TY - GEN
T1 - A graph-based brain parcellation method extracting sparse networks
AU - Honnorat, Nicolas
AU - Eavani, Harini
AU - Satterthwaite, Theodore D.
AU - Davatzikos, Christos
PY - 2013
Y1 - 2013
N2 - FMRI is a powerful tool for assessing the functioning of the brain. The analysis of resting-state fMRI allows to describe the functional relationship between the cortical areas. Since most connectivity analysis methods suffer from the curse of dimensionality, the cortex needs to be first partitioned into regions of coherent activation patterns. Once the signals of these regions of interest have been extracted, estimating a sparse approximation of the inverse of their correlation matrix is a classical way to robustly describe their functional interactions. In this paper, we address both objectives with a novel parcellation method based on Markov Random Fields that favors the extraction of sparse networks of regions. Our method relies on state of the art rsfMRI models, naturally adapts the number of parcels to the data and is guaranteed to provide connected regions due to the use of shape priors. The second contribution of this paper resides in two novel sparsity enforcing potentials. Our approach is validated with a publicly available dataset.
AB - FMRI is a powerful tool for assessing the functioning of the brain. The analysis of resting-state fMRI allows to describe the functional relationship between the cortical areas. Since most connectivity analysis methods suffer from the curse of dimensionality, the cortex needs to be first partitioned into regions of coherent activation patterns. Once the signals of these regions of interest have been extracted, estimating a sparse approximation of the inverse of their correlation matrix is a classical way to robustly describe their functional interactions. In this paper, we address both objectives with a novel parcellation method based on Markov Random Fields that favors the extraction of sparse networks of regions. Our method relies on state of the art rsfMRI models, naturally adapts the number of parcels to the data and is guaranteed to provide connected regions due to the use of shape priors. The second contribution of this paper resides in two novel sparsity enforcing potentials. Our approach is validated with a publicly available dataset.
KW - Markov Random Fields
KW - fMRI
KW - parcellation
KW - sparsity
KW - star convexity
UR - http://www.scopus.com/inward/record.url?scp=84885200220&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885200220&partnerID=8YFLogxK
U2 - 10.1109/PRNI.2013.48
DO - 10.1109/PRNI.2013.48
M3 - Conference contribution
AN - SCOPUS:84885200220
SN - 9780769550619
T3 - Proceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
SP - 157
EP - 160
BT - Proceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
T2 - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
Y2 - 22 June 2013 through 24 June 2013
ER -