| Project/Area Number |
09044148
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Biomedical engineering/Biological material science
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
KOSUGI Yukio Tokyo Institute of Technology, Frontier Collaborative Research Center, Professor, フロンティア創造共同研究センター, 教授 (30108237)
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| Co-Investigator(Kenkyū-buntansha) |
KANEYAMA Keisuke Interdisciplinary Graduate School of Science and Engineering, Research Associate, 大学院・総合理工学研究科, 助手 (40242309)
WU Dongsheng イリノイ大学, 電気・計測工学科, 助手
HE Bin イリノイ大学, 電気・計測工学科, 助教授
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| Project Period (FY) |
1997 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥4,100,000 (Direct Cost: ¥4,100,000)
Fiscal Year 1999: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 1998: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 1997: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | cortical activity / neural networks / inverse problems / dipole localization / PET / metabolic imaging / regularization / 脳電位 / ネットワークインバージョン / 協調項 / SPECT画像 / パーキンソン病 / 動的正則化 |
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| Research Abstract |
Artificial neural networks can be exploited to solve inverse problems arising from the visualization of neural activities in the brain. In this research, we made use of the network inversion techniques for solving inverse problems with special attention directed towards electroencephalographic dipole localization and the improvement of positron emission tomography. In **r regluarized network inversion technique, for stabilizing the solution, we explicitly include the a priori knowledge by adding penalty terms to the energy function and/or build this knowledge into the architecture of the multi-layered neural networks that are to be used as an inverse problem solver.
In the electroencephalogram analysis, the consensus term added to the energy function facilitated S-dipole localization for visually evoked potentials. Effectiveness of our regularization technique is shown in improving the positron emission tomogrgphic images and for generating metabolic images of the brain, under the constraints given by the a priori knowledge inherent to the measurement systems and physiological rules.
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