A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities
Relating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of the brain and possibly other clinical and biol...
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doaj-25f5f1b8887e4bb39a5e71db66ae73ae2020-11-25T01:03:38ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-03-011210.3389/fnins.2018.00184254541A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various ModalitiesWenqiong Xue0F. DuBois Bowman1Jian Kang2Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United StatesDepartment of Biostatistics, The Mailman School of Public Health, Columbia University, New York, NY, United StatesDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, United StatesRelating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of the brain and possibly other clinical and biologic measures. We propose a Bayesian hierarchical model to predict disease status, which is able to incorporate information from both functional and structural brain imaging scans. We consider a two-stage whole brain parcellation, partitioning the brain into 282 subregions, and our model accounts for correlations between voxels from different brain regions defined by the parcellations. Our approach models the imaging data and uses posterior predictive probabilities to perform prediction. The estimates of our model parameters are based on samples drawn from the joint posterior distribution using Markov Chain Monte Carlo (MCMC) methods. We evaluate our method by examining the prediction accuracy rates based on leave-one-out cross validation, and we employ an importance sampling strategy to reduce the computation time. We conduct both whole-brain and voxel-level prediction and identify the brain regions that are highly associated with the disease based on the voxel-level prediction results. We apply our model to multimodal brain imaging data from a study of Parkinson's disease. We achieve extremely high accuracy, in general, and our model identifies key regions contributing to accurate prediction including caudate, putamen, and fusiform gyrus as well as several sensory system regions.http://journal.frontiersin.org/article/10.3389/fnins.2018.00184/fullBayesian spatial modelpredictionMCMCposterior predictive probabilityimportance samplingParkinson's disease |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wenqiong Xue F. DuBois Bowman Jian Kang |
spellingShingle |
Wenqiong Xue F. DuBois Bowman Jian Kang A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities Frontiers in Neuroscience Bayesian spatial model prediction MCMC posterior predictive probability importance sampling Parkinson's disease |
author_facet |
Wenqiong Xue F. DuBois Bowman Jian Kang |
author_sort |
Wenqiong Xue |
title |
A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities |
title_short |
A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities |
title_full |
A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities |
title_fullStr |
A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities |
title_full_unstemmed |
A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities |
title_sort |
bayesian spatial model to predict disease status using imaging data from various modalities |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroscience |
issn |
1662-453X |
publishDate |
2018-03-01 |
description |
Relating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of the brain and possibly other clinical and biologic measures. We propose a Bayesian hierarchical model to predict disease status, which is able to incorporate information from both functional and structural brain imaging scans. We consider a two-stage whole brain parcellation, partitioning the brain into 282 subregions, and our model accounts for correlations between voxels from different brain regions defined by the parcellations. Our approach models the imaging data and uses posterior predictive probabilities to perform prediction. The estimates of our model parameters are based on samples drawn from the joint posterior distribution using Markov Chain Monte Carlo (MCMC) methods. We evaluate our method by examining the prediction accuracy rates based on leave-one-out cross validation, and we employ an importance sampling strategy to reduce the computation time. We conduct both whole-brain and voxel-level prediction and identify the brain regions that are highly associated with the disease based on the voxel-level prediction results. We apply our model to multimodal brain imaging data from a study of Parkinson's disease. We achieve extremely high accuracy, in general, and our model identifies key regions contributing to accurate prediction including caudate, putamen, and fusiform gyrus as well as several sensory system regions. |
topic |
Bayesian spatial model prediction MCMC posterior predictive probability importance sampling Parkinson's disease |
url |
http://journal.frontiersin.org/article/10.3389/fnins.2018.00184/full |
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