TY - GEN
T1 - Integrative Bayesian analysis of neuroimaging-genetic data through hierarchical dimension reduction
AU - Azadeh, S.
AU - Hobbs, B. P.
AU - Ma, L.
AU - Nielsen, D. A.
AU - Moeller, F. G.
AU - Baladandayuthapani, V.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/15
Y1 - 2016/6/15
N2 - Advances in neuromedicine have emerged from endeavors to elucidate the distinct genetic factors that influence the changes in brain structure that underlie various neurological conditions. We present a framework for examining the extent to which genetic factors impact imaging phenotypes described by voxel-wise measurements organized into collections of functionally relevant regions of interest (ROIs) that span the entire brain. Statistically, the integration of neuroimaging and genetic data is challenging. Because genetic variants are expected to impact different regions of the brain, an appropriate method of inference must simultaneously account for spatial dependence and model uncertainty. Our proposed framework combines feature extraction using generalized principal component analysis to account for inherent short- and long-range structural dependencies with Bayesian model averaging to effectuate variable selection in the presence of multiple genetic variants. The methods are demonstrated on a cocaine dependence study to identify ROIs associated with genetic factors that impact diffusion parameters.
AB - Advances in neuromedicine have emerged from endeavors to elucidate the distinct genetic factors that influence the changes in brain structure that underlie various neurological conditions. We present a framework for examining the extent to which genetic factors impact imaging phenotypes described by voxel-wise measurements organized into collections of functionally relevant regions of interest (ROIs) that span the entire brain. Statistically, the integration of neuroimaging and genetic data is challenging. Because genetic variants are expected to impact different regions of the brain, an appropriate method of inference must simultaneously account for spatial dependence and model uncertainty. Our proposed framework combines feature extraction using generalized principal component analysis to account for inherent short- and long-range structural dependencies with Bayesian model averaging to effectuate variable selection in the presence of multiple genetic variants. The methods are demonstrated on a cocaine dependence study to identify ROIs associated with genetic factors that impact diffusion parameters.
KW - Bayesian model averaging
KW - diffusion tensor imaging
KW - generalized principal component analysis
KW - imaging-genetics
UR - http://www.scopus.com/inward/record.url?scp=84978436661&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978436661&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2016.7493393
DO - 10.1109/ISBI.2016.7493393
M3 - Conference contribution
C2 - 27917260
AN - SCOPUS:84978436661
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 824
EP - 828
BT - 2016 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Y2 - 13 April 2016 through 16 April 2016
ER -