Intersubject 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 multilayered 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 intersubject dynamic conditional correlation (ISDCC).

Method:

ISDCC uses intersubject analysis to remove intrinsic and non-neuronal signals, retaining only intersubject-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 functional Magnetic Resonance Imaging (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; and (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.

Impact statement

A novel model-based method, intersubject dynamic conditional correlation (ISDCC), was introduced to track the framewise network implication during naturalistic stimuli. First, ISDCC accurately unveiled underlying network reconfiguration patterns in simulated task-fMRI data. Second, ISDCC effectively differentiated between stimulus types with varying temporal coherence. Third, ISDCC unveiled correlations between the activity of the default mode network (DMN) and listener engagement during narrative comprehension, shedding light on how the DMN accumulates and integrates information during narrative comprehension. These findings provide valuable insights for researchers investigating the network processes involved in responding to naturalistic stimuli.