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Updated: Jul 19, 2025

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A functional MRI pre-processing and quality control protocol based on statistical parametric mapping (SPM) and

Xin Di¹, Bharat B Biswal¹

¹Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States.

Frontiers in Neuroimaging

|August 9, 2023

Summary

This summary is machine-generated.

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.

Keywords:

functional MRI head motion pre-processing quality control resting-state skull stripping

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Published on: September 8, 2021

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.