A resting-state connectivity metric independent of temporal signal-to-noise ratio and signal amplitude

Temporal signal-to-noise ratio (tSNR) and the amplitude of low-frequency resting-state fluctuations (signal amplitude [SA]) can vary between magnetic resonance imaging sessions, thereby decreasing the reliability and reproducibility of measurements of resting-state connectivity between regions of interest (ROIs) in the human brain. In this study, a new metric for quantifying the strength of resting-state connections is introduced, which possesses low sensitivity to tSNR and SA but maintains high sensitivity to expected changes in connectivity magnitude or region volume caused by the presence of neurological disease, for example. This new metric is one that essentially divides the temporal cross-correlation of two ROIs by the temporal cross-correlation of one the ROIs with itself (i.e., a relative connectivity [RelCon]). The robustness of the new metric is demonstrated and compared with several existing metrics, using simulated datasets of varying tSNR and SA, as well as in data collected over multiple sessions from healthy subjects. For both simulated and real datasets, relative connectivity exhibited lower sensitivity to tSNR and SA compared with existing (i.e., absolute) connectivity metrics. Further, simulation suggests that for RelCon, it is better to calculate the correlation between all possible pairs of ROI voxel signals and then appropriately average the correlation coefficients, whereas for absolute connectivity it is better to average signals within the ROIs and then determine the correlation between the averaged signals. RelCon permits the comparison of connectivity across datasets acquired with different scanners or imaging parameters that potentially generate data with differing tSNR and SA.