| Project/Area Number |
16KT0134
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 特設分野 |
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| Research Field |
Mathematical Sciences in Search of New Cooperation
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| Research Institution | Oita University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
矢尾 育子 関西学院大学, 理工学部, 教授 (60399681)
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| Project Period (FY) |
2016-07-19 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 質量分析イメージング / 多様体学習 / 計算トポロジー / 機械学習 / 神経変性疾患 / バイオマーカー / 多変量データ解析 |
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| Outline of Final Research Achievements |
Recently, the methodologies of imaging mass spectrometry (IMS) are receiving attention in the various fields of science and technology. The data in association with IMS is composed of a huge number of mass spectra according to spatial observation points, which brings difficulty in applications of conventional statistical analysis.
In this study, we proposed an approach based on manifold learning for analyzing the IMS data of mice brains. To this end, we introduced a distance measure based on Fisher metric between two persistent diagrams (PDs) associated with mass spectra. Then, we applied the proposed approach to slices of the mice brains and showed that our prosed approach is useful for visualizing a set of the mass spectra as well-separated islands of points in the low-dimensional space, where each island reflects a specific character of chemical species in the brain. We also considered how our results can be interpreted in terms of neurophysiology.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究成果を発展させることで、遺伝子改変に伴う脳内環境の変化を検出するためのデータ解析法を提案し、多数のサンプルを用いた大規模な解析を通じて、パーキンソン病やレビー小体型認知症などの神経変性疾患に関与するバイオ・マーカーの候補の発見に繋がる。
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