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ICA of fMRI studies: new approaches and cutting edge applications

Edited by: Veronika Schöpf, Simon D. Robinson

Publisher: Frontiers Media SA

ISBN: 978-2-88919-200-7

Product Name: Frontiers Research Topic Ebook

Independent Component Analysis (ICA) is the most commonly used and most diversely applicable exploratory method for the analysis of functional Magnetic Resonance Imaging (fMRI) data. Over the last ten years it has offered a wealth of insights into brain function during task execution and in the resting state.

ICA is a blind source separation method that was originally applied to identify technical and physiological artifacts and allow their removal prior to analysis with model-based approaches. It has matured into a method capable of offering a stand-alone assessment of activation on a sound statistical footing. Recent innovations have taken on the complex challenges of how components should be combined over subjects to allow group inferences, and how activation identified with ICA might be compared between groups – of patients and controls - for instance. Having proved its worth in the investigation of resting state networks, ICA is being applied in other cutting edge uses of fMRI; in multivariate pattern analysis, real-time fMRI, in utero studies and a wide variety of paradigms and stimulus types and with challenging tasks with patients at ultra-high field. These are testament both to ICA’s flexibility and its central role in basic neuroscience and clinical applications of fMRI.

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