Voxel-Based Morphometryに基づくAlzheimer病の診断 : 解析法の影響 Alzheimer's Disease : Diagnosis by Different Methods of Voxel-Based Morphometry

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Abstract

Purpose : The purpose of this study was to determine the optimalcomputationaloptions in voxel-based morphometry (VBM) for discrimination between Alzheimer's disease (AD) patients and healthy control (HC) subjects. Materials and Methods : Structuralmagnetic resonance images of 24 AD patients and 26 HC subjects were analyzed using VBM to determine brain regions with significant gray matter (GM) loss due to AD. The VBM analyses were performed with 4 different computationaloptions : gray matter concentration (GMC) analysis with and without global normalization, and gray matter volume (GMV) analysis, with and without global normalization. Statistical maps calculated with the 4 computational options were obtained at 3 different P-value thresholds (P < 0. 001, P < 0. 0005, and P < 0. 0001, uncorrected for multiple comparisons), yielding a total of 12 sets of maps, from which regions-of-interest (ROI) were generated for subsequent analyses of performance in terms of discrimination between AD patients and HC subjects as based on the mean value of either the GMC or GMV within the ROI for each of the 12 maps. Discrimination performance was evaluated by means of comparing the area-under-the-curve derived from the receiver-operating characteristic analysis as well as on the accuracy of the discrimination. Results : Discrimination based on GMC analysis resulted in better performance than that based on GMV analysis. The best discrimination performance was achieved with GMC analysis either with or without proportionalgl obalnormal ization. Conclusion : The findings suggested that GMC-based VBM is better suited than GMV-based VBM for discrimination between AD patients and HC subjects.

Journal

  • 福岡医学雑誌

    福岡医学雑誌 103(3), 59-69, 2012-03-25

    福岡医学会

Codes

  • NII Article ID (NAID)
    40019326251
  • NII NACSIS-CAT ID (NCID)
    AN00215478
  • Text Lang
    ENG
  • Article Type
    journal article
  • Journal Type
    大学紀要
  • ISSN
    0016-254X
  • NDL Article ID
    023780542
  • NDL Call No.
    Z19-86
  • Data Source
    NDL  IR 
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