Generative modeling of brain maps with spatial autocorrelation

By J. Burt, Markus Helmer, Maxwell Shinn, Alan Anticevic, John D. Murray. bioRxiv, 2020

454 citations recorded by the source corpus.

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 ontology 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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3ef4ea883e28d76ae1bad0085bb182a8e07cbe25

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

neuromaps: structural and functional interpretation of brain maps (2022, methodology): “For analyses in the current report using surface-based coordinate systems, we apply the original spatial permutation framework procedure 38 ; for analyses using volumetric systems we apply the variogram-matching method 43 .”
Comparing spatial null models for brain maps (2021, unknown): “Burt-2020 method Two years after introducing their spatial autoregressive method, Burt et al. (2020) proposed a novel model to generate surrogate data using variogram estimation.”
Heterochronous laminar maturation in the human prefrontal cortex (2025, unknown): “The significance of this correlation was assessed with a conservative spatial autocorrelation-preserving null ( p SA-null ) using the BrainSMASH software version 0.11.0 112 .”