How does spatial extent of fMRI datasets affect independent component analysis decomposition?

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to diff...

Spatial, Temporal, and Spatiotemporal Independent Component Analysis of Fmri Data

Independent Component Analysis of Auditory Fmri Responses

The basic principles used by the auditory cortex to decipher the sensory streams are less understood than those used by the visual cortex. Based on previous research on animals, functional Magnetic Resonance Imaging (fMRI) responses in the human auditory cortex to simple streams of colored acoustic noise are expected to follow both linear (sustained) and non-linear (transient) spatio-temporal p...

Independent Component Analysis of fMRI Data

Techniques employed to analyze functional magnetic resonance imaging (fMRI) data typically use some form of univariate data analysis to determine regions of task-related activity. Changes in blood oxygen level dependent (BOLD) signal at each voxel can successfully be analyzed using univariate techniques. This type of analysis can identify voxels where the BOLD signal changes are significantly c...

Mining EEG-fMRI using independent component analysis.

Independent component analysis (ICA) is a multivariate approach that has become increasingly popular for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the brain's response to stimuli, ICA allows the researcher to explore the factors that constitute the data and alleviates the need for explicit spatial and temporal ...