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Defining the face processing network: optimization of the functional localizer in fMRI.

Christopher J Fox¹, Giuseppe Iaria, Jason J S Barton

¹Graduate Program in Neuroscience, University of British Columbia, Vancouver, BC, Canada. cjfox@interchange.ubc.ca

Human Brain Mapping

|July 29, 2008

Summary

Dynamic facial images improve the identification of face-processing brain regions in functional magnetic resonance imaging (fMRI) studies. This method enhances the detection of face-selective regions-of-interest (ROIs) for individual subjects.

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Area of Science:

Neuroscience
Cognitive Neuroscience
Neuroimaging

Background:

Functional magnetic resonance imaging (fMRI) uses face versus object localizers to study face processing.
Current fMRI protocols struggle to reliably identify all core face processing regions in every subject using conservative statistical thresholds.
Inconsistent ROI size estimates arise from arbitrary threshold variations in fMRI studies.

Purpose of the Study:

To investigate if dynamic facial images enhance the detection of face-selective regions-of-interest (ROIs) in individual subjects.
To explore a method for determining statistically optimal ROI cluster size independent of thresholds.
To improve the sensitivity and specificity of characterizing face-related ROIs in fMRI.

Main Methods:

Utilized dynamic and static facial stimuli in fMRI localizer tasks.
Compared the effectiveness of dynamic versus static stimuli in identifying core and extended face processing systems.
Developed and applied a method to determine the optimal cluster size for ROIs based on maximum statistical face-selectivity.

Main Results:

Dynamic facial stimuli identified 98% of core and 69% of extended face processing ROIs, significantly outperforming static stimuli (72% core, 39% extended).
An optimal cluster size of approximately 50 mm³ was determined for key core face processing ROIs (FFA, OFA, pSTS).
The combination of dynamic stimuli and optimal cluster sizing increased both sensitivity and specificity in characterizing face-related ROIs.

Conclusions:

Dynamic facial stimuli are more effective than static stimuli for reliably identifying face-selective ROIs in individual subjects.
A threshold-independent method for determining optimal ROI cluster size enhances the accuracy of fMRI studies.
Combining dynamic localizers with optimal cluster size determination offers a more sensitive and specific approach to characterizing face-related brain regions.