Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study

Abstract

Signal fluctuations in functional magnetic resonance imaging (fMRI) can result from a number of sources that may have a neuronal, physiologic or instrumental origin. To determine the relative contribution of these sources, we recorded physiological (respiration and cardiac) signals simultaneously with fMRI in human volunteers at rest with their eyes closed. State-of-the-art technology was used including high magnetic field (7 T), a multichannel detector array and high-resolution (3 mm(3)) echo-planar imaging. We investigated the relative contribution of thermal noise and other sources of variance to the observed fMRI signal fluctuations both in the visual cortex and in the whole brain gray matter. The following sources of variance were evaluated separately: low-frequency drifts due to scanner instability, effects correlated with respiratory and cardiac cycles, effects due to variability in the respiratory flow rate and cardiac rate, and other sources, tentatively attributed to spontaneous neuronal activity. We found that low-frequency drifts are the most significant source of fMRI signal fluctuations (3.0% signal change in the visual cortex, TE=32 ms), followed by spontaneous neuronal activity (2.9%), thermal noise (2.1%), effects due to variability in physiological rates (respiration 0.9%, heartbeat 0.9%), and correlated with physiological cycles (0.6%). We suggest the selection and use of four lagged physiological noise regressors as an effective model to explain the variance related to fluctuations in the rates of respiration volume change and cardiac pulsation. Our results also indicate that, compared to the whole brain gray matter, the visual cortex has higher sensitivity to changes in both the rate of respiration and the spontaneous resting-state activity. Under the conditions of this study, spontaneous neuronal activity is one of the major contributors to the measured fMRI signal fluctuations, increasing almost twofold relative to earlier experiments under similar conditions at 3 T.

Figure 1

A) The stimulus employed to functionally localize the visual cortex (polar-angle mapping run). The wedge-shaped checkerboard (contrast reversing at 7.5Hz) performed a full clock-wise rotation in 90s, covering 30 positions. For details, see Materials and Methods. B) Region of interest in the visual cortex (ROI_VC, for an example data-set), defined on the basis of the polar-angle mapping run. In particular, ROI_VC comprised the voxels in the visual cortex responding to the wedge-shaped checkerboard at any position in the visual field. C) Region of interest in the gray matter (ROI_GM, for the same data-set as shown in Figure 1B). ROI_GM was identified after regression of the signal of each voxel with a global regressor (average time-series across the whole brain), following a procedure developed in previous work [18]. Voxels with significant correlation (p < 0.001) were included in ROI_GM.

Figure 2

A) t-value of the correlation between the RVT and cardiac-rate regressors (shifted over a range of lag times) and the single-voxel time-series. The results of averaging the t-values of correlation in each voxel over ROI_VC and ROI_GM are shown here. For the RVT, the time lags −9 s and +9 s correspond to the maximum and minimum t-values, respectively (positive time lags indicate that fluctuations in RVT predict future fMRI signal changes; viceversa for negative time lags). The Cardiac-rate regressor shows two (negative) peaks at time lags −3 s and +9 s. B) Variance (%) explained by each regressor at different delays. Mean values (of ROI-averaged values of VE) ± s.e. across subjects are displayed. In this computation, low frequency drifts were accounted for with third-degree polynomials (i.e., X = [Xpol Xrvt] or X = [Xpol Xcard-rate]).

Figure 3

The correlation matrix (upper triangular matrix, r-value ranging from −1 to 1) of all noise regressors for noise sources 1) −4) is displayed, showing the collinearity between regressors. In the lower triangular matrix a contour plot highlighting regressors with significant collinearity (p < 0.05) is shown. At this statistical threshold (but not for p < 0.001) some regressors for different noise sources are collinear (see for example low frequency drifts and RVT regressors, or RVT regressors with cardiac-rate regressors).

Figure 4

Pie-charts showing the fMRI data variance explained (R²_adj, %, upper bold) by, and fMRI signal change (SC, %, lower italic) attributed to non-thermal noise sources 1) −4), thermal noise and spontaneous activity. Average (s.e) values across subjects are shown. The contribution of thermal noise at the ROI level was negligible.