Mapping sources of correlation in resting state FMRI, with artifact detection and removal

Abstract

Many components of resting-state (RS) FMRI show non-random structure that has little to do with neural connectivity but can covary over multiple brain structures. Some of these signals originate in physiology and others are hardware-related. One artifact discussed herein may be caused by defects in the receive coil array or the RF amplifiers powering it. During a scan, this artifact results in small image intensity shifts in parts of the brain imaged by the affected array components. These shifts introduce artifactual correlations in RS time series on the spatial scale of the coil's sensitivity profile, and can markedly bias RS connectivity results. We show that such a transient artifact can be substantially removed from RS time series by using locally formed regressors from white matter tissue. This is particularly important in arrays with larger numbers of coils, which may generate smaller artifact zones. In such a case, brain-wide average noise estimates would fail to capture the artifact. We also examine the anatomical structure of artifactual variance in RS FMRI time series, by identifying sources that contribute to these signals and where in the brain are they manifested. We consider current methods for reducing confounding sources (or noises) and their effects on connectivity maps, and offer an improved approach (ANATICOR) that can also reduce hardware artifacts. The methods described herein are currently available with AFNI, in addition to tools for rapid, interactive generation of seed based correlation maps at single-subject and group levels.

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