A functional MRI pre-processing and quality control protocol based on statistical parametric mapping (SPM) and MATLAB
1Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States.
Abstract:
Functional MRI (fMRI) has become a popular technique to study brain functions and their alterations in psychiatric and neurological conditions. The sample sizes for fMRI studies have been increasing steadily, and growing studies are sourced from open-access brain imaging repositories. Quality control becomes critical to ensure successful data processing and valid statistical results. Here, we outline a simple protocol for fMRI data pre-processing and quality control based on statistical parametric mapping (SPM) and MATLAB. The focus of this protocol is not only to identify and remove data with artifacts and anomalies, but also to ensure the processing has been performed properly. We apply this protocol to the data from fMRI Open quality control (QC) Project, and illustrate how each quality control step can help to identify potential issues. We also show that simple steps such as skull stripping can improve coregistration between the functional and anatomical images.
Insights
This study presents a simple protocol for functional MRI (fMRI) data pre-processing and quality control (QC) to ensure valid results. The protocol helps identify artifacts and ensures proper data processing, improving image coregistration.
Area of Science:
- Neuroimaging
- Brain Function Analysis
- Psychiatric and Neurological Disorders
Background:
- Functional MRI (fMRI) is increasingly used to study brain function and its alterations in various conditions.
- Growing fMRI studies utilize open-access repositories, necessitating robust quality control.
- Ensuring data quality is critical for reliable processing and valid statistical outcomes in fMRI research.
Purpose of the Study:
- To outline a straightforward protocol for fMRI data pre-processing and quality control.
- To identify and remove fMRI data containing artifacts and anomalies.
- To verify the proper execution of the fMRI data processing pipeline.
Main Methods:
- Utilized Statistical Parametric Mapping (SPM) and MATLAB for data pre-processing and quality control.
- Applied the protocol to data from the fMRI Open Quality Control (QC) Project.
- Demonstrated the effectiveness of individual QC steps in identifying potential data issues.
Main Results:
- The developed protocol effectively identifies and removes fMRI data with artifacts.
- The protocol ensures that data processing steps have been performed correctly.
- Simple procedures like skull stripping were shown to enhance the coregistration of functional and anatomical MRI images.
Conclusions:
- A simple, effective protocol for fMRI pre-processing and quality control is presented.
- This protocol aids in ensuring the integrity of fMRI data for research.
- The findings highlight the importance of rigorous QC in neuroimaging studies and suggest improvements like skull stripping for better image alignment.
