TY - GEN
T1 - Correlation-weighted sparse group representation for brain network construction in MCI classification
AU - Yu, Renping
AU - Zhang, Han
AU - An, Le
AU - Chen, Xiaobo
AU - Wei, Zhihui
AU - Shen, Dinggang
N1 - Funding Information:
R. Yu was supported by the Research Fund for the Doctoral Program of Higher Education of China (RFDP) (No. 20133219110029), the Key Research Foundation of Henan Province (15A520056) and NFSC (No. 61171165, No. 11431015).
Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders,such as Alzheimer’s disease and its early stage,mild cognitive impairment (MCI). In all these applications,the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network,sparse learning has been widely used for complex BFCN construction. However,the conventional l1-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network,which ignores the link strength and could remove strong links in the brain network. Besides,the conventional sparse regularization often overlooks group structure in the brain network,i.e.,a set of links (or connections) sharing similar attribute. To address these issues,we propose to construct BFCN by integrating both link strength and group structure information. Specifically,a novel correlation-weighted sparse group constraint is devised to account for and balance among (1) sparsity,(2) link strength,and (3) group structure,in a unified framework. The proposed method is applied to MCI classification using the resting-state fMRI from ADNI-2 dataset. Experimental results show that our method is effective in modeling human brain connectomics,as demonstrated by superior MCI classification accuracy of 81.8%. Moreover,our method is promising for its capability in modeling more biologically meaningful sparse brain networks,which will benefit both basic and clinical neuroscience studies.
AB - Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders,such as Alzheimer’s disease and its early stage,mild cognitive impairment (MCI). In all these applications,the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network,sparse learning has been widely used for complex BFCN construction. However,the conventional l1-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network,which ignores the link strength and could remove strong links in the brain network. Besides,the conventional sparse regularization often overlooks group structure in the brain network,i.e.,a set of links (or connections) sharing similar attribute. To address these issues,we propose to construct BFCN by integrating both link strength and group structure information. Specifically,a novel correlation-weighted sparse group constraint is devised to account for and balance among (1) sparsity,(2) link strength,and (3) group structure,in a unified framework. The proposed method is applied to MCI classification using the resting-state fMRI from ADNI-2 dataset. Experimental results show that our method is effective in modeling human brain connectomics,as demonstrated by superior MCI classification accuracy of 81.8%. Moreover,our method is promising for its capability in modeling more biologically meaningful sparse brain networks,which will benefit both basic and clinical neuroscience studies.
UR - http://www.scopus.com/inward/record.url?scp=84996590243&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46720-7_5
DO - 10.1007/978-3-319-46720-7_5
M3 - Conference contribution
AN - SCOPUS:84996590243
SN - 9783319467191
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 37
EP - 45
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
A2 - Ourselin, Sebastian
A2 - Joskowicz, Leo
A2 - Sabuncu, Mert R.
A2 - Wells, William
A2 - Unal, Gozde
PB - Springer Verlag
T2 - 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Y2 - 21 October 2016 through 21 October 2016
ER -