Abstract
"Resting-state" or task-free fMRI can assess intrinsic connectivity network (ICN) integrity in health and disease, suggesting a potential for use of these methods as disease-monitoring biomarkers. Numerous analytical options are available, including model-driven ROI-based correlation analysis and model-free, independent component analysis (ICA). High test-retest reliability will be a necessary feature of a successful ICN biomarker, yet available reliability data remains limited. Here, we examined ICN fMRI test-retest reliability in 24 healthy older subjects scanned roughly one year apart. We focused on the salience network, a disease-relevant ICN not previously subjected to reliability analysis, as well as the default mode network. Most ICN analytical methods proved reliable (intraclass coefficients>0.4) and were further improved by wavelet analysis. Seed-based ROI correlation analysis showed high scan-wise reliability, whereas graph theoretical analysis and temporal concatenation group ICA proved most reliable at the individual unit-wise level (voxels, ROIs). Including global signal regression in ROI-based correlation analyses reduced reliability. Our study provides a direct comparison between the most commonly used ICN fMRI methods and potential guidelines for measuring intrinsic connectivity in aging control and patient populations over time.