Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Functional Connectivity Analysis

Background:

• Independent Component Analysis (ICA) is a popular method for resting state fMRI data analysis due to its model-free and multivariate approach.
• Unlike seed-based analysis, ICA evaluates all brain resting state networks (RSNs) simultaneously, retaining magnitude information crucial for detecting subtle connectivity changes.

Purpose of the Study:

• To investigate the dual regression approach for assessing group differences in resting state functional connectivity within brain networks.
• To demonstrate how ignoring amplitude effects and excessive motion can corrupt connectivity maps and lead to spurious findings.

Main Methods:

• Utilized group ICA and the dual regression technique to analyze resting state fMRI data.
• Investigated the impact of amplitude effects and motion on connectivity maps using simulated and in vivo data from healthy subjects and patients with bipolar disorder and schizophrenia.

Main Results:

• Ignoring amplitude effects and excessive motion corrupt connectivity maps, resulting in spurious differences.
• Implementing dual regression to retain amplitude information and using strict motion criteria are essential for accurate resting state connectivity analyses.
• Dual regression outputs can be used to identify potential motion effects.

Conclusions:

• Dual regression is a valuable technique for retaining amplitude information in resting state connectivity analyses.
• Strict motion criteria are critical for controlling motion-related amplitude effects and ensuring the reliability of connectivity findings.
• Accurate resting state functional connectivity analysis requires methods that preserve magnitude information and control for motion artifacts.