5,764 research outputs found
Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.
Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. 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 the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making
A group model for stable multi-subject ICA on fMRI datasets
Spatial Independent Component Analysis (ICA) is an increasingly used
data-driven method to analyze functional Magnetic Resonance Imaging (fMRI)
data. To date, it has been used to extract sets of mutually correlated brain
regions without prior information on the time course of these regions. Some of
these sets of regions, interpreted as functional networks, have recently been
used to provide markers of brain diseases and open the road to paradigm-free
population comparisons. Such group studies raise the question of modeling
subject variability within ICA: how can the patterns representative of a group
be modeled and estimated via ICA for reliable inter-group comparisons? In this
paper, we propose a hierarchical model for patterns in multi-subject fMRI
datasets, akin to mixed-effect group models used in linear-model-based
analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based
on i) probabilistic dimension reduction of the individual data, ii) canonical
correlation analysis to identify a data subspace common to the group iii)
ICA-based pattern extraction. In addition, we introduce a procedure based on
cross-validation to quantify the stability of ICA patterns at the level of the
group. We compare our method with state-of-the-art multi-subject fMRI ICA
methods and show that the features extracted using our procedure are more
reproducible at the group level on two datasets of 12 healthy controls: a
resting-state and a functional localizer study
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