Abstract
The presence of spontaneous BOLD fMRI signal fluctuations in human grey matter compromises the detection and interpretation of evoked responses and limits the sensitivity gains that are potentially available through coil arrays and high field systems. In order to overcome these limitations, we adapted and improved a recently described correlated noise suppression method (de Zwart et al., 2008), demonstrating improved precision in estimating the response to ultra-short visual stimuli at 7 T. In this procedure, the temporal dynamics of spontaneous signal fluctuations are estimated from a reference brain region outside the area targeted with the stimulus. Rather than using the average signal in this region as regressor, as proposed in the original method, we used principal component analysis to derive multiple regressors in order to optimally describe nuisance signals (e.g. spontaneous fluctuations) and separate these from evoked activity in the target region. Experimental results obtained from application of the original method showed a 66% improvement in estimation precision. The novel, enhanced version of the method, using 18 PCA-derived noise regressors, led to a 160% increase in precision. These increases were relative to a control condition without noise suppression, which was simulated by randomizing the time-course of the nuisance-signal regressor(s) without altering their power spectrum. The increase of estimation precision was associated with decreased autocorrelation levels of the residual errors. These results suggest that modeling of spontaneous fMRI signal fluctuations as multiple independent sources can dramatically improve detection of evoked activity, and fully exploit the potential sensitivity gains available with high field technology.