| Project/Area Number |
17590904
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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 |
Neurology
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| Research Institution | Meiji University of Oriental Medicine |
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Principal Investigator |
HIGUCHI Toshihiro Meiji University of Oriental Medicine, School of Acupuncture and Moxibustion, Professor, 鍼灸学部, 教授 (80218700)
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| Co-Investigator(Kenkyū-buntansha) |
TANAKA Chuzo Meiji University of Oriental Medicine, School of Health Science and Medical Care, Professor, 保健医療学部, 教授 (80163541)
UMEDA Masahiro Meiji University of Oriental Medicine, School of Acupuncture and Moxibustion, Associate professor, 鍼灸学部, 助教授 (60223608)
AOKI Ichio National Institute of Radiological Science, Molecular Imaging Center, team leader, 分子イメージング研究センター, 上席研究員 (10319519)
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| Project Period (FY) |
2005 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2006: ¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 2005: ¥2,100,000 (Direct Cost: ¥2,100,000)
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| Keywords | fmrl / functional connectivity / aging / independent component analysis / brain function / DTI / ファンクショナルコネクティビティ / トラクトグラフィ / 独立成分分析法 / 構造方程式モデル / マンガン |
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| Research Abstract |
Although considerable progress has been made in delineating structural MRI of the aging brain, relatively little progress has been made in evaluating its anatomical and functional relationship between brain areas. Some investigators in the earlier studies have hypothesized some type of connectivity reduction model in aged brain. In this study, we have planed to develop analysis system to investigate such connection changes in the pathophysiology by using functional connectivity MRI (fcMRI) and diffusion tensor MRI (DTI). Accordingly, we have employed a novel and integrative methods such as Independent Component Analysis (ICA) and mutual information. In the results of validation stage, we found that filtering out of physiological signals (MR signal change related to cardiac cycle, respiration cycle and volumes) in pre-processing is extremely important for de-noising of fcMRI analysis. In the application stage, we have applied this method to detect the pathophysiological changes of functional connectivity in stroke patients that have certain damaged region in the brain. We found that functional connectivity in ICA results in stroke patients is relatively decreased than normal volunteer. However there are substantial fluctuation of the physiological signals in stroke patients, so we keep working further improvement in pre-processing stage of this analysis system.
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