When structure affects function--the need for partial volume effect correction in functional and resting state magnetic resonance imaging studies

Abstract

Both functional and also more recently resting state magnetic resonance imaging have become established tools to investigate functional brain networks. Most studies use these tools to compare different populations without controlling for potential differences in underlying brain structure which might affect the functional measurements of interest. Here, we adapt a simulation approach combined with evaluation of real resting state magnetic resonance imaging data to investigate the potential impact of partial volume effects on established functional and resting state magnetic resonance imaging analyses. We demonstrate that differences in the underlying structure lead to a significant increase in detected functional differences in both types of analyses. Largest increases in functional differences are observed for highest signal-to-noise ratios and when signal with the lowest amount of partial volume effects is compared to any other partial volume effect constellation. In real data, structural information explains about 25% of within-subject variance observed in degree centrality--an established resting state connectivity measurement. Controlling this measurement for structural information can substantially alter correlational maps obtained in group analyses. Our results question current approaches of evaluating these measurements in diseased population with known structural changes without controlling for potential differences in these measurements.

Figure 1. Schematic overview of the data generation procedure and statistical testing performed in this study.

x – mean of the corresponding voxel time series, s_x – standard deviation of the corresponding voxel time series, GM – grey matter, WM – white matter, CSF – cerebrospinal fluid, fMRI – functional magnetic resonance imaging, SNR – signal-to-noise ratio.

Figure 2. Data generation and statistical results.

a) Data generated using the boxcar function to simulate a block design functional magnetic resonance imaging (fMRI) signal. Original signal, signal with Gaussian noise, and the convolved noisy signal are displayed. b) Simulated fMRI time series with four different signal-to-noise ratios are displayed. c) Simulated rsMRI time series with two different signal-to-noise ratios are displayed. d) Two exemplary results of the fMRI simulation study for estimation of beta coefficients are displayed for the 980 functional voxels generated for each constellation of partial volume effect contribution. gm – grey matter, wm – white matter, csf – cerebrospinal fluid, SNR – signal-to-noise ratio.

Figure 3. Results of the functional magnetic resonance imaging simulation study.

Numbers of significant voxels detected for each signal-to-noise ratio (SNR), grey matter contribution (GM), and white matter (WM) to cerebrospinal fluid ratio (CSF) ratio are displayed as a colour scale. The colour scale indicates the number of significant voxels detected for each partial volume effect constellation (out of 980).

Figure 4. Results of the resting state magnetic resonance imaging simulation study.

Numbers of significant voxels detected for each signal-to-noise ratio (SNR), grey matter contribution (GM), and white matter (WM) to cerebrospinal fluid ratio (CSF) ratio are displayed as a colour scale. The colour scale indicates the number of significant connectivity differences detected for each partial volume effect constellation (maximum 3600).

Figure 5. Results of partial volume effects estimation for real resting state magnetic resonance imaging data.

a) T-values obtained when comparing voxel-wise degree centrality values grouped by their relative cerebrospinal fluid concentration. Black squares indicate non-signficant results (p<.05 Bonferroni corrected). b) T-values obtained when comparing voxel-wise degree centrality values grouped by their relative white matter concentration. Black squares indicate non-signficant results (p<.05 Bonferroni corrected). c) Mean and standard deviations of degree centralities observed after grouping by their relative cerebrospinal fluid (CSF) or white matter (WM) concentration. *indicates a significantly lower degree centrality value as compared to the next lower contribution of respective tissue. d) A plot of observed vs. predicted degree centrality values for a representative subject in the leave-one-out cross-validation using grey and white matter probabilities to compute the voxel-wise general linear models.

Figure 6. Statistical parametric mapping (SPM) results obtained when testing for negative and positive correlations with age and sex on the group level with and without adjustment for underlying structural information are displayed.

On the right, Jaccard indices of overlap for the corresponding statistical maps are shown.