Pattern localization of functional connectivity in Parkinson's disease based on sparse representation
CHEN Yong-bin
LI Yuan-qing
Abstract：In the analysis of brain imaging data, the sparse representation-based pattern localization algorithm has a very good performance at the group level data analysis. But at the single level, it's performance is still disappointed. Therefore, in order to compensate for this deficiency, an improved algorithm based on previous research was proposed in this study. By generating multiple derived data sets from the original data and then performing pattern localization procedure, the improved algorithm has better performance compared to the original in simulation. Subsequently, the improved algorithm was applied to the analysis of localizing all abnormal brain functional connections in Parkinson's disease. 269 abnormal connections were obtained and they were widely distributed throughout the entire brain. Thus, the effectiveness of the algorithm was verified and our findings may have the potential to advance the understanding of the neural mechanism of this disease.
Keywords：pattern localizationsparse representationmultivariate pattern analysisfunctional connectivity
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 843-848 )