Making the most of fMRI at 7 T by suppressing spontaneous signal fluctuations

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.

Figure 1

Map of the active (ROI_active, green) and the reference region (ROI_ref, red) for two example slices of a foveal-stimulus data-set for one of the volunteers. Activations were significant at p < 0.05, Bonferroni corrected for multiple comparisons.

Figure 2

The two visual stimuli employed in the experiments, each consisting of a black-and-white checkerboard: A) a very small ring-shaped checkerboard with 0.5-1.2° eccentricity range (“foveal stimulation”), and B) a narrow wedge-shaped checkerboard (“wedge stimulation”) in the lower-left quadrant (eccentricity range = 0-11 °, width = ± 10° around polar angle 225°). In both cases, the visual angle of the entire image was 42°×32°.

Figure 3

Plot of the percentage of (cumulative) explained variance as a function of the number of PCA components, employed to determine the number of PCA components to use during analysis. The procedure employed to define the number of principal components describing non-thermal noise sources in ROI_ref was as follows: The explained variance in ROI_ref (see Figure 1) for each rank-ordered PCA component (blue) is shown. The variance is computed after eigenvalue decomposition of the data covariance matrix, by dividing each eigenvalue with the sum of all eigenvalues. The cumulative variance (magenta), equal to the sum of the variances across all the components from 1 to each rank-ordered component number, is also shown. To establish the number (M) of principal components describing non-thermal noise sources in ROI_ref, first the fraction of variance explained by non-thermal noise sources was determined. This fraction is equal to 1-(TSNR/SNR)², which was 87.3% on average for the data in ROI^ref in these experiments (solid horizontal line, black). The number (M) of components needed to describe that amount of variance was identified from the cumulative variance, resulting in M equal to about 18 for these data (dashed vertical line, black).

Figure 4

Sample time-courses for the ultra-short stimulus runs for one volunteer, with A) foveal stimulation, and B) wedge stimulation. After applying the SR-, and PCA-based noise correction procedures and the control procedures, the average time-course signal in ROI_active was computed. Eighteen principal components were included in the PCA-correction procedure. Subsequently the average short-stimulus response over 31 stimulus-events was calculated and displayed (error bars indicate the s.e. across trials). The average standard error σ_E in the estimation precision formula (EP, Eq. 1) was computed as the mean of the standard-errors for the 15 time points following stimulus onset.

Figure 5

Estimation precision (EP, upper panel) for stimulus-related responses, and the temporal autocorrelation level (AL, lower panel) of residual errors, after general linear model fitting with additional SR- (red) and 18 PCA (green)-regressors. Control conditions with randomized regressor(s) are indicated in cyan and blue. AL for simulated residuals with Gaussian properties is also displayed (yellow). The groups of bars on the left (A) and right (B) show results for foveal stimulation and wedge stimulation (ultra-short stimulus runs), respectively. The error bars indicate the average ± s.e. across subjects (n = 7).