Inter-subject dynamic conditional correlation: A novel method to track the framewise network implication during naturalistic stimuli.

Abstract

Background: Naturalistic stimuli have become increasingly popular in modern cognitive neuroscience. These stimuli have high ecological validity due to their rich and multi-layered features. However, their complexity also presents methodological challenges for uncovering neural network reconfiguration. Dynamic functional connectivity using the sliding-window technique is commonly used but has several limitations. In this study, we introduce a new method called inter-subject dynamic conditional correlation (ISDCC).

Method: ISDCC employs inter-subject analysis to remove intrinsic and non-neuronal signals, retaining only inter-subject-consistent stimuli-induced signals. It then applies dynamic conditional correlation (DCC) based on the generalized autoregressive conditional heteroskedasticity to calculate the framewise functional connectivity. To validate ISDCC, we analyzed simulation data with known network reconfiguration patterns and two publicly available narrative fMRI datasets.

Results: 1) ISDCC accurately unveiled the underlying network reconfiguration patterns in simulation data, demonstrating greater sensitivity than DCC; 2) ISDCC identified synchronized network reconfiguration patterns across listeners; 3) ISDCC effectively differentiated between stimulus types with varying temporal coherence; 4) network reconfigurations unveiled by ISDCC were significantly correlated with listener engagement during narrative comprehension.

Conclusion: ISDCC is a precise and dynamic method for tracking network implications in response to naturalistic stimuli.