TY - GEN
T1 - Constrained sparse functional connectivity networks for MCI classification
AU - Wee, Chong Yaw
AU - Yap, Pew Thian
AU - Zhang, Daoqiang
AU - Wang, Lihong
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.
PY - 2012
Y1 - 2012
N2 - Mild cognitive impairment (MCI) is difficult to diagnose due to its subtlety. Recent emergence of advanced network analysis techniques utilizing resting-state functional Magnetic Resonance Imaging (rs-fMRI) has made the understanding of neurological disorders more comprehensively at a whole-brain connectivity level. However, inferring effective brain connectivity from fMRI data is a challenging task, particularly when the ultimate goal is to obtain good control-patient classification performance. Incorporating sparsity into connectivity modeling can potentially produce results that are biologically more meaningful since most biologically networks are formed by a relatively few number of connections. However, this constraint, when applied at an individual level, will degrade classification performance due to inter-subject variability. To address this problem, we consider a constrained sparse linear regression model associated with the least absolute shrinkage and selection operator (LASSO). Specifically, we introduced sparsity into brain connectivity via l1-norm penalization, and ensured consistent non-zero connections across subjects via l2-norm penalization. Our results demonstrate that the constrained sparse network gives better classification performance than the conventional correlation-based network, indicating its greater sensitivity to early stage brain pathologies.
AB - Mild cognitive impairment (MCI) is difficult to diagnose due to its subtlety. Recent emergence of advanced network analysis techniques utilizing resting-state functional Magnetic Resonance Imaging (rs-fMRI) has made the understanding of neurological disorders more comprehensively at a whole-brain connectivity level. However, inferring effective brain connectivity from fMRI data is a challenging task, particularly when the ultimate goal is to obtain good control-patient classification performance. Incorporating sparsity into connectivity modeling can potentially produce results that are biologically more meaningful since most biologically networks are formed by a relatively few number of connections. However, this constraint, when applied at an individual level, will degrade classification performance due to inter-subject variability. To address this problem, we consider a constrained sparse linear regression model associated with the least absolute shrinkage and selection operator (LASSO). Specifically, we introduced sparsity into brain connectivity via l1-norm penalization, and ensured consistent non-zero connections across subjects via l2-norm penalization. Our results demonstrate that the constrained sparse network gives better classification performance than the conventional correlation-based network, indicating its greater sensitivity to early stage brain pathologies.
UR - http://www.scopus.com/inward/record.url?scp=84872932372&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872932372&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33418-4_27
DO - 10.1007/978-3-642-33418-4_27
M3 - Conference contribution
C2 - 23286051
AN - SCOPUS:84872932372
SN - 9783642334177
VL - 7511 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 212
EP - 219
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings
A2 - Menze, Bjoern H.
A2 - Tu, Zhuowen
A2 - Criminisi, Antonio
A2 - Menze, Bjoern H.
A2 - Langs, Georg
A2 - Montillo, Albert
A2 - Ayache, Nicholas
A2 - Delingette, Hervé
A2 - Lu, Le
A2 - Langs, Georg
A2 - Golland, Polina
A2 - Mori, Kensaku
PB - Springer Verlag
T2 - 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Y2 - 5 October 2012 through 5 October 2012
ER -