Partner‐matching for the automated identification of reproducible ICA components from fMRI datasets: Algorithm and validation

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Abstract
The analysis of functional magnetic resonance imaging (fMRI) data is complicated by the presence of a mixture of many sources of signal and noise. Independent component analysis (ICA) can separate these mixtures into independent components, each of which contains maximal information from a single, independent source of signal, whether from noise or from a discrete physiological or neural system. ICA typically generates a large number of components for each subject imaged, however, and therefore it generates a vast number of components across all of the subjects imaged in an fMRI dataset. The practical implementation of ICA has been limited by the difficulty in discerning which of these many components are spurious and which are reproducible, either within or across individuals of the dataset. We have developed a novel clustering algorithm, termed “Partner‐Matching” (PM), which identifies automatically the independent components that are reproducible either within or between subjects. It identifies those components by clustering them according to robust measures of similarity in their spatial configurations either across different subjects of an fMRI dataset, within a single subject scanned across multiple scanning sessions, or within an individual subject scanned across multiple runs within a single scanning session. We demonstrate the face validity of our algorithm by applying it to the analysis of three fMRI datasets acquired in 13 healthy adults performing simple auditory, motor, and visual tasks. From among 50 independent components generated for each subject, our PM algorithm automatically identified, across all 13 subjects, components representing activity within auditory, motor, and visual cortices, respectively, as well as numerous other reproducible components outside of primary sensory and motor cortices, in functionally connected circuits that subserve higher‐order cognitive functions, even in these simple tasks. Hum Brain Mapp, 2008.