Artifacts in functional magnetic resonance imaging (fMRI) data, primarily those related to
motion and physiological sources, negatively impact the functional signal-to-noise ratio in …

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the
effects that are truly related to the underlying neuronal activity difficult. Independent …

Blood oxygenation level dependent (BOLD) signals in functional magnetic resonance
imaging (fMRI) are often small compared to the level of noise in the data. The sources of …

When applied to functional magnetic resonance imaging (fMRI) data, spatial independent
component analysis (sICA), a data-driven technique that addresses the blind source …

This short “how to” article describes a plot I find useful for assessing fMRI data quality. I
discuss the reasoning behind the plot and how it is constructed. I create the plot in scans …

Methods for testing and validating independent component analysis (ICA) results in fMRI are
growing in importance as the popularity of this model for studying brain function increases …

Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI)
signal, complicating attempts to infer those changes that are truly related to brain activation …

Principle Component Analysis (PCA) and Independent Component Analysis (ICA) were
used to decompose the fMRI time series signal and separate the BOLD signal change from …

fMRI data are commonly analyzed by testing the time course from each voxel against
specific hypothesized waveforms, despite the fact that many components of fMRI signals are …

Independent component analysis (ICA) is a signal processing technique using higher-order
statistics to extract signals by unmixing signal mixtures. McKeown et al.(1998) introduced …