Title :
Discriminative Brain Effective Connectivity Analysis for Alzheimer´s Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network
Author :
Luping Zhou ; Lei Wang ; Lingqiao Liu ; Ogunbona, Philip ; Dinggang Shen
Author_Institution :
Sch. of Comput. Sci. & Software Eng., Univ. of Wollongong, Wollongong, NSW, Australia
Abstract :
Analyzing brain networks from neuroimages is becoming a promising approach in identifying novel connectivity-based biomarkers for the Alzheimer\´s disease (AD). In this regard, brain ``effective connectivity" analysis, which studies the causal relationship among brain regions, is highly challenging and of many research opportunities. Most of the existing works in this field use generative methods. Despite their success in data representation and other important merits, generative methods are not necessarily discriminative, which may cause the ignorance of subtle but critical disease-induced changes. In this paper, we propose a learning-based approach that integrates the benefits of generative and discriminative methods to recover effective connectivity. In particular, we employ Fisher kernel to bridge the generative models of sparse Bayesian networks (SBN) and the discriminative classifiers of SVMs, and convert the SBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. Our method is able to simultaneously boost the discriminative power of both the generative SBN models and the SBN-induced SVM classifiers via Fisher kernel. The proposed method is tested on analyzing brain effective connectivity for AD from ADNI data, and demonstrates significant improvements over the state-of-the-art work.
Keywords :
Gaussian processes; belief networks; brain; diseases; learning (artificial intelligence); medical image processing; support vector machines; Alzheimer´s disease; Fisher kernel learning; SBN parameter learning; SBN-induced SVM classifiers; SVMs; connectivity-based biomarker identification; critical disease-induced changes; data representation; discriminative brain effective connectivity analysis; discriminative classifiers; discriminative method; generalization error bound minimisation; generative SBN models; generative methods; kernel learning approach; neuroimages; sparse Bayesian networks; sparse Gaussian Bayesian network; Bayes methods; Educational institutions; Kernel; Magnetic resonance imaging; Optimization; Support vector machines; Vectors; Alzheimer´s Disease; Brain connectivity analysis; Discriminative learning; sparse Bayesian Network;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on
Conference_Location :
Portland, OR
DOI :
10.1109/CVPR.2013.291