| Project/Area Number |
17591313
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Radiation science
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| Research Institution | National Institute for Longevity Sciences,NCGG |
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Principal Investigator |
ITO Kengo National Institute for Longevity Sciences,NCGG, National Center for Geriatrics and Gerontology Department of Brain Sciences and Molecular Imaging, Department Head (70184653)
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| Co-Investigator(Kenkyū-buntansha) |
KATO Takashi National Center for Geriatrics and Gerontology, Department of Brain Sciences and Molecular Imaging Section of Brain Pathophysiology, Section Chief (60242864)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥4,010,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥210,000)
Fiscal Year 2007: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2006: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 2005: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | Alzheimer's disease / early diagnosis / SPECT / numerical analysis / 数値解析 / PET / 神経心理検査 |
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| Research Abstract |
To achieve a higher diagnostic performance of brain perfusion SPECT for Alzheimer's disease(AD) a new method, voxel based weighted factor analysis(VBWFA), has been developed based on multivariate analysis and then clinically validated in mild cognitive impairment(MCI) cases. Methods : ^123I-IMP brain perfusion SPECT data were processed using 3D-SSP in 19 normal controls(NC) and 12 AD patients, and then the factor analysis of surface projection images with global mean normalization was performed. After extracting factor score images suggesting AD characteristics, factor scores were weighted to optimize the differentiation between NC and AD and the sum of squares was considered as the score of each case. Using these data as a basic data set, numerical diagnosis was performed in 25 MCI patients. The results of numerical diagnosis were compared with image diagnosis of SPECT by visual interpretation in all cases and with clinical outcomes in 10 cases. Results : Factor analysis of the data set of NC and AD revealed factor score images reflecting the changes of cerebral blood flow in the posterior cingulate gyri/precunei, parietotemporal association area, medial temporal area, and frontal base. Factor score for each MCI case was distributed randomly between NC and AD. The results of numerical diagnosis were in agreement with image diagnosis in 21 of the 25 cases and with clinical outcomes in 8 of the 10 cases. Conclusion : The VBWFA based on multivariate analysis can extract image characteristics of AD and would be useful for early diagnosis of AD at MCI stage.
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