A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space

NeuroImage

Venkata S. Mattay¹  Alessandro Bertolino²  Qiang Chen³  William Ulrich⁴  Leonardo Fazio⁵  Sayan Ghosal⁶  Archana Venkataraman⁷  Daniel R. Weinberger⁷  Antonio Rampino⁷  Karen F. Berman⁸  Giuseppe Blasi⁸  Giulio Pergola⁸  Aaron L. Goldman⁸ 

[1] 4IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG), Italy;Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy;Department of Electrical and Computer Engineering, Johns Hopkins University, USA;Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy;Clinical and Translational Neuroscience Branch, NIMH, NIH, USA;Corresponding author.;Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy;Lieber Institute for Brain Development, USA;

关键词: Imaging-genetics;    Clinical diagnosis;    Low dimensional subspace;    Graph regularization;   

DOI  :  

来源: DOAJ

【 摘 要 】

We propose a novel optimization framework that integrates imaging and genetics data for simultaneous biomarker identification and disease classification. The generative component of our model uses a dictionary learning framework to project the imaging and genetic data into a shared low dimensional space. We have coupled both the data modalities by tying the linear projection coefficients to the same latent space. The discriminative component of our model uses logistic regression on the projection vectors for disease diagnosis. This prediction task implicitly guides our framework to find interpretable biomarkers that are substantially different between a healthy and disease population. We exploit the interconnectedness of different brain regions by incorporating a graph regularization penalty into the joint objective function. We also use a group sparsity penalty to find a representative set of genetic basis vectors that span a low dimensional space where subjects are easily separable between patients and controls. We have evaluated our model on a population study of schizophrenia that includes two task fMRI paradigms and single nucleotide polymorphism (SNP) data. Using ten-fold cross validation, we compare our generative-discriminative framework with canonical correlation analysis (CCA) of imaging and genetics data, parallel independent component analysis (pICA) of imaging and genetics data, random forest (RF) classification, and a linear support vector machine (SVM). We also quantify the reproducibility of the imaging and genetics biomarkers via subsampling. Our framework achieves higher class prediction accuracy and identifies robust biomarkers. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.

【 授权许可】

Unknown