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Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

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Stimulus Dependent Dynamic Reorganization of the Human Face Processing Network.

Gideon Rosenthal^1,2, Olaf Sporns³, Galia Avidan^1,2,4

¹Department of Brain and Cognitive Sciences, Ben-Gurion University of the Negev, PO Box 653, Beer-Sheva 8410501, Israel.

Cerebral Cortex (New York, N.Y. : 1991)

|September 14, 2016

Summary

The brain

Keywords:

dynamic connectivity fMRI face processing functional connectivity network analysis

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Area of Science:

Neuroscience
Cognitive Neuroscience
Brain Imaging

Background:

Face perception relies on specialized brain networks.
The face inversion effect, where inverted faces are harder to recognize, highlights unique face processing mechanisms.
Understanding the dynamic functional connectivity within and between brain networks is crucial for elucidating face representation.

Purpose of the Study:

To investigate the network mechanisms underlying face representation using the face inversion effect.
To track stimulus-dependent dynamic functional connectivity in brain networks processing upright and inverted faces.
To develop and apply a novel dynamic general linear model (GLM) approach for analyzing functional magnetic resonance imaging (fMRI) data.

Main Methods:

Utilized functional magnetic resonance imaging (fMRI) to monitor brain activity.
Employed a novel dynamic general linear model (GLM) framework adapted for fMRI connectivity analysis.
Applied network decomposition techniques to assess stimulus-dependent dynamic connectivity patterns.

Main Results:

Demonstrated complementary roles for face and non-face networks under face inversion.
Identified stimulus-dependent dynamic connectivity patterns correlating with the behavioral face inversion effect.
Established a network-level signature for the face inversion effect, revealing functional reorganization in brain networks.

Conclusions:

A simple physical transformation (inversion) of a face stimulus significantly reorganizes functional connectivity across brain networks.
The developed dynamic GLM network analysis offers a generalizable framework for studying stimulus-dependent connectivity in neuroimaging.
This approach provides insights into the dynamic nature of brain network function during complex cognitive tasks like face perception.