Modeling group fMRI data
TL;DR: The need for a mixed effects model is motivated and the different stages of the mixed model used to analyze group fMRI data are outlined.
read more
Abstract: The analysis of group fMRI data requires a statistical model known as the mixed effects model. This article motivates the need for a mixed effects model and outlines the different stages of the mixed model used to analyze group fMRI data. Different modeling options and their impact on analysis results are also described.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
How reliable are the results from functional magnetic resonance imaging
TL;DR: This paper examines the many factors that influence the quality of acquired fMRI data and conducts a review of the existing literature to determine if some measure of agreement has emerged regarding the reliability of fMRI.
637
Six problems for causal inference from fMRI
Joseph D. Ramsey,Stephen José Hanson,Catherine Hanson,Yaroslav O. Halchenko,Russell A. Poldrack,Clark Glymour +5 more
TL;DR: Combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects are described.
404
Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.
TL;DR: The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of theGLM framework, and on the methods that have been developed to correct for any violation of those assumptions.
Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives
James M. Shine,James M. Shine,Oluwasanmi Koyejo,Peter T. Bell,Krzysztof J. Gorgolewski,Moran Gilat,Russell A. Poldrack +6 more
TL;DR: This paper introduces the 'Multiplication of Temporal Derivatives' (MTD) and demonstrates the utility of this metric to detect dynamic changes in connectivity using data from a novel state-switching simulation; accurately estimate graph structure in a previously-described 'ground-truth' simulated dataset; and identify task-driven alterations in functional connectivity.
189
MVPA-Light: A Classification and Regression Toolbox for Multi-Dimensional Data.
TL;DR: The toolbox computes various classification and regression metrics and establishes their statistical significance, is modular and easily extendable, and offers interfaces for LIBSVM and LIBLINEAR as well as an integration into the FieldTrip neuroimaging toolbox.
References
•Book
Linear Mixed Models for Longitudinal Data
Geert Verbeke,Geert Molenberghs +1 more
- 26 Mar 2013
TL;DR: Using data of 955 men, Brant et al showed that the average rates of increase of systolic blood pressure (SBP) are smallest in the younger age groups, and greatest in the older agegroups, and that obese individuals tend to have a higher SBP than non-obese individuals.
4.1K
Temporal autocorrelation in univariate linear modeling of FMRI data.
TL;DR: Estimation is improved by using nonlinear spatial filtering to smooth the estimated autocorrelation, but only within tissue type, and reduced bias to close to zero at probability levels as low as 1 x 10(-5).
3K
Linear Mixed Models for Longitudinal Data
TL;DR: This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data and attempts to target applied statisticians and biomedical researchers in industry, public health organizations, contract research organizations, and academia.
2.5K
Multilevel linear modelling for FMRI group analysis using Bayesian inference.
Mark W. Woolrich,Mark W. Woolrich,Timothy E.J. Behrens,Timothy E.J. Behrens,Christian F. Beckmann,Christian F. Beckmann,Mark Jenkinson,Stephen M. Smith +7 more
TL;DR: This work introduces to neuroimage modelling the approach of reference priors, which drives the choice of prior such that it is noninformative in an information-theoretic sense, and proposes two inference techniques at the top level for multilevel hierarchies.
1.8K
General multilevel linear modeling for group analysis in FMRI.
TL;DR: It is demonstrated that by taking into account lower-level covariances and heterogeneity a substantial increase in higher-level Z score is possible, and this result has significant implications for group studies in FMRI.
1.5K