TY - GEN
T1 - Structural brain network constrained neuroimaging marker identification for predicting cognitive functions
AU - Wang, De
AU - Nie, Feiping
AU - Huang, Heng
AU - Yan, Jingwen
AU - Risacher, Shannon L.
AU - Saykin, Andrew J.
AU - Shen, Li
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Brain Network Based Feature Selection
KW - Correlated Marker Selection
KW - Imaging Genetics
KW - Neuroimaging Marker Identification
UR - http://www.scopus.com/inward/record.url?scp=84879870123&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38868-2_45
DO - 10.1007/978-3-642-38868-2_45
M3 - 会议稿件
AN - SCOPUS:84879870123
SN - 9783642388675
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 536
EP - 547
BT - Information Processing in Medical Imaging - 23rd International Conference, IPMI 2013, Proceedings
T2 - 23rd International Conference on Information Processing in Medical Imaging, IPMI 2013
Y2 - 28 June 2013 through 3 July 2013
ER -