Abstract
We propose a joint dictionary learning framework that couples imaging and genetics data in a low dimensional subspace as guided by clinical diagnosis. We use a graph regularization penalty to simultaneously capture inter-regional brain interactions and identify the representative set anatomical basis vectors that span the low dimensional space. We further employ group sparsity to find the representative set of genetic basis vectors that span the same latent space. Finally, the latent projection is used to classify patients versus controls. We have evaluated our model on two task fMRI paradigms and single nucleotide polymorphism (SNP) data from schizophrenic patients and matched neurotypical controls. We employ a ten fold cross validation technique to show the predictive power of our model. We compare our model with canonical correlation analysis of imaging and genetics data and random forest classification. Our approach shows better prediction accuracy on both task datasets. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Batmanghelich, N.K., et al.: Probabilistic modeling of imaging, genetics and diagnosis. IEEE Trans. Med. Imaging 35(7), 1765–1779 (2016)
Callicott, J.H., et al.: Abnormal fMRI response of the dorsolateral prefrontal cortex in cognitively intact siblings of patients with schizophrenia. Am. J. Psychiatry 160(4), 709–719 (2003)
Chen, Q., et al.: Schizophrenia polygenic risk score predicts mnemonic hippocampal activity. Brain 141(4), 1218–1228 (2018)
Dean, B.: Is schizophrenia the price of human central nervous system complexity? Aust. New Zealand J. Psychiatry 43(1), 13–24 (2009)
Du, L., et al.: Pattern discovery in brain imaging genetics via SCCA modeling with a generic non-convex penalty. Sci. Rep. 7(1), 14052 (2017)
Fan, L., et al.: The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26(8), 3508–3526 (2016)
Rasetti, R., et al.: Altered hippocampal-parahippocampal function during stimulus encoding. JAMA Psychiatry 71(3), 236 (2014)
Wang, H., et al.: Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics 28(2), 229–237 (2012)
Acknowledgements
This work was supported by NSF CRCNS 1822575, and the National Institute of Mental Health extramural research program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ghosal, S. et al. (2019). Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_71
Download citation
DOI: https://doi.org/10.1007/978-3-030-32251-9_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32250-2
Online ISBN: 978-3-030-32251-9
eBook Packages: Computer ScienceComputer Science (R0)