The Statistical Analysis of fMRI Data.

By Martin A. Lindquist. 2008

815 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ee7f867739a0d638997bb0f63b6ad18a38bd5be7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Practical Sequential Stopping Rule for High-Dimensional Markov Chain Monte Carlo (2016, methodology): “Data were preprocessed using standard techniques such as slice timing and spatial smoothing (for a review see Lindquist 2008) and were registered in standardized space with 64 × 64 × 8 dimensions for 54 time points.”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, background): “This, subse-1055 quently, leads to a complex connectivity structure, which comprises of linear and nonlinear patterns and is contaminated with noise (Thirion and Faugeras, 2004; Lindquist, 2008).”
ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data (2019, neutral): “Functional Magnetic Resonance Imaging (fMRI) is a brain imaging technique that is used for studying brain activities [29], [30].”
Dynamic connectivity regression: Determining state-related changes in brain connectivity (2012, background): “Traditionally, these studies have focused on locating brain regions showing task-related changes in neural activity, for example, greater activity during an experimental task than during a baseline state (Lindquist, 2008).”
Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks (2017, background): “Each of these voxels represents the fluctuation of the BOLD intensity of thousands of neurons over time [6], [7].”
Functional Causal Mediation Analysis With an Application to Brain Connectivity (2012, methodology): “Functional MRI measures changes in blood ﬂow and oxygenation in the brain in response to neural stimuli, thereby providing a means to non-invasively study changes in mental activity in response to a certain task (Lindquist, 2008).”
ECA: High-Dimensional Elliptical Component Analysis in Non-Gaussian Distributions (2013, methodology): “For example, it is one of the most useful techniques for data visualization in studying brain imaging data (Lindquist 2008).”
Fast transient networks in spontaneous human brain activity (2014, methodology): “This design matrix, together with the full-rank (before whitening) source space data, was used in a GLM analysis (Friston et al., 1996; Brookes et al., 2004; Woolrich et al., 2009).”
Tests for separability in nonparametric covariance operators of random surfaces (2015, background): “Examples abound and include images from medical devices such as MRI (Lindquist 2008) or PET (Worsley et al. 1996), spectrograms ∗Research Supported by EPSRC grant EP/K021672/2.”
Tensor Regression with Applications in Neuroimaging Data Analysis (2012, background): “…function of the human brains and their connection with neuropsychiatric and neurodegenerative disorders is one of the most intriguing scientiﬁc questions (Towle et al., 1993; Niedermeyer and da Silva, 2004; Buzsaki, 2006; Fass, 2008; Lindquist, 2008; Lazar, 2008; Friston, 2009; Kang et al., 2012).”
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data (2019, background): “Functional magnetic resonance imaging (fMRI), a non-invasive measure of neural activation, has been paramount in advancing our understanding of the functional organization of the brain [21].”
Evaluating Dynamic Bivariate Correlations in Resting-state fMRI: A comparison study and a new approach (2014, neutral): “These techniques model covariance in an analogous manner as fMRI noise is modeled in standard GLM analysis using more conventional time series analysis techniques such as autoregressive (AR) and autoregressive moving-average (ARMA) models (Lindquist, 2008; Purdon et al., 2001).”