Cost Efficiency of fMRI Studies Using Resting-State Vs. Task-Based Functional Connectivity

Abstract

We investigate whether and how we can improve the cost efficiency of neuroimaging studies with well-tailored fMRI tasks. The comparative study is conducted using a novel network science-driven Bayesian connectome-based predictive method, which incorporates network theories in model building and substantially improves precision and robustness in imaging biomarker detection. The robustness of the method lays the foundation for identifying predictive power differentials across fMRI task conditions if such differences exist. When applied to a clinically heterogeneous transdiagnostic cohort, we find shared and distinct functional fingerprints of neuropsychological outcomes across seven fMRI conditions. For example, the emotional N-back memory task is found to be less optimal for negative emotion outcomes, and the gradual-onset continuous performance task is found to have stronger links with sensitivity and sociability outcomes than with cognitive control outcomes. Together, our results show that there are unique optimal pairings of task-based fMRI conditions and neuropsychological outcomes that should not be ignored when designing well-powered neuroimaging studies.