Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm. - Ricardo Pizarro; Xi Cheng; Alan S. Barnett; Hervé Lemaître; Beth A. Verchinski; Aaron L. Goldman; Ena Xiao; Qian Luo; Karen Faith Berman; Joseph H. Callicott; Daniel R. Weinberger; Venkata S. Mattay (Frontiers Neuroinformatics, 2016)

Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework. - Sayan Ghosal; Qiang Chen; Aaron L. Goldman; William Ulrich; Karen Faith Berman; Daniel R. Weinberger; Venkata S. Mattay; Archana Venkataraman (MICCAI (4), 2019)

A generative-predictive framework to capture altered brain activity in fMRI and its association with genetic risk: application to Schizophrenia. - Sayan Ghosal; Qiang Chen; Aaron L. Goldman; William Ulrich; Karen Faith Berman; Daniel R. Weinberger; Venkata S. Mattay; Archana Venkataraman (Image Processing, 2019)

G-MIND: an end-to-end multimodal imaging-genetics framework for biomarker identification and disease classification. - Sayan Ghosal; Qiang Chen; Giulio Pergola; Aaron L. Goldman; William Ulrich; Karen Faith Berman; Giuseppe Blasi; Leonardo Fazio; Antonio Rampino; Alessandro Bertolino; Daniel R. Weinberger; Venkata S. Mattay; et al. (Image Processing, 2021)

A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space. - Sayan Ghosal; Qiang Chen; Giulio Pergola; Aaron L. Goldman; William Ulrich; Karen Faith Berman; Giuseppe Blasi; Leonardo Fazio; Antonio Rampino; Alessandro Bertolino; Daniel R. Weinberger; Venkata S. Mattay; et al. (NeuroImage, 2021)

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease. - Sayan Ghosal; Qiang Chen; Giulio Pergola; Aaron L. Goldman; William Ulrich; Daniel R. Weinberger; Archana Venkataraman (ICLR, 2022)