Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior

Resting-state functional connectivity (RSFC) has been widely used to predict behavioral measures. To predict behavioral measures, there are different approaches for representing RSFC with parcellations and gradients being the two most popular approaches. There is limited comparison between parcellation and gradient approaches in the literature. Here, we compared different parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we considered group-average “hard” parcellations (Schaefer et al., 2018), individual-specific “hard” parcellations (Kong et al., 2021a), and an individual-specific “soft” parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we considered the well-known principal gradients derived from diffusion embedding (Margulies et al., 2016), and the local gradient approach that detects local changes in RSFC across the cortex (Laumann et al., 2015). Across two regression algorithms (linear ridge regression and kernel ridge regression), we found that individual-specific hard-parcellation performed the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average “hard” parcellations exhibited similar performance. On the other hand, principal gradients and all parcellation approaches performed similarly in the ABCD dataset. Across both datasets, local gradients performed the worst. Finally, we found that the principal gradient approach required at least 40 to 60 gradients in order to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients could provide significant behaviorally relevant information.