Preprocessing strategy influences graph-based exploration of altered functional networks in major depression

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Psychiatry

Background:

• Resting-state functional magnetic resonance imaging (fMRI) is widely used for neuropsychiatric disorder research.
• Graph metrics from functional connectivity reveal disease-related variations in disorders like major depressive disorder (MDD).
• Diverse analytical methods in resting-state fMRI introduce variability and methodological challenges.

Purpose of the Study:

• To systematically compare different preprocessing strategies for resting-state fMRI.
• To evaluate the influence of preprocessing choices on group differences between healthy controls (HC) and MDD patients.
• To identify optimal preprocessing parameters for detecting neurobiological differences in MDD.

Main Methods:

• Investigated effects of global mean-signal regression (GMR), temporal filtering, detrending, and network sparsity.
• Analyzed group differences in global and nodal graph theoretical metrics between HC and MDD patients.
• Systematically varied preprocessing parameters and sparsity thresholds (16-22%) to assess impact on network topology.

Main Results:

• Group differences in global graph metrics were largely absent across most preprocessing variants.
• Differences in local graph metrics were sparse, variable, and highly dependent on preprocessing and sparsity.
• Sparsity thresholds between 16% and 22% showed the greatest potential for revealing group differences.

Conclusions:

• Methodological decisions in resting-state fMRI preprocessing critically influence findings in MDD research.
• Specific preprocessing combinations and sparsity thresholds are essential for reliable detection of group differences.
• Findings highlight the need for careful consideration of analytical choices to understand MDD neurobiology.