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Systems and methods for modeling and processing functional magnetic resonance image data using full-brain vector auto-regressive model
专利权人:
Guillermo Alberto Cecchi;Rahul Garg;Ravishankar Rao
发明人:
Guillermo Alberto Cecchi,Rahul Garg,Ravishankar Rao
申请号:
US13197011
公开号:
US08861815B2
申请日:
2011.08.03
申请国别(地区):
US
年份:
2014
代理人:
摘要:
Systems and methods for modeling functional magnetic resonance image datasets using a multivariate auto-regressive model which captures temporal dynamics in the data, and creates a reduced representation of the dataset representative of functional connectivity of voxels with respect to brain activity. Raw spatio-temporal data is processed using a multivariate auto-regressive model, wherein coefficients in the model with high weights are retained as indices that best describe the full spatio-temporal data. When there are a relatively small number of temporal samples of the data, sparse regression techniques are used to build the model. The model coefficients are used to perform data processing functions such as indexing, prediction, and classification.
来源网站:
中国工程科技知识中心
来源网址:
http://www.ckcest.cn/home/

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