Bootstrap approach for meta-synthesis of MRI findings from multiple scanners

Abstract

Background: Neuroimaging data from large epidemiologic cohort studies often come from multiple scanners. The variations of MRI measurements due to differences in magnetic field strength, image acquisition protocols, and scanner vendors can influence the interpretation of aggregated data. The purpose of the present study was to compare methods that meta-analyze findings from a small number of different MRI scanners.

Methods: We proposed a bootstrap resampling method using individual participant data and compared it with two common random effects meta-analysis methods, DerSimonian-Laird and Hartung-Knapp, and a conventional pooling method that combines MRI data from different scanners. We first performed simulations to compare the power and coverage probabilities of the four methods in the absence and presence of scanner effects on measurements. We then examined the association of age with white matter hyperintensity (WMH) volumes from 787 participants.

Results: In simulations, the bootstrap approach performed better than the other three methods in terms of coverage probability and power when scanner differences were present. However, the bootstrap approach was consistent with pooling, the optimal approach, when scanner differences were absent. In the association of age with WMH volume, we observed that age was significantly associated with WMH volumes using the bootstrap approach, pooling, and the DerSimonian-Laird method, but not using the Hartung-Knapp method (p < 0.0001 for the bootstrap approach, DerSimonian-Laird, and pooling but p = 0.1439 for the Hartung-Knapp approach).

Conclusion: The bootstrap approach using individual participant data is suitable for integrating outcomes from multiple MRI scanners regardless of absence or presence of scanner effects on measurements.

Keywords: Bootstrap resampling; Meta-analysis; Multiple MRI scanners; White matter hyperintensity.

Figure 1.. Diagram of the Bootstrap approach

At each bootstrap resampling (j=1,…,M), the weighted sum, ${\widehat{\beta }}^{B(j)}$, is estimated, and the variance is obtained as the final outcome. 95% CI is estimated using quantiles from the standard normal distribution.

Figure 2.

Procedures from the four methods.

Figure 3.

Coverage probability. Coverage probability (y-axis) versus the number of participants (N= 30, 50, 100) in x-axis from four methods, bootstrap, pooling, DerSimonian-Laird, and Hartung-Knapp, are shown. Simulation results without scanner differences are shown in (a) and (b), and those with scanner differences in (c) and (d). The number of scanners is two in (a) and (c), and five in (b) and (d). In each plot, red line is for the nominal coverage probability (0.95), the blue solid line connects the simulation results for the bootstrap method, the black solid line for the pooling approach, the black dotted line for Dersimonian-Laird, and the black dot-dashed line those for Hartung-Knapp.

Figure 4.

Power. Power (y-axis) versus the number of participants (N= 30, 50, 100) in x-axis from four methods, bootstrap, pooling, DerSimonian-Laird, and Hartung-Knapp, are shown. Results without scanner differences are shown in (a) and (b), and those with scanner differences in (c) and (d). The number of scanners is two in (a) and (c), and five in (b) and (d). In each plot, blue solid line is for the bootstrap method, black solid line for the pooling approach, black dotted line for Dersimonian-Laird, and black dotdashed line for Hartung-Knapp.

Figure 5.

Association of age with WMH volume by scanner. Blue dots show participants from Scanner A, and turquoise dots from Scanner B. WMH volume = log10 (WMH volume in mm³ as a percentage of ICV).