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Discriminative Brain Effective Connectivity Analysis for Alzheimer’s Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network

Dr. Luping Zhou

Speaker: Dr. Luping Zhou, Faculty of Informatics, University of Wollongong

Seminar Chairman: Associate Professor Jian Zhang (jian.zhang@uts.edu.au)

Seminar Abstract:

Analysing brain networks from neuro images 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 analysing brain effective connectivity for AD from ADNI data, and demonstrates significant improvements over the state-of-the-art work.

Biography

Dr. Luping Zhou is a Vice Chancellor research fellow in Faculty of Informatics, University of Wollongong. She obtained her Ph.D in medical image analysis from School of Engineering, The Australian National University in 2010. After that, she worked as a postdoctoral research fellow in IDEA group, University of North Carolina at Chapel Hill, and then a research scientist in Australian e-Health Research Centre, CSIRO. Before she started her Ph.D, she was a senior R&D engineer in Volume Interactions (Bracco), developing medical imaging applications for surgical navigation and planning. Her research interests include medical image analysis, machine learning and computer vision.

Overview to AAI seminar series

The Advanced Analytics Seminar Series presents the latest theoretical advancement and empirical experience in a broad range of interdisciplinary and business-oriented analytics fields. It covers topics related to data mining, machine learning, statistics, bioinformatics, behavior informatics, marketing analytics and multimedia analytics. It also provides a platform for the showcase of commercial products in ubiquitous advanced analytics. Speakers are invited from both academia and industry. It opens regularly on every Friday afternoon at the garden-like UTS Blackfriars Campus. You are warmly welcome to attend this seminar series.

Jinyan Li, Seminar Coordinator, Associate Professor
Advanced Analytics Institute, School of Software, Faculty of Engineering and IT
University of Technology, Sydney
P.O. Box 123, Broadway, NSW 2007, Australia
Tel: 02 95149264 (office);
http://www-staff.it.uts.edu.au/~jinyli</p>

Date:
13 September 2013
Time:
13:30 - 14:30
Location:
Blackfriars CB25 Room GC.05.01.04
(5 minutes walk from the UTS Tower Building, CB01)
Audience:
All Welcome
Contact:
Colin Wise

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