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

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.

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.

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).

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.