Comparison between gradients and parcellations for functional connectivity prediction of behavior

Ru Kong, Yanrui Tan, Naren Wulan, Leon Qi Rong Ooi, Seyedeh-Rezvan Farahibozorg, Samuel J. Harrison, Janine D. Bijsterbosch, Boris C. Bernhardt, Simon B. Eickhoff, B. T. Thomas Yeo

Abstract: Highlights•Different parcellation and gradient approaches were compared for RSFC prediction of a broad range of behavioral measures in two datasets.•Across two regression algorithms (LRR and KRR), individual-specific hard-parcellation performed the best in the HCP dataset.•Principal gradients and all parcellation approaches performed similarly in the ABCD dataset.•Principal gradient approach required at least 40 to 60 gradients in order to perform as well as parcellation approaches.