The Statistical Analysis of fMRI Data

TL;DR

This paper reviews the statistical methods used in analyzing fMRI data, highlighting current practices, challenges, and future opportunities for statistical contributions in neuroimaging research.

Contribution

It provides a comprehensive overview of statistical techniques in fMRI analysis and identifies areas where statistical methods can be further developed and applied.

Findings

Statistics are essential for interpreting noisy fMRI data.

Current methods address brain activity localization and connectivity inference.

Future statistical approaches can improve neuroimaging insights.

Abstract

In recent years there has been explosive growth in the number of neuroimaging studies performed using functional Magnetic Resonance Imaging (fMRI). The field that has grown around the acquisition and analysis of fMRI data is intrinsically interdisciplinary in nature and involves contributions from researchers in neuroscience, psychology, physics and statistics, among others. A standard fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. Statistics plays a crucial role in understanding the nature of the data and obtaining relevant results that can be used and interpreted by neuroscientists. In this paper we discuss the analysis of fMRI data, from the initial acquisition of the raw data to its use in locating brain activity, making inference about brain connectivity and predictions about psychological or disease states. Along the way, we illustrate interesting and important issues where statistics already plays a crucial role. We also seek to illustrate areas where statistics has perhaps been underutilized and will have an increased role in the future.