Journal Article

Generative modeling of brain maps with spatial autocorrelation

Murray, John D., Helmer, Markus, Burt, Joshua B., Shinn, Maxwell, Anticevic, Alan

2020

Overview

Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene set enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization. •Spatial autocorrelation can dramatically inflate p-values in brain map analyses.•Null model generates surrogate brain maps matched to target spatial autocorrelation.•Spatial autocorrelation drives spurious findings in gene set enrichment analyses.•Surrogate maps can correct statistical analyses including gene set enrichment.•Python-based package implements the generative model with neuroimaging functionality.

• Publisher: Elsevier Inc,Elsevier Limited,Elsevier
• Subject: Brain / Brain - diagnostic imaging / Brain architecture / Brain Mapping / Connectome / Gene expression / Gene mapping / Gene set enrichment analysis / Generative null modeling / Humans / Hypotheses / Large-scale gradients / Medical imaging / Models, Statistical / Neuroimaging / Open source software / Principal components analysis / Spatial Analysis / Spatial autocorrelation / Statistical analysis / Topography