Biao Jie

TL;DR: A novel connectivity based framework to integrate multiple properties of connectivity network for improving the classification performance and results show that this method achieves significant performance improvement over those using only one type of network property.

TL;DR: A manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality, which is essential for subsequent classification.

TL;DR: Connectivity analysis indicates that the connectivity of the selected brain regions is different between MCI patients and NC, that is,MCI patients show reduced functional connectivity compared with NC, in line with the findings reported in the existing studies.

TL;DR: Results on 149 subjects with baseline rs‐fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) suggest that the method can not only improve the classification performance in comparison with state‐of‐the‐art methods, but also provide insights into the spatio‐temporal interaction patterns of brain activity and their changes in brain disorders.

TL;DR: A novel framework for estimating the hyper-connectivity network of brain functions and then using this hyper-network for brain disease diagnosis is proposed, which can not only improve the classification performance, but also help discover disease-related biomarkers important for disease diagnosis.