Double Layers of Causal Inference: Application to fMRI Effective Connectivity Research

Music City Center 

Effective connectivity (EC) research investigates whether and to what extent functional activity in one brain region causally influences another. Recent studies have shown a growing interest in the effects of external intervention on subject-level connectivity. This introduces two layers of causal inference problems: causal relationships among brain regions and the effect of an external intervention on those relationships. Each layer is susceptible to distinct or shared confounding factors. To address confounding in estimating EC, we propose using a sample splitting method for time-series data and then fitting a vector autoregressive model. With the estimated EC, we develop an inverse probability weighting estimator to examine the intervention effect on EC while adjusting for subject-level confounding and multiple testing. We demonstrate, both in theory and simulations, that the proposed method is asymptotically valid under certain conditions, effectively controlling type-I error rates and familywise error rates. We apply this approach to resting-state fMRI data from the Alzheimer's Disease Neuroimaging Initiative.

causal inference

effective connectivity

fMRI

Alzheimer’s disease 

Korean International Statistical Society