On the relationship between seed-based and ICA-based measures of functional connectivity
Suresh E Joel1, Brian S Caffo, Peter C M van Zijl
1Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA. sejoel@mri.jhu.edu
Magnetic Resonance in Medicine
|March 12, 2011
Summary
This study clarifies the relationship between two brain functional connectivity (FC) analysis methods. Seed-based FC is mathematically equivalent to the sum of within- and between-network connectivities derived from spatial independent component analysis.
Area of Science:
- Neuroimaging
- Cognitive Neuroscience
- Systems Neuroscience
Background:
- Brain functional connectivity (FC) quantifies inter-regional synchrony in BOLD-fMRI signals.
- FC analysis is crucial for understanding brain function in task and resting states, linking to behavior and diagnosis.
- Two primary FC methods are seed-based analysis and spatial independent component analysis (ICA).
Purpose of the Study:
- To elucidate and illustrate the quantitative relationship between seed-based and ICA-derived FC measures.
- To demonstrate that seed-based FC is a composite of ICA-derived network connectivities.
Main Methods:
- Temporal correlation analysis using a seed region of interest.
- Spatial independent component analysis (ICA) for data-driven network identification.
- A simulation study and a visuomotor task experiment were conducted.
Main Results:
- Seed-based FC measures were shown to be the sum of ICA-derived within-network and between-network connectivities.
- This relationship was confirmed both qualitatively and quantitatively through simulation and experimental data.
- The findings highlight the mathematical linkage between the two distinct FC methodologies.
Conclusions:
- Seed-based and ICA approaches to FC analysis are fundamentally related.
- Understanding this relationship aids in the interpretation of FC findings across different analytical frameworks.
- This work provides a unified perspective on interpreting brain connectivity patterns.
