# Preparing fMRI Data for Statistical Analysis

**Authors:** Alfonso Nieto-Castanon  
**arXiv:** [2210.13564](https://arxiv.org/abs/2210.13564)  
**Date:** 2022-10-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2210.13564v1/

## Key points

- Alfonso Nieto-Castanon provides a resource for researchers working with fMRI data
- Various procedures are described to prepare fMRI data for statistical analyses
- Recommendations for quality control procedures to detect potential problems in the data are offered
- Common preprocessing steps such as spatial realignment, coregistration and spatial normalization are discussed
- Several denoising techniques are presented to reduce the impact of noise sources on the BOLD signal time series
- The guide is invaluable for researchers who want accurate results from their statistical analyses.

## Summary

In this chapter, Alfonso Nieto-Castanon provides an essential resource for researchers working with functional magnetic resonance imaging (fMRI) data. He describes the various procedures used to prepare fMRI data for statistical analyses and offers recommendations for quality control procedures to detect potential problems in the data. Common preprocessing steps such as spatial realignment, coregistration and spatial normalization are discussed in order to align fMRI data within- and between-subjects. Additionally, several denoising techniques are presented that aim to reduce the impact of noise sources on the blood oxygen level-dependent (BOLD) signal time series. This comprehensive guide is invaluable for researchers who want to ensure accurate results from their statistical analyses.