.contributor.authorChurilov, Leonid-
dc.contributor.authorRowe, Christopher C-
dc.contributor.authorBrodtmann, Amy-
dc.contributor.authorVillemagne, Victor L-
dc.contributor.authorConnelly, Alan-
dc.date2020-10-26-
dc.date.accessioned2021-01-13T03:00:23Z-
dc.date.available2021-01-13T03:00:23Z-
dc.date.issued2020-
dc.identifier.citationNeuroImage. Clinical 2020; 28: 102479en
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/25636-
dc.description.abstractWhite matter hyperintensities (WMH) are regions of high signal intensity typically identified on fluid attenuated inversion recovery (FLAIR). Although commonly observed in elderly individuals, they are more prevalent in Alzheimer's disease (AD) patients. Given that WMH appear relatively homogeneous on FLAIR, they are commonly partitioned into location- or distance-based classes when investigating their relevance to disease. Since pathology indicates that such lesions are often heterogeneous, probing their microstructure in vivo may provide greater insight than relying on such arbitrary classification schemes. In this study, we investigated WMH in vivo using an advanced diffusion MRI method known as single-shell 3-tissue constrained spherical deconvolution (SS3T-CSD), which models white matter microstructure while accounting for grey matter and CSF compartments. Diffusion MRI data and FLAIR images were obtained from AD (n = 48) and healthy elderly control (n = 94) subjects. WMH were automatically segmented, and classified: (1) as either periventricular or deep; or (2) into three distance-based contours from the ventricles. The 3-tissue profile of WMH enabled their characterisation in terms of white matter-, grey matter-, and fluid-like characteristics of the diffusion signal. Our SS3T-CSD findings revealed substantial heterogeneity in the 3-tissue profile of WMH, both within lesions and across the various classes. Moreover, this heterogeneity information indicated that the use of different commonly used WMH classification schemes can result in different disease-based conclusions. We conclude that future studies of WMH in AD would benefit from inclusion of microstructural information when characterising lesions, which we demonstrate can be performed in vivo using SS3T-CSD.en
dc.language.isoeng
dc.subject3-tissueen
dc.subjectAlzheimer’s diseaseen
dc.subjectDiffusion MRIen
dc.subjectHeterogeneityen
dc.subjectWhite matter hyperintensitiesen
dc.titleIn vivo microstructural heterogeneity of white matter lesions in healthy elderly and Alzheimer's disease participants using tissue compositional analysis of diffusion MRI data.en
dc.typeJournal Articleen
dc.identifier.journaltitleNeuroImage. Clinicalen
dc.identifier.affiliationMolecular Imaging and Therapyen
dc.identifier.affiliationFlorey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australiaen
dc.identifier.affiliationCSIRO, Health & Biosecurity, The Australian eHealth Research Centre, Brisbane, Queensland, Australiaen
dc.identifier.affiliationCSIRO Data61, Sydney, New South Wales, Australiaen
dc.identifier.affiliationEastern Clinical Research Unit, Monash University, Box Hill Hospital, Melbourne, Victoria, Australiaen
dc.identifier.affiliationFlorey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australiaen
dc.identifier.affiliationFlorey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australiaen
dc.identifier.affiliationDevelopmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australiaen
dc.identifier.affiliationMedicine (University of Melbourne)en
dc.identifier.affiliationFlorey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australiaen
dc.identifier.affiliationCSIRO, Health & Biosecurity, The Australian eHealth Research Centre, Brisbane, Queensland, Australiaen
dc.identifier.doi10.1016/j.nicl.2020.102479en
dc.type.contentTexten
dc.identifier.pubmedid33395971
local.name.researcherBrodtmann, Amy
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
crisitem.author.deptMedicine (University of Melbourne)-
crisitem.author.deptThe Florey Institute of Neuroscience and Mental Health-
crisitem.author.deptMolecular Imaging and Therapy-
crisitem.author.deptThe Florey Institute of Neuroscience and Mental Health-
crisitem.author.deptMolecular Imaging and Therapy-
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