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Connectivity‐informed adaptive regularization for generalized outcomes
Author(s) -
Brzyski Damian,
Karas Marta,
M Ances Beau,
Dzemidzic Mario,
Goñi Joaquín,
W Randolph Timothy,
Harezlak Jaroslaw
Publication year - 2021
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11606
Subject(s) - regression , inference , computer science , regularization (linguistics) , robustness (evolution) , linear regression , mutual information , artificial intelligence , neuroimaging , generalized linear model , machine learning , causal inference , dimensionality reduction , data mining , mathematics , econometrics , statistics , psychology , biochemistry , chemistry , psychiatry , gene
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.