About this Research Topic
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.
The goal of this Research Topic is to unite contributions from researchers developing ICA methods, and tools based on ICA, and those focused on applying them in new contexts. We solicit original research articles and reviews of aspects not considered elsewhere. While focusing on work in the neurosciences, this Research Topic also welcomes contributions in the form of methodological innovations, computational approaches, developmental and patient studies. We hope that collecting a group of papers on this Research Topic will help provide an overview of the state of the art as well as opening a forum for discussion of the exciting new opportunities offered by ICA across scientific disciplines.
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.
The goal of this Research Topic is to unite contributions from researchers developing ICA methods, and tools based on ICA, and those focused on applying them in new contexts. We solicit original research articles and reviews of aspects not considered elsewhere. While focusing on work in the neurosciences, this Research Topic also welcomes contributions in the form of methodological innovations, computational approaches, developmental and patient studies. We hope that collecting a group of papers on this Research Topic will help provide an overview of the state of the art as well as opening a forum for discussion of the exciting new opportunities offered by ICA across scientific disciplines.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
