Pitfalls of mapping functional and molecular human brain imaging data from separate cohorts

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Abstract

It has become increasingly common to probe correlations between human brain imaging measures of receptor/protein binding and function using population-level brain maps drawn from independent cohorts, estimating correlations across regions. This strategy raises issues of interpretation that we highlight here with a multimodal brain imaging dataset and simulation studies. Twenty-four healthy participants completed neuroimaging with both [11C]Cimbi-36 positron emission tomography and magnetic resonance imaging scans to estimate receptor binding potential (BP) and cerebral blood flow (CBF), respectively, in 18 cortical/subcortical regions. Correlations between BP and CBF were estimated in three ways: 1) Pearson correlation across regions of mean regional BP and CBF (𝜌1), to mimic studies using data from independent cohorts; 2) Pearson correlation between BP and CBF across participants in each region (𝜌2); or 3) the correlation between BP and CBF across participants across all regions within a single linear mixed effects model (𝜌3). We observed a significant positive correlation across regions (𝜌1 = 0.672; p = 0.0023). Region-specific correlations across participants were substantively lower and not statistically significant (𝜌2: mean = 0.140, range = -0.112 to 0.336; all p > 0.10), nor when estimated simultaneously within a linear mixed model (𝜌3 = 0.138, p = 0.26). Our simulation study illustrated that regional differences in BP or CBF mean and variance can bias across regions correlations by 1000% or create a type-1 error of 100%. Our observations that both the estimated correlation and the statistical significance can differ greatly highlights that inferring across region correlation as evidence for correlation across participants is erroneous. Without validated methods that limit confounding and other biases, we discourage future studies from inferring across region correlation of population-level brain maps from independent cohorts in this manner.

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