Titlebook: Brain Informatics and Health; 8th International Co Yike Guo,Karl Friston,Hanchuan Peng Conference proceedings 2015 Springer International P

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John H. Hubbard,Beverly H. Westst based on the G-test, we validated this framework in a sample of euthymic bipolar I subjects, and identified abnormal subgraph patterns in the rsfMRI networks of these subjects relative to healthy controls.

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John H. Hubbard,Beverly H. Westtics data from Alzheimer’s Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 12 significant high level two dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiol

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https://doi.org/10.1007/978-1-349-06473-1nal manner, which helps to extract efficient and robust features and conserve abundant detail information for the neuroimaging classification. The proposed algorithm was verified by three human brain fMRI classification datasets, and showed a great potential compared with the traditional classificat

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Identifying Distinguishing Factors in Predicting Brain Activities – An Inclusive Machine Learning Ap our inclusive approach can help machine learning methods to automatically identify most discriminating factors in predicting brain activities with much higher accuracy than the previous exclusive approaches.

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Two-Dimensional Enrichment Analysis for Mining High-Level Imaging Genetic Associationstics data from Alzheimer’s Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 12 significant high level two dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiol