26 January 2017 Large-scale classification of major depressive disorder via distributed Lasso
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Proceedings Volume 10160, 12th International Symposium on Medical Information Processing and Analysis; 101600Y (2017) https://doi.org/10.1117/12.2256935
Event: 12th International Symposium on Medical Information Processing and Analysis, 2016, Tandil, Argentina
Abstract
Compared to many neurological disorders, for which imaging biomarkers are often available, there are no accepted imaging biomarkers to assist in the diagnosis of major depressive disorder (MDD). One major barrier to understanding MDD has been the lack of a practical and efficient platform for collaborative efforts across multiple data centers; integrating the knowledge from different centers should make it easier to identify characteristic measures that are consistently associated with the illness. Here we applied our newly developed “distributed Lasso” method to brain MRI data from multiple centers to perform feature selection and classification. Over 1,000 participants were involved in the study; our results indicate the potential of the proposed framework to enable large-scale collaborative data analysis in the future.
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Dajiang Zhu, Qingyang Li, Brandalyn C. Riedel, Neda Jahanshad, Derrek P. Hibar, Ilya M. Veer, Henrik Walter, Lianne Schmaal, Dick J. Veltman, Dominik Grotegerd, Udo Dannlowski, Matthew D. Sacchet, Ian H. Gotlib, Jieping Ye, Paul M. Thompson, "Large-scale classification of major depressive disorder via distributed Lasso ", Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 101600Y (26 January 2017); doi: 10.1117/12.2256935; https://doi.org/10.1117/12.2256935
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