On testing for spatial correspondence between maps of human brain structure and function

By Aaron F. Alexander-Bloch, Haochang Shou, Siyuan Liu, Theodore D. Satterthwaite, David C. Glahn, Russell T. Shinohara, Simon N. Vandekar, Armin Raznahan. NeuroImage, 2018

716 citations recorded by the source corpus.

Abstract A critical issue in many neuroimaging studies is the comparison between brain maps. Nonetheless, it remains unclear how one should test hypotheses focused on the overlap or spatial correspondence between two or more brain maps. This “correspondence problem” affects, for example, the interpretation of comparisons between task‐based patterns of functional activation, resting‐state networks or modules, and neuroanatomical landmarks. To date, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap by applying random rotations to spherical representations of the cortical surface, an approach for which we also provide a theoretical statistical foundation. We use this method to derive clusters of cognitive functions that are correlated in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task‐based functional activity, resting‐state fMRI networks and gyral‐based anatomical landmarks. We provide open‐access code to implement the methods presented for two commonly‐used tools for surface based cortical analysis (https://www.github.com/spin‐test). This spatial permutation approach constitutes a useful advance over widely‐used methods for the comparison of cortical maps, thereby opening new possibilities for the integration of diverse neuroimaging data. HighlightsA new method is developed to test the anatomical correspondence between brain maps.Random rotational permutations generate rigorous null models of correspondence.The correspondence of structural, functional and resting‐state maps is quantified.These methods are publicly available for future applications.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d14040cf903c6d7158ed6cdfbac9e80491b56106

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets (2019, methodology): “BrainSpace provides two different approaches to build null models, including an adaptation of a previously released spin permutation test 46 and Moran ’ s spectral randomization 50 .”
neuromaps: structural and functional interpretation of brain maps (2022, methodology): “[4] as the default for use with surface data, and the parameterized generative method described in Burt et al.”
Gradients of structure–function tethering across neocortex (2019, positive): “To assess the relationship between regional structure–function R(2) values and network membership or gradient position, we applied a recently developed null model that preserves the spatial embedding and autocorrelation of our parcellations (37).”
Mapping neurotransmitter systems to the structural and functional organization of the human neocortex (2021, neutral): “24,25 ).”
Generative modeling of brain maps with spatial autocorrelation (2020, neutral): “these approaches, the spin method, involves randomizing the anatomical alignment between two cortical surface maps through spherical rotation by a random angle (Alexander-Bloch et al., 2018).”