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Related Experiment Video

Updated: Sep 27, 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

Harmonizing functional connectivity reduces scanner effects in community detection.

Andrew A Chen¹, Dhivya Srinivasan², Raymond Pomponio³

¹Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.

|April 14, 2022

Summary

This summary is machine-generated.

Scanner effects in brain imaging data can skew results. This study introduces new methods to harmonize functional connectivity, reducing scanner variability and improving the reliability of brain community structure analysis.

Keywords:

Brain networks Community detection Functional connectivity Harmonization Network analyses Site effects

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Area of Science:

Neuroimaging
Brain Network Analysis
Data Harmonization

Background:

Functional magnetic resonance imaging (fMRI) studies rely on graph theory for brain functional organization insights.
Multi-scanner datasets introduce scanner effects, impacting network metrics and community detection.
Existing harmonization methods may not fully address scanner-induced variability in brain connectivity.

Purpose of the Study:

To identify and quantify scanner effects in community detection and network metrics from fMRI data.
To evaluate a standard harmonization technique for functional connectivity.
To develop and validate novel methods for harmonizing functional connectivity, accounting for network structure and data covariance.

Main Methods:

Analysis of scanner effects on data-driven community detection and network metrics in multi-scanner fMRI datasets.
Assessment of a prevalent functional connectivity harmonization approach.
Development of new harmonization techniques leveraging prior network knowledge and covariance patterns.

Main Results:

Scanner effects were confirmed to significantly impact brain community structure and network metrics.
The proposed novel harmonization methods demonstrated a reduction in scanner effects.
The new methods improved the consistency of community structure and network metrics across different scanners.

Conclusions:

Scanner effects pose a significant challenge in large-scale fMRI studies of brain organization.
The developed harmonization techniques offer improved tools for addressing scanner variability in functional connectivity.
These methods can enhance the reliability and prevent spurious findings in future functional connectivity research.