Resolution and Reliability in Functional Connectivity Analysis

Increasingly, researchers are interested in the functional connectivity between different brain regions of resting-state imaging. However, quantifying the reliability of measures of both brain function and structure is difficult, while reliability is essential to accurately detect differences between subjects or groups and predict outcomes. Here, we seek to evaluate the reliability and prediction performance of functional connectivity networks obtained using independent components analysis (ICA) with varying number of nodes. Reliability is measured by intra-class correlation coefficient (ICC), image intra-class correlation coefficient (I2C2) and Kolmogorov–Smirnov (KS) test. In particular, we evaluated how the number of components influence the results. We found the local (ICC) and global (I2C2) reliability have different trends with the change in the resolution of the ICA-based pacellation, and higher reliability was achieved on sessions that took place on the same day compared to different days for both ICC and I2C2. Individual patterns of functional connectivity do exist. First, KS test shown greater similarity between different sessions than different subjects. Second, fingerprinting and prediction of sex using connectivity matrix yield high prediction accuracy (over 90%). In addition, a moderate resolution of the parcellation seems to be an optimal choice. It provides more information about functional connectivity, improves the performance of prediction and avoids unnecessary computational cost, which only contribute little improvement to prediction accuracy