Single-index models with functional connectivity network predictors

Author:

Weaver Caleb1ORCID,Xiao Luo1ORCID,Lindquist Martin A2ORCID

Affiliation:

1. Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27606, USA

2. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA

Abstract

Summary Functional connectivity is defined as the undirected association between two or more functional magnetic resonance imaging (fMRI) time series. Increasingly, subject-level functional connectivity data have been used to predict and classify clinical outcomes and subject attributes. We propose a single-index model wherein response variables and sparse functional connectivity network valued predictors are linked by an unspecified smooth function in order to accommodate potentially nonlinear relationships. We exploit the network structure of functional connectivity by imposing meaningful sparsity constraints, which lead not only to the identification of association of interactions between regions with the response but also the assessment of whether or not the functional connectivity associated with a brain region is related to the response variable. We demonstrate the effectiveness of the proposed model in simulation studies and in an application to a resting-state fMRI data set from the Human Connectome Project to model fluid intelligence and sex and to identify predictive links between brain regions.

Funder

National Institute of Health

Publisher

Oxford University Press (OUP)

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

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