图 书 馆
脑网络激活中重复模式的识别Identification of Recurrent Patterns in the Activation of Brain Networks |
|
| 课程网址: | http://videolectures.net/machine_janoos_networks/ |
| 主讲教师: | Firdaus Janoos |
| 开课单位: | 埃克森美孚公司 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Identifying patterns from the neuroimaging recordings of brain activity related to the unobservable psychological or mental state of an individual can be treated as a unsupervised pattern recognition problem. The main challenges, however, for such an analysis of fMRI data are: a) defining a physiologically meaningful feature-space for representing the spatial patterns across time; b) dealing with the high-dimensionality of the data; and c) robustness to the various artifacts and confounds in the fMRI time-series. In this paper, we present a network-aware feature-space to represent the states of a general network, that enables comparing and clustering such states in a manner that is a) meaningful in terms of the network connectivity structure; b)computationally efficient; c) low-dimensional; and d) relatively robust to structured and random noise artifacts. This feature-space is obtained from a spherical relaxation of the transportation distance metric which measures the cost of transporting ``mass'' over the network to transform one function into another. Through theoretical and empirical assessments, we demonstrate the accuracy and efficiency of the approximation, especially for large problems. While the application presented here is for identifying distinct brain activity patterns from fMRI, this feature-space can be applied to the problem of identifying recurring patterns and detecting outliers in measurements on many different types of networks, including sensor, control and social networks. |
| 关 键 词: | 无监督学习; 计算机科学; 机器学习; 半监督学习; 神经生物学 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-18：cwx |
| 阅读次数: | 47 |