TY - GEN
T1 - Clustering of high dimensional longitudinal imaging data
AU - Lee, Seonjoo
AU - Zipunnikov, Vadim
AU - Shiee, Navid
AU - Crainiceanu, Ciprian
AU - Caffo, Brian S.
AU - Pham, Dzung L.
PY - 2013
Y1 - 2013
N2 - In the study of brain disease processes and aging, longitudinal imaging studies are becoming increasingly commonplace. Indeed, there are hundreds of studies collecting multi-sequence multi-modality brain images at multiple time points on hundreds of subjects over many years. A fundamental problem in this context is how to classify subjects according to their baseline and longitudinal changes in the presence of strong spatio-temporal biological and technological measurement error. We propose a fast and scalable clustering approach by defining a metric between latent trajectories of brain images. Methods were motivated by and applied to a longitudinal voxel-based morphometry study of multiple sclerosis. Results indicate that there are two distinct patterns of ventricular change that are associated with clinical outcomes.
AB - In the study of brain disease processes and aging, longitudinal imaging studies are becoming increasingly commonplace. Indeed, there are hundreds of studies collecting multi-sequence multi-modality brain images at multiple time points on hundreds of subjects over many years. A fundamental problem in this context is how to classify subjects according to their baseline and longitudinal changes in the presence of strong spatio-temporal biological and technological measurement error. We propose a fast and scalable clustering approach by defining a metric between latent trajectories of brain images. Methods were motivated by and applied to a longitudinal voxel-based morphometry study of multiple sclerosis. Results indicate that there are two distinct patterns of ventricular change that are associated with clinical outcomes.
KW - cluster analysis
KW - longitudinal functional principal component analysis (LFPCA)
KW - regional analysis of volumes examined in normalized space (RAVENS)
KW - ultra high dimensional longitudinal data
UR - http://www.scopus.com/inward/record.url?scp=84885200378&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885200378&partnerID=8YFLogxK
U2 - 10.1109/PRNI.2013.18
DO - 10.1109/PRNI.2013.18
M3 - Conference contribution
AN - SCOPUS:84885200378
SN - 9780769550619
T3 - Proceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
SP - 33
EP - 36
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 -