TY - GEN
T1 - Inferring functional network-based signatures via structurally-weighted LASSO model
AU - Zhu, Dajiang
AU - Shen, Dinggang
AU - Liu, Tianming
PY - 2013
Y1 - 2013
N2 - Most current research approaches for functional/effective connectivity analysis focus on pair-wise connectivity and cannot deal with network-scale functional interactions. In this paper, we propose a structurally-weighted LASSO (SW-LASSO) regression model to represent the functional interaction among multiple regions of interests (ROIs) based on resting state fMRI (R-fMRI) data. The structural connectivity constraints derived from diffusion tenor imaging (DTI) data will guide the selection of the weights which adjust the penalty levels of different coefficients corresponding to different ROIs. Using the Default Mode Network (DMN) as a test-bed, our results indicate that the learned SW-LASSO has good capability of differentiating Mild Cognitive Impairment (MCI) subjects from their normal controls and has promising potential to characterize the brain functions among different condition, thus serving as the functional network-based signature.
AB - Most current research approaches for functional/effective connectivity analysis focus on pair-wise connectivity and cannot deal with network-scale functional interactions. In this paper, we propose a structurally-weighted LASSO (SW-LASSO) regression model to represent the functional interaction among multiple regions of interests (ROIs) based on resting state fMRI (R-fMRI) data. The structural connectivity constraints derived from diffusion tenor imaging (DTI) data will guide the selection of the weights which adjust the penalty levels of different coefficients corresponding to different ROIs. Using the Default Mode Network (DMN) as a test-bed, our results indicate that the learned SW-LASSO has good capability of differentiating Mild Cognitive Impairment (MCI) subjects from their normal controls and has promising potential to characterize the brain functions among different condition, thus serving as the functional network-based signature.
KW - Functional network-based signature
KW - regression model
UR - http://www.scopus.com/inward/record.url?scp=84881618510&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881618510&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2013.6556638
DO - 10.1109/ISBI.2013.6556638
M3 - Conference contribution
AN - SCOPUS:84881618510
SN - 9781467364546
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 970
EP - 973
BT - ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
T2 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Y2 - 7 April 2013 through 11 April 2013
ER -