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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: Sep 11, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Spurious correlations in surface-based functional brain imaging.

Jayson Jeganathan^1,2, Nikitas C Koussis^2,3,4, Bryan Paton^1,2,3

¹School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, NSW, Australia.

Imaging Neuroscience (Cambridge, Mass.)

|August 13, 2025

Summary

This summary is machine-generated.

Processing functional MRI data on surface meshes can introduce "gyral bias" due to uneven vertex spacing. This bias distorts results in common neuroimaging analyses, impacting functional connectivity and reliability studies.

Keywords:

bias fMRI parcellation surface

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Published on: March 21, 2019

Area of Science:

Neuroimaging
Computational Neuroscience
Brain Mapping

Background:

Functional MRI (fMRI) analysis increasingly uses surface-based vertex mapping.
Common processing pipelines create meshes with uneven vertex spacing, denser in sulci than gyri.
This uneven spacing, termed 'gyral bias,' is not fully understood but impacts fMRI data.

Purpose of the Study:

To explain the origins of gyral bias in fMRI surface mapping.
To illustrate the impact of gyral bias on fMRI data using in-silico models.
To identify common analyses affected by gyral bias and propose solutions.

Main Methods:

Developed in-silico models of fMRI data to simulate gyral bias.
Analyzed the effects of uneven vertex spacing on simulated fMRI time series.
Evaluated the impact of gyral bias on various neuroimaging analysis techniques.

Main Results:

Gyral bias leads to spurious correlations and anatomical folding information leakage into fMRI time series.
Common analyses like test-retest reliability, fingerprinting, and functional parcellations are significantly affected.
The onavg template reduces but does not eliminate gyral bias, showing residual vertex spacing variability.

Conclusions:

Gyral bias is a critical artifact in surface-based fMRI analysis that can lead to erroneous conclusions.
Understanding and addressing gyral bias is essential for accurate interpretation of functional brain data.
Recommendations are provided to mitigate or correct for gyral bias in fMRI studies.