Brain effective connectivity modeling for alzheimer's disease by sparse gaussian bayesian network

Authors:
Shuai Huang;Jing Li;Jieping Ye;Adam Fleisher;Kewei Chen;Teresa Wu;Eric Reiman
Affiliations:
Arizona State University, Tempe, AZ, USA;Arizona State University, Tempe, AZ, USA;Arizona State University, Tempe, AZ, USA;Banner Alzheimer's Institute, Banner Good Samaritan Medical Center, Phoenix, AZ, USA;Banner Alzheimer's Institute, Banner Good Samaritan Medical Center, Phoenix, AZ, USA;Arizona State University, Tempe, AZ, USA;Banner Alzheimer's Institute, Banner Good Samaritan Medical Center, Phoenix, AZ, USA
Venue:
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2011

Citing 7
Cited 0

Bayesian Artificial Intelligence

Bayesian Artificial Intelligence
The max-min hill-climbing Bayesian network structure learning algorithm

Machine Learning
Using Markov Blankets for Causal Structure Learning

The Journal of Machine Learning Research
Sparse multivariate autoregressive (mAR)-based partial directed coherence (PDC) for electroencephalogram (EEG) analysis

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Learning graphical model structure using L1-regularization paths

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Causal inference and causal explanation with background knowledge

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent studies have shown that Alzheimer's disease (AD) is related to alteration in brain connectivity networks. One type of connectivity, called effective connectivity, defined as the directional relationship between brain regions, is essential to brain function. However, there have been few studies on modeling the effective connectivity of AD and characterizing its difference from normal controls (NC). In this paper, we investigate the sparse Bayesian Network (BN) for effective connectivity modeling. Specifically, we propose a novel formulation for the structure learning of BNs, which involves one L1-norm penalty term to impose sparsity and another penalty to ensure the learned BN to be a directed acyclic graph - a required property of BNs. We show, through both theoretical analysis and extensive experiments on eleven moderate and large benchmark networks with various sample sizes, that the proposed method has much improved learning accuracy and scalability compared with ten competing algorithms. We apply the proposed method to FDG-PET images of 42 AD and 67 NC subjects, and identify the effective connectivity models for AD and NC, respectively. Our study reveals that the effective connectivity of AD is different from that of NC in many ways, including the global-scale effective connectivity, intra-lobe, inter-lobe, and inter-hemispheric effective connectivity distributions, as well as the effective connectivity associated with specific brain regions. These findings are consistent with known pathology and clinical progression of AD, and will contribute to AD knowledge discovery.