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矿业脑区的连接通过稀疏逆协方差估计阿尔茨海默病研究
Mining Brain Region Connectivity for Alzheimer's Disease Study via Sparse Inverse Covariance Estimation |
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| 课程网址: |
http://videolectures.net/kdd09_ye_mbrcadssice/
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| 主讲教师: |
Jieping Ye
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| 开课单位: |
密歇根大学
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| 开课时间: |
2009-09-14
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| 课程语种: |
英语
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| 中文简介: |
阿尔茨海默病 (ad) 是老年患者最常见的老年痴呆症类型, 在生物医学研究中具有重要意义。最近的研究表明, ad 与大脑网络的结构变化密切相关, 即不同大脑区域之间的连接。连接模式将提供有用的基于图像的生物标志物, 以区分正常控制 (nc)、轻度认知障碍 (mci) 患者和 ad 患者。本文研究了用于识别不同大脑区域之间连通性的稀疏逆协方差估计技术。特别是提出了一种新的基于块坐标下降法的逆协方差矩阵直接估计算法。该算法的一个吸引人的特点是, 它允许将用户反馈 (例如, 先前的域知识) 纳入估计过程, 同时可以自动发现连接模式。将该算法应用于 232个 nc、mci 和 ad 主体的 fdg-pet 图像集合。实验结果表明, 该算法在揭示这些群体之间的脑区域连通性差异方面具有广阔的应用前景。
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| 课程简介: |
Effective diagnosis of Alzheimer's disease (AD), the most common type of dementia in elderly patients, is of primary importance in biomedical research. Recent studies have demonstrated that AD is closely related to the structure change of the brain network, i.e., the connectivity among different brain regions. The connectivity patterns will provide useful imaging-based biomarkers to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. In this paper, we investigate the sparse inverse covariance estimation technique for identifying the connectivity among different brain regions. In particular, a novel algorithm based on the block coordinate descent approach is proposed for the direct estimation of the inverse covariance matrix. One appealing feature of the proposed algorithm is that it allows the user feedback (e.g., prior domain knowledge) to be incorporated into the estimation process, while the connectivity patterns can be discovered automatically. We apply the proposed algorithm to a collection of FDG-PET images from 232 NC, MCI, and AD subjects. Our experimental results demonstrate that the proposed algorithm is promising in revealing the brain region connectivity differences among these groups.
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| 关 键 词: |
计算机科学; 数据挖掘; 应用
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-13：zyk
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| 阅读次数: |
41
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