Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning

Abstract

Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer’s disease (AD). Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer’s Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns.

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

“…There are, however, a few examples of studies that are indeed “predicting” specific values of the target measures. For example, Wan et al has proposed an elegant regression model called CORNLIN (Wan et al, 2014) that employs a sparse Bayesian learning algorithm to predict multiple cognitive scores based on 98 structural MRI regions of interests (ROIs) for Alzheimer’s disease patients. The polynomial model used in CORNLIN can detect either a nonlinear or linear relationship between brain structure and cognitive decline.…”

Linearized and Kernelized Sparse Multitask Learning for Predicting Cognitive Outcomes in Alzheimer’s Disease

“…Ten widely used clinical/cognitive assessment scores [3, 20, 21] were employed in this study, including Alzheimer's Disease Assessment Scale (ADAS) cognitive total score, Mini Mental State Exam (MMSE) score, Rey Auditory Verbal Learning Test (RAVLT) involving total score of the first 5 learning trials (TOTAL), Trial 6 total number of words recalled (TOT6), 30-minute delay score (T30), and 30-minute delay recognition score (RECOG), FLU involving animal total score (ANIM) and vegetable total score (VEG), and TRAILS including Trail Making test A score and B score.…”

“…Regression analyses were commonly used to predict cognitive scores from imaging measures. The relationship between commonly used cognitive measures and structural changes with MRI has been previously studied by regression models and the results demonstrated that there exists a relationship between baseline MRI features and cognitive measures [3, 4]. For example, Wan et al proposed an elegant regression model called CORNLIN that employs a sparse Bayesian learning algorithm to predict multiple cognitive scores based on 98 structural MRI regions of interests (ROIs) for Alzheimer's disease patients.…”

“…For example, Wan et al proposed an elegant regression model called CORNLIN that employs a sparse Bayesian learning algorithm to predict multiple cognitive scores based on 98 structural MRI regions of interests (ROIs) for Alzheimer's disease patients. The polynomial model used in CORNLIN can detect either a nonlinear or a linear relationship between brain structure and cognitive decline [3]. Stonnington et al adopted relevance vector regression, a sparse kernel method formulated in a Bayesian framework, to predict four sets of cognitive scores using MRI voxel based morphometry measures [4].…”

The Role of Neuroimaging and Electroencephalogram in Diagnosis of Alzheimer Disease

“…Alzheimer Disease is a chronic Neuro-degenerative disorder that has ranked as third most expensive disease and sixth leading cause of death in United States. It is neurodegenerative disorder characterized by rapid impairment of memory and some other cognitive functions, which are mainly associated with the behavioral disturbances and finally leads to total dependency [3]. An important research is to identify the neuroanatomical basis of cognitive impairment in Alzheimer Disease (AD).…”