[Paper Review] Preparing fMRI Data for Statistical Analysis

Alfonso Nieto-Castañón|arXiv (Cornell University)|Oct 24, 2022

Functional Brain Connectivity Studies47 citations

TL;DR

This chapter outlines standard preprocessing and denoising steps to prepare fMRI data for statistical analysis, including alignment, normalization, noise mitigation, and quality control.

ABSTRACT

This chapter describes several procedures used to prepare fMRI data for statistical analyses. It includes the description of common preprocessing steps, such as spatial realignment, coregistration, and spatial normalization, aimed at the spatial alignment of all fMRI data within- and between- subjects, as well as several denoising procedures aimed at minimizing the impact of common noise sources, including physiological and residual subject motion effects, on the BOLD signal time series. The chapter ends with a description of quality control procedures recommended for detecting potential problems in the fMRI data and evaluating its suitability for subsequent statistical analyses.

Motivation & Objective

• Motivate the need for careful preprocessing of fMRI data for reliable statistical analysis.
• Describe common preprocessing steps to achieve spatial alignment within and between subjects.
• Explain denoising procedures to minimize physiological and motion-related noise in BOLD signals.
• Outline quality control procedures to detect data problems and assess suitability for analysis.

Proposed method

• Describe spatial realignment to correct for head motion.
• Describe coregistration to align functional images to anatomical scans.
• Describe spatial normalization to a common template for group analyses.
• Discuss denoising approaches addressing physiological noise and residual motion effects.
• Present quality control procedures to evaluate data integrity and readiness for statistical analysis.

Experimental results

Research questions

• RQ1 What preprocessing steps are essential to align fMRI data spatially within and across subjects?
• RQ2 What denoising strategies effectively minimize physiological and motion-related noise in BOLD time series?
• RQ3 What quality control checks best indicate data suitability for subsequent statistical analyses?

Key findings

• Identified a set of standard preprocessing steps (realignment, coregistration, normalization) essential for accurate group analyses.
• Outlined denoising procedures to mitigate physiological noise and motion-related artifacts.
• Provided quality control guidelines to detect potential problems before statistical analysis.