The Statistical Analysis of fMRI Data.

@article{Lindquist2008TheSA,
  title={The Statistical Analysis of fMRI Data.},
  author={Martin A. Lindquist},
  journal={Statistical Science},
  year={2008},
  volume={23},
  pages={439-464}
}
  • M. Lindquist
  • Published 1 November 2008
  • Mathematics, Biology
  • Statistical Science
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… 

Figures from this paper

Bayesian Models for fMRI Data Analysis.
TLDR
This paper provides a review of the most relevant models developed in recent years for fMRI data, dividing methods according to the objective of the analysis and addressing the very important problem of estimating brain connectivity.
An Introduction to the Analysis of Functional Magnetic Resonance Imaging Data
TLDR
The reader is introduced to the rich and diverse literature in the fascinating field of fMRI data analysis, providing an overview of its main challenges and of the most common approaches to overcome them.
A Survey of FMRI Data Analysis Methods
TLDR
The preprocessing and several methods of analyzing the fMRI data is examined and it is shown that machine learning methods have been developed and trained to use f MRI data as input and aid medical professionals for diagnostic purposes.
Bayesian models for functional magnetic resonance imaging data analysis
TLDR
A review of the most relevant models developed in recent years for fMRI data, starting from spatiotemporal models for f MRI data that detect task-related activation patterns and addressing the very important problem of estimating brain connectivity.
Modelling the data and not the images in FMRI
The standard approach to the analysis of functional magnetic resonance imaging (FMRI) data applies various preprocessing steps to the original FMRI. These preprocessings lead to a general
LISA improves statistical analysis for fMRI
TLDR
A new fMRI analysis tool, LISA, is introduced, which provides increased statistical power compared to existing techniques and allows to find small activation areas that have previously evaded detection.
Case for fMRI data repositories
  • S. Iyengar
  • Computer Science, Medicine
    Proceedings of the National Academy of Sciences
  • 2016
TLDR
The performance of three software packages on datasets made available through sharing agreements are assessed, with a sobering tale of Family-wise error (FWE) rates that should be about 5% can be much higher and there was a bug in software that led to inflated error rates for 15 y.
Methods for the Analysis of Missing Data in FMRI Studies
TLDR
Missing data in fMRI studies can undermine the benefits provided by high quality imaging technology used to generate data testing predictions about brain function, because statistic maps with applied effect size or significance thresholds do not typically include information about which voxels were omitted from analyses.
Statistical Analysis of Functional MRI Data using Independent Component Analysis
TLDR
This work was to analyze the same fMRI data using Independent Component Analysis (ICA) and compare the results with those obtained through GLM, showing that ICA was able to find more active networks than GLM.
Statistical approaches for resting state fMRI data analysis
TLDR
Novel approaches are introduced, discussed and validated on simulated data and on real datasets, in health and disease, in order to track modulation of brain dynamics and HRF across different pathophysiological conditions.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 117 REFERENCES
THE ACQUISITION AND STATISTICAL ANALYSIS OF RAPID 3D FMRI DATA
TLDR
A new approach toward the acquisition and statistical analysis of fMRI data based on repeatedly measuring the low spatial frequencies present in the MR signal, which allows for a low spatial resolution snapshot of the brain with extremely high temporal resolution, which will become an important tool for studying any cognition task which involves rapid mental processing in more than one region.
Bayesian spatiotemporal inference in functional magnetic resonance imaging.
TLDR
The aim of this article is to present hierarchical Bayesian approaches that allow one to simultaneously incorporate temporal and spatial dependencies between pixels directly in the model formulation.
Change point estimation in multi-subject fMRI studies
TLDR
This paper assumes that the timing of a subject's activation onset and duration are random variables drawn from unknown population distributions and proposes a technique for estimating these distributions assuming no functional form, and allowing for the possibility that some subjects may show no response.
Spatio-temporal modeling of localized brain activity.
  • F. Bowman
  • Computer Science, Medicine
    Biostatistics
  • 2005
TLDR
This work proposes a two-stage spatio-temporal model that protects against type-I errors, enables the detection of both localized and regional activations, provides information on functional connectivity in the brain, and establishes a framework to produce spatially smoothed maps of distributed brain activity for each individual.
Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate
TLDR
This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR) and demonstrates this approach using both simulations and functional magnetic resonance imaging data from two simple experiments.
A Bayesian hierarchical framework for spatial modeling of fMRI data
TLDR
This work applies the Bayesian hierarchical model to two novel fMRI data sets: one considering inhibitory control in cocaine-dependent men and the second considering verbal memory in subjects at high risk for Alzheimer's disease.
Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling
TLDR
The results show that it is surprisingly difficult to accurately recover true task-evoked changes in BOLD signal and that there are substantial differences among models in terms of power, bias and parameter confusability.
Modeling state-related fMRI activity using change-point theory
TLDR
This work introduces a new analysis approach that allows the predicted signal to depend non-linearly on the input, and develops a group analysis using a hierarchical model, which is a multi-subject extension of the exponentially weighted moving average (EWMA) method used in change-point analysis.
RESIDUAL ANALYSIS FOR DETECTING MIS-MODELING IN fMRI
The voxel-wise general linear model (GLM) approach has arguably be- come the dominant way to analyze functional magnetic resonance imaging (fMRI) data. The approach relies on specifying predicted
A unified framework for group independent component analysis for multi-subject fMRI data
TLDR
A class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases are considered and a maximum likelihood (ML) approach with a modified Expectation-Maximization (EM) algorithm is proposed.
...
1
2
3
4
5
...