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.

Original languageEnglish
Pages (from-to)203-227
Number of pages25
JournalCanadian Journal of Statistics
Issue number1
StatePublished - Mar 2021


  • Brain connectivity
  • Laplacian matrix
  • brain structure
  • generalized linear regression
  • penalized regression
  • structured penalties


Dive into the research topics of 'Connectivity-informed adaptive regularization for generalized outcomes'. Together they form a unique fingerprint.

Cite this