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Structural Brain Network Constrained Neuroimaging Marker Identification for Predicting Cognitive Functions
De Wang1, Feiping Nie1, Heng Huang1, Jingwen Yan2, Shannon L. Risacher2, Andrew J. Saykin2, and Li Shen2
1Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA
wangdelp@gmail.com
feipingnie@gmail.com
heng@uta.edu
2Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
jingyan@umail.iu.edu
srisache@iu.edu
asaykin@iu.edu
shenli@iu.edu
Abstract. Neuroimaging markers have been widely used to predict the cognitive functions relevant to the progression of Alzheimer’s disease (AD). Most previous studies identify the imaging markers without considering the brain structural correlations between neuroimaging measures. However, many neuroimaging markers interrelate and work together to reveal the cognitive functions, such that these relevant markers should be selected together as the phenotypic markers. To solve this problem, in this paper, we propose a novel network constrained feature selection (NCFS) model to identify the neuroimaging markers guided by the structural brain network, which is constructed by the sparse representation method such that the interrelations between neuroimaging features are encoded into probabilities. Our new methods are evaluated by the MRI and AV45-PET data from ADNI-GO and ADNI-2 (Alzheimer’s Disease Neuroimaging Initiative). In all cognitive function prediction tasks, our new NCFS method outperforms other state-of-the-art regression approaches. Meanwhile, we show that the new method can select the correlated imaging markers, which are ignored by the competing approaches.
Keywords: Neuroimaging Marker Identification, Brain Network Based Feature Selection, Correlated Marker Selection, Imaging Genetics
LNCS 7917, p. 536 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013