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Data-driven cluster selection for subcortical shape and cortical thickness predicts recovery from depressive symptoms.

, , , , , , , , , , , and . ISBI, page 502-506. IEEE, (2017)

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Random forest classification of depression status based on subcortical brain morphometry following electroconvulsive therapy., , , , , , , and . ISBI, page 92-96. IEEE, (2015)Data-driven cluster selection for subcortical shape and cortical thickness predicts recovery from depressive symptoms., , , , , , , , , and 2 other author(s). ISBI, page 502-506. IEEE, (2017)Multimodal Data Registration for Brain Structural Association Networks., , , , , , and . MICCAI (2), volume 11765 of Lecture Notes in Computer Science, page 373-381. Springer, (2019)Subcortical shape and volume abnormalities in an elderly HIV+ cohort., , , , , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 9417 of SPIE Proceedings, page 94171S. SPIE, (2015)Machine learning on high dimensional shape data from subcortical brain surfaces: A comparison of feature selection and classification methods., , , and . Pattern Recognit., (2017)Mapping abnormal subcortical brain morphometry in an elderly HIV+ cohort., , , , , , and . ISBI, page 971-975. IEEE, (2015)ENIGMA military brain injury: A coordinated meta-analysis of diffusion MRI from multiple cohorts., , , , , , , , , and 13 other author(s). ISBI, page 1386-1389. IEEE, (2018)Machine Learning on High Dimensional Shape Data from Subcortical Brain Surfaces: A Comparison of Feature Selection and Classification Methods., , , and . MLMI, volume 9352 of Lecture Notes in Computer Science, page 36-43. Springer, (2015)