Scalar-on-image regression via the soft-thresholded Gaussian process

Kang, Jian; Reich, Brian J.; Staicu, Ana‐Maria

doi:10.1093/biomet/asx075

Cited by 62 publications

(64 citation statements)

References 31 publications

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“…Our model also has interpretive advantages over SVM and GP models by identifying particular voxels, subregions, or regions that contribute significantly to accurate prediction. Compared to the methodology of scalar-on-image regression (Goldsmith et al, 2014 ; Reiss et al, 2015 ; Kang et al, 2016 ; Wang et al, 2017 ), our method models the images as the response, which is a natural generative process, and then we predict the disease distribution given the imaging scans.…”

Section: Discussionmentioning

confidence: 99%

A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities

2018

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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.

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Section: Discussionmentioning

confidence: 99%

A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities

2018

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“…The functional covariates and the coefficient functions are all discretized, e.g. via the pixels of the images, see Goldsmith et al (2014); Li et al (2015); Kang et al (2016). In these two-or three-dimensional problems, because of the curse of dimensionality, the points which are included in the support of the coefficient functions follow a parametric distribution, namely an Ising model.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Functional Linear Regression with Sparse Step Functions

Grollemund¹,

Abraham²,

Baragatti³

et al. 2019

Bayesian Anal.

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The functional linear regression model is a common tool to determine the relationship between a scalar outcome and a functional predictor seen as a function of time. This paper focuses on the Bayesian estimation of the support of the coefficient function. To this aim we propose a parsimonious and adaptive decomposition of the coefficient function as a step function, and a model including a prior distribution that we name Bayesian functional Linear regression with Sparse Step functions (Bliss). The aim of the method is to recover areas of time which influences the most the outcome. A Bayes estimator of the support is built with a specific loss function, as well as two Bayes estimators of the coefficient function, a first one which is smooth and a second one which is a step function. The performance of the proposed methodology is analysed on various synthetic datasets and is illustrated on a black Périgord truffle dataset to study the influence of rainfall on the production.MSC 2010 subject classifications: Primary 62F15; Secondary 62J05.

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“…First, our method was implemented in a two‐stage basis. A single‐stage method, such as Ising and Gaussian process priors, 22,39 may be considered to further improve the estimation performance. However, such priors introduce high computational challenges under ultrahigh‐dimensional settings.…”

Section: Discussionmentioning

confidence: 99%

“…The role of c V is to rescale the total effect of massive image predictors (ie, the summation in ()), such that this total effect is bounded away from infinity when V increases. The value of c V can be chosen as V 1/2 22 …”

Section: Model Descriptionmentioning

confidence: 99%

Bayesian latent factor on image regression with nonignorable missing data

Wang

Song

Zhu

2020

Statistics in Medicine

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Medical imaging data have been widely used in modern health care, particularly in the prognosis, screening, diagnosis, and treatment of various diseases. In this study, we consider a latent factor‐on‐image (LoI) regression model that regresses a latent factor on ultrahigh dimensional imaging covariates. The latent factor is characterized by multiple manifest variables through a factor analysis model, while the manifest variables are subject to nonignorable missingness. We propose a two‐stage approach for statistical inference. At the first stage, an efficient functional principal component analysis method is applied to reduce the dimension and extract useful features/eigenimages. At the second stage, a factor analysis mode is proposed to characterize the latent response variable. Moreover, an LoI model is used to detect influential risk factors, and an exponential tiling model applied to accommodate nonignoreable nonresponses. A fully Bayesian method with an adjust spike‐and‐slab absolute shrinkage and selection operator (lasso) procedure is developed for the estimation and selection of influential features/eigenimages. Simulation studies show the proposed method exhibits satisfactory performance. The proposed methodology is applied to a study on the Alzheimer's Disease Neuroimaging Initiative data set.

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Scalar-on-image regression via the soft-thresholded Gaussian process

Cited by 62 publications

References 31 publications

A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities

A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities

Bayesian Functional Linear Regression with Sparse Step Functions

Bayesian latent factor on image regression with nonignorable missing data

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