A review of fMRI simulation studies

Area of Science:

• Neuroimaging
• Statistical modeling
• Computational neuroscience

Background:

• Validating statistical methods for functional Magnetic Resonance Imaging (fMRI) data is complex due to inherent data characteristics.
• A lack of standardized data-generating processes complicates the reliable assessment of fMRI analysis techniques.
• Existing simulation studies vary in their approaches to modeling Blood-Oxygen-Level-Dependent (BOLD) signals and noise.

Purpose of the Study:

• To critically evaluate the methodologies and reporting standards of existing fMRI simulation studies.
• To identify common shortcomings in the design and execution of fMRI simulation studies.
• To provide recommendations for improving the quality and rigor of fMRI simulation research.

Main Methods:

• A comprehensive literature search was conducted to identify relevant fMRI simulation studies.
• A database of these studies was compiled, focusing on simulation design parameters, data generation models, and reporting practices.
• The collected information was analyzed to assess the quality and completeness of the experimental designs and data acquisition considerations.

Main Results:

• Analysis revealed that many fMRI simulation studies lack a thorough experimental design.
• Crucial aspects of how fMRI data are acquired are frequently overlooked in simulation studies.
• Significant heterogeneity exists in the models used to generate simulated fMRI data, including BOLD activation and noise components.

Conclusions:

• The quality of fMRI simulation studies is often compromised by incomplete reporting and inadequate consideration of data acquisition principles.
• Improvements in simulation study design, including detailed reporting of experimental parameters and data generation processes, are necessary.
• Adopting best practices will enhance the reliability of statistical technique validation in fMRI research.