Generative modeling of brain maps with spatial autocorrelation
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Abstract
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.
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A correlation coefficient proposed by Burt and colleagues reflects the relationship between two maps free from spatial autocorrelation.
“we obtained a correlation coefficient that is proposed, by Burt and colleagues38, to reflect the relationship between the two maps free from spatial autocorrelation.”
From Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis (2026) · cites this paper as [38]
Section 3.2 says Pearson-correlation p-values are blind to spatial autocorrelation and can make randomly generated maps appear significantly correlated. The paper uses spatial-autocorrelation-preserving surrogate maps to assess the significance of a correlation, not a proposed correlation coefficient that measures the relationship free from spatial autocorrelation (Sections 3.2 and 2.8; page numbers unavailable). (LLM, conf=0.85)
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Experimental — accuracy not yet measuredBrainSMASH was used to generate spatially autocorrelated surrogate maps to account for spatial autocorrelation in dMRI maps.
“BrainSMASH 67 was used to generate spatially autocorrelated surrogate maps (n = 1000 per dMRI metric) that matched the spatial dependence of the original dMRI data for each genotype separately”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [67]
Spatial autocorrelation was accounted for using the BrainSMASH toolbox.
“We accounted for spatial autocorrelation using the BrainSMASH toolbox38 to generate the MNI (Montreal Neurological Institute) coordinates for our atlas.”
From Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis (2026) · cites this paper as [38]
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- Authors
- Burt, J. B., Helmer, M., Shinn, M., Anticevic, A., & Murray, J. D.
- Affiliations
- Yale University (US)
- Corresponding author
- Murray, John D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 220
- Pages
- 117038
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W3036199858
- Cited by
- 528
- References
- 62
- Citations by year
- 2026: 125 · 2025: 115 · 2024: 104 · 2023: 70 · 2022: 62 · 2021: 41 · 2020: 11
- FWCI
- 24.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (R01MH112189, P50AA012870, R01MH108590, U01MH121766, R01MH112746)
- Simons Foundation Autism Research Initiative
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Mathematics › Applied Mathematics › Point processes and geometric inequalities
- Keywords
- spatial autocorrelation, large-scale brain organization, gene set enrichment analysis, brain parcellation, cortical maps
- MeSH terms
- Brain, Brain Mapping, Humans, Models, Statistical, Neuroimaging, Spatial Analysis, Connectome
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