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    Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach

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    Author
    Adamson, C; Beare, R; Ball, G; Walterfang, M; Seal, M
    Date
    2018-12-01
    Source Title
    Brain and Behavior
    Publisher
    WILEY
    University of Melbourne Author/s
    Walterfang, Mark; Seal, Marc; Ball, Gareth
    Affiliation
    Psychiatry
    Paediatrics (RCH)
    Metadata
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    Document Type
    Journal Article
    Citations
    Adamson, C., Beare, R., Ball, G., Walterfang, M. & Seal, M. (2018). Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach. BRAIN AND BEHAVIOR, 8 (12), https://doi.org/10.1002/brb3.1142.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/253621
    DOI
    10.1002/brb3.1142
    Abstract
    INTRODUCTION: Alzheimer's disease (AD) is the most common form of dementia. Finding biomarkers to prognosticate transition from mild cognitive impairment (MCI) to AD is important to clinical medicine. Promising imaging biomarkers of AD conversion identified so far include atrophy of the cerebral cortex and subcortical gray matter nuclei. METHODS: This study introduces thickness and bending angle of the corpus callosum as a putative white matter marker of MCI to AD conversion. The corpus callosum is computationally less demanding to segment automatically compared to more complicated structures and a subject can be processed in a few minutes. We aimed to demonstrate that callosal shape and thickness measures provide a simple, effective, and accurate prognostication tool in ADNI dataset. Using longitudinal datasets, we classified MCI subjects based on conversion to AD assessed via cognitive testing. We evaluated the classification accuracy of callosal shape features in comparison with the existing "gold standard" cortical thickness and subcortical gray matter volume measures. RESULTS: The callosal thickness measures were less accurate in classifying conversion status by cognitive scores compared to gray matter measures for AD. CONCLUSIONS: While this paper presented a negative result, this method may be more suitable for a disease of the white matter.

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