TY - JOUR
T1 - Connectivity-informed adaptive regularization for generalized outcomes
AU - Brzyski, Damian
AU - Karas, Marta
AU - M Ances, Beau
AU - Dzemidzic, Mario
AU - Goñi, Joaquín
AU - W Randolph, Timothy
AU - Harezlak, Jaroslaw
N1 - Publisher Copyright:
© 2021 Statistical Society of Canada / Société statistique du Canada
PY - 2021/3
Y1 - 2021/3
N2 - One of the challenging problems in neuroimaging is the principled incorporation of information from different imaging modalities. Data from each modality are frequently analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, generalized ridgified Partially Empirical Eigenvectors for Regression (griPEER), to estimate associations between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER improves the regression coefficient estimation by providing a principled approach to use external information from the structural brain connectivity. Specifically, we incorporate a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. In this work, we address both theoretical and computational issues and demonstrate the robustness of our method despite incomplete information about the structural brain connectivity. In addition, we also provide a significance testing procedure for performing inference on the estimated coefficients. Finally, griPEER is evaluated both in extensive simulation studies and using clinical data to classify HIV+ and HIV− individuals.
AB - One of the challenging problems in neuroimaging is the principled incorporation of information from different imaging modalities. Data from each modality are frequently analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, generalized ridgified Partially Empirical Eigenvectors for Regression (griPEER), to estimate associations between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER improves the regression coefficient estimation by providing a principled approach to use external information from the structural brain connectivity. Specifically, we incorporate a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. In this work, we address both theoretical and computational issues and demonstrate the robustness of our method despite incomplete information about the structural brain connectivity. In addition, we also provide a significance testing procedure for performing inference on the estimated coefficients. Finally, griPEER is evaluated both in extensive simulation studies and using clinical data to classify HIV+ and HIV− individuals.
KW - Brain connectivity
KW - Laplacian matrix
KW - brain structure
KW - generalized linear regression
KW - penalized regression
KW - structured penalties
UR - http://www.scopus.com/inward/record.url?scp=85100869742&partnerID=8YFLogxK
U2 - 10.1002/cjs.11606
DO - 10.1002/cjs.11606
M3 - Article
C2 - 35002039
AN - SCOPUS:85100869742
SN - 0319-5724
VL - 49
SP - 203
EP - 227
JO - Canadian Journal of Statistics
JF - Canadian Journal of Statistics
IS - 1
ER -