TY - GEN
T1 - Riccati-regularized precision matrices for neuroimaging
AU - Honnorat, Nicolas
AU - Davatzikos, Christos
N1 - Funding Information:
Data were provided by the Human Connectome Project, WU-Minn Consortium (PI: D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. This work was supported by NIH grant R01 EB022573.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - The introduction of graph theory in neuroimaging has provided invaluable tools for the study of brain connectivity. These methods require the definition of a graph, which is typically derived by estimating the effective connectivity between brain regions through the optimization of an ill-posed inverse problem. Considerable efforts have been devoted to the development of methods extracting sparse connectivity graphs. The present paper aims at highlighting the benefits of an alternative approach.We investigate low-rank L2 regularized matrices recently introduced under the denomination of Riccati regularized precision matrices. We demonstrate their benefits for the analysis of cortical thickness map and the extraction of functional biomarkers from resting state fMRI scans. In addition, we explain how speed and result quality can be further improved with random projections. The promising results obtained using the Human Connectome Project dataset, as well as, the numerous possible extensions and applications suggest that Riccati precision matrices might usefully complement current sparse approaches.
AB - The introduction of graph theory in neuroimaging has provided invaluable tools for the study of brain connectivity. These methods require the definition of a graph, which is typically derived by estimating the effective connectivity between brain regions through the optimization of an ill-posed inverse problem. Considerable efforts have been devoted to the development of methods extracting sparse connectivity graphs. The present paper aims at highlighting the benefits of an alternative approach.We investigate low-rank L2 regularized matrices recently introduced under the denomination of Riccati regularized precision matrices. We demonstrate their benefits for the analysis of cortical thickness map and the extraction of functional biomarkers from resting state fMRI scans. In addition, we explain how speed and result quality can be further improved with random projections. The promising results obtained using the Human Connectome Project dataset, as well as, the numerous possible extensions and applications suggest that Riccati precision matrices might usefully complement current sparse approaches.
KW - Precision
KW - Sparse inverse covariance
KW - rs-fMRI
UR - http://www.scopus.com/inward/record.url?scp=85020505910&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020505910&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59050-9_22
DO - 10.1007/978-3-319-59050-9_22
M3 - Conference contribution
C2 - 29503515
AN - SCOPUS:85020505910
SN - 9783319590493
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 275
EP - 286
BT - Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings
A2 - Zhu, Hongtu
A2 - Niethammer, Marc
A2 - Styner, Martin
A2 - Zhu, Hongtu
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Aylward, Stephen
A2 - Oguz, Ipek
PB - Springer Verlag
T2 - 25th International Conference on Information Processing in Medical Imaging, IPMI 2017
Y2 - 25 June 2017 through 30 June 2017
ER -