Share Email Print

Proceedings Paper • new

A generative-predictive framework to capture altered brain activity in fMRI and its association with genetic risk: application to Schizophrenia
Author(s): Sayan Ghosal; Qiang Chen; Aaron L. Goldman; William Ulrich; Karen F. Berman; Daniel R. Weinberger; Venkata S. Mattay; Archana Venkataraman
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We present a generative-predictive framework that captures the differences in regional brain activity between a neurotypical cohort and a clinical population, as guided by patient-specific genetic risk. Our model assumes that the functional activations in the neurotypical subjects are distributed around a population mean, and that the altered brain activity in neuropsychiatric patients is defined via deviations from this neurotypical mean. We employ group sparsity to identify a set of brain regions that simultaneously explain the salient functional differences and specify a set of basis vector, that span the low dimensional data subspace. The patient-specific projections onto this subspace are used as feature vectors to identify multivariate associations with genetic risk. We have evaluated our model on a task-based fMRI dataset from a population study of schizophrenia. We compare our model with two baseline methods, regression using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression, which establishes direct association between the brain activity during a working memory task and schizophrenia polygenic risk. Our model demonstrates greater consistency and robustness across bootstrapping experiments than the machine learning baselines. Moreover, the set of brain regions implicated by our model underlie the well documented executive cognitive deficits in schizophrenia.

Paper Details

Date Published: 15 March 2019
PDF: 11 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094927 (15 March 2019); doi: 10.1117/12.2511220
Show Author Affiliations
Sayan Ghosal, Johns Hopkins Univ. (United States)
Qiang Chen, Lieber Institute for Brain Development (United States)
Aaron L. Goldman, Lieber Institute for Brain Development (United States)
William Ulrich, Lieber Institute for Brain Development (United States)
Karen F. Berman, National Institutes of Health (United States)
Daniel R. Weinberger, Lieber Institute for Brain Development (United States)
Johns Hopkins Univ. (United States)
Venkata S. Mattay, Lieber Institute for Brain Development (United States)
Johns Hopkins Univ. (United States)
Archana Venkataraman, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top