@inproceedings{889b93fa373b42348f03c95d08ea8a27,
title = "G-MIND: An end-to-end multimodal imaging-genetics framework for biomarker identification and disease classification",
abstract = "We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. Our model consists of an encoder, a decoder and a classifier. The encoder learns a non-linear subspace shared between the input data modalities. The classifier and the decoder act as regularizers to ensure that the low-dimensional encoding captures predictive differences between patients and controls. We use a learnable dropout layer to extract interpretable biomarkers from the data, and our unique training strategy can easily accommodate missing data modalities across subjects. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data. Using 10-fold cross validation, we demonstrate that our model achieves better classification accuracy than baseline methods, and that this performance generalizes to a second dataset collected at a different site. In an exploratory analysis we further show that the biomarkers identified by our model are closely associated with the well-documented deficits in schizophrenia.",
keywords = "Deep Neural Networks, Imaging-Genetics, Learnable Dropout, Schizophrenia",
author = "Sayan Ghosal and Qiang Chen and Giulio Pergola and Goldman, {Aaron L.} and William Ulrich and Berman, {Karen F.} and Giuseppe Blasi and Leonardo Fazio and Antonio Rampino and Alessandro Bertolino and Weinberger, {Daniel R.} and Mattay, {Venkata S.} and Archana Venkataraman",
note = "Funding Information: Acknowledgements: This work was supported by NSF CRCNS 1822575, and the National Institute of Mental Health extramural research program. Publisher Copyright: {\textcopyright} 2021 SPIE.; Medical Imaging 2021: Image Processing ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2581127",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Ivana Isgum and Landman, {Bennett A.}",
booktitle = "Medical Imaging 2021",
}