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Framing Effects

Framing Effects

Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...

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Related Experiment Video

Updated: Jun 12, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Intersubject Dynamic Conditional Correlation: A Novel Method to Track the Framewise Network Implication during

Lifeng Chen^1,2, Shiyao Tan^1,2, Chaoqun Li^1,2

¹Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China.

Brain Connectivity

|September 20, 2024

Summary

This summary is machine-generated.

A new method, intersubject dynamic conditional correlation (ISDCC), precisely tracks brain network changes during naturalistic stimuli. This approach enhances understanding of cognitive neuroscience by revealing synchronized neural activity and listener engagement.

Keywords:

dynamic conditional correlation fMRI intersubject analysis naturalistic stimuli

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Area of Science:

Cognitive Neuroscience
Neuroimaging Analysis

Background:

Naturalistic stimuli offer high ecological validity in cognitive neuroscience but pose methodological challenges.
Existing dynamic functional connectivity methods, like sliding-window, have limitations in analyzing complex neural data.

Purpose of the Study:

Introduce and validate a novel method, intersubject dynamic conditional correlation (ISDCC), for analyzing neural network reconfiguration.
To overcome limitations of current methods in capturing dynamic brain activity in response to naturalistic stimuli.

Main Methods:

ISDCC employs intersubject analysis to isolate consistent, stimulus-evoked neural signals.
Dynamic Conditional Correlation (DCC) based on generalized autoregressive conditional heteroskedasticity is used for framewise functional connectivity calculation.
Validation involved simulation data and functional Magnetic Resonance Imaging (fMRI) datasets of narrative comprehension.

Main Results:

ISDCC accurately identified network reconfiguration patterns in simulations, outperforming standard DCC.
Synchronized network reconfiguration patterns were observed across participants.
The method differentiated brain activity based on stimulus temporal coherence.
Network changes correlated significantly with listener engagement during narrative tasks.

Conclusions:

ISDCC provides a sensitive and dynamic approach for analyzing brain network dynamics.
This method enhances the study of neural responses to ecologically valid, naturalistic stimuli.
ISDCC offers precise tracking of network implications during complex cognitive processes.