Data Driven & Exploratory Analyses
Vince Calhoun1
1The Mind Research Network & The University of New Mexico, Albuquerque, NM, United States
Synopsis
Independent
component analysis (ICA) has grown to be a widely used and continually
developing staple for analyzing fMRI functional connectivity data. In this
paper we discuss some key observations and assumptions regarding ICA and also
key new applications of ICA to brain imaging data.
10 key observations on the analysis of resting-state fMRI data using independent component analysis
For
over 20 years, the powerful, flexible family of independent component analysis
techniques has been used to examine spatial, temporal and subject variation in
fMRI data. Here, we provide an overview of 10 key principles in the basic and
advanced application of ICA to resting-state fMRI. ICA’s core advantages
include robustness to artifact, false positives and autocorrelation,
adaptability to variant study designs, agnosticism to the temporal evolution of
fMRI signals, and ability to extract, identify and analyze neural networks. ICA
remains in the vanguard of fMRI methods development, generating cutting-edge
approaches to dynamic functional connectivity, deep learning and
inter-individual variation.Acknowledgements
No acknowledgement found.References
No reference found.
Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)