Combining independent component analysis and correlation analysis to probeinterregional connectivity in fMRI task activation datasets

A new approach in studying interregional functional connectivity using func tional magnetic resonance imaging (fMRI) is presented. Functional connectiv ity may be detected by means of cross correlating time course data from fun ctionally related brain regions. These data exhibit high temporal coherence of low frequency fluctuations due to synchronized blood flow changes. In t he past, this fMRI technique for studying functional connectivity has been applied to subjects that performed no prescribed task ("resting" state). Th is paper presents the results of applying the same method to task-related a ctivation datasets. Functional connectivity analysis is first performed in areas not involved with the task. Then a method is devised to remove the ef fects of activation from the data using independent component analysis (ICA ) and functional connectivity analysis is repeated. Functional connectivity , which is demonstrated in the "resting brain," is not affected by tasks wh ich activate unrelated brain regions. In addition, ICA effectively removes activation from the data and may allow us to study functional connectivity even in the activated regions. (C) 2000 Elsevier Science Inc. All rights re served.