Abstract
Understanding the mechanisms of brain function and dysfunction is at the core of the neuroscience mission. However, the field’s grasp of causal relationships between brain properties has been hindered by a focus on single modalities that neglects the complex interplay between the features found at different neural scales. Progress in neuroinformatics and the increasing availability of open datasets have helped overcome this limitation by facilitating the contextualization of brain maps against cellular, metabolic and network features. Despite the rapid uptake of data contextualization methods proposing that quantification of spatial similarity between brain maps may shed light on pathways of structure–function coupling, development and disease, their potential pitfalls have received little attention. In the context of neuroimaging research, these limitations include reliance on often small-sample and non-representative reference datasets, repeated use of the same brain maps across studies, and problems with intermodal and interindividual alignment. Applying data contextualization without considering these limitations can lead to circular reasoning, overfitting and correlational overreach, and limits the interpretation of findings to the properties of the source data. Here we provide a Roadmap of practical guidelines operating at the level of study design, analysis pipelines and interpretation of findings to encourage the development of best practices in data contextualization. A more informed use of brain map correlation approaches will improve mechanistic investigations and our understanding of causal relationships between brain properties.
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Data availability
The data used to generate Fig. 1a are available as part of the MICA-MICs dataset (https://osf.io/j532r/)20. For subpanels displayed in Fig. 1b, Fig. 3a (bottom) and Fig. 3c, ultrahigh resolution 7 T MRI data are available as part of the MICA-PNI dataset (https://osf.io/mhq3f/)207. Histological data sampling cortical cyto-architecture are available via BigBrain25 (raw data: https://bigbrain.loris.ca) and BigBrainWarp (processed data: https://github.com/caseypaquola/BigBrainWarp)74. Gene expression data are provided by the Allen Human Brain Atlas (raw data: https://human.brain-map.org/)27. These data were processed with abagen, available via GitHub at https://github.com/rmarkello/abagen (ref. 96) and are made available in the ENIGMA toolbox via GitHub at https://github.com/MICA-MNI/ENIGMA (ref. 19). Data and analysis tools to reproduce Fig. 2 are available in neuromaps via GitHub at https://github.com/netneurolab/neuromaps (ref. 18).
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Acknowledgements
J.R. received support from the Canadian Institutes of Health Research (CIHR Fellowship, PhD) and a Banting Postdoctoral Fellowship from the National Science and Engineering Research Council of Canada (NSERC). C.P. is supported by the Deutsche Forschungsgemeinschaft (DFG Emmy Noether Programme 524408221). S.L. acknowledges research support from the Centre de Recherche du CHUS, Molson Foundation and the National Science and Engineering Research Council of Canada (NSERC Discovery RGPIN-2025-06138). B.C.B. acknowledges research support from the National Science and Engineering Research Council of Canada (NSERC RGPIN-2025-05932), CIHR (FDN-154298, PJT-174995, PJT-191853 and PJT-203761), SickKids Foundation (NI17-039), Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL), HBHL, Brain Canada Foundation, FRQS, Tier-2 Canada Research Chairs Program and The Centre for Excellence in Epilepsy at the Neuro (CEEN).
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J.R. and B.C.B. researched data for the article. J.R., B.C.B, C.P., S.L. and R.L. contributed substantially to the discussion of the content and wrote the article. All authors reviewed and/or edited the manuscript before submission.
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B.C.B. is co-founder of BrainScores Inc. The other authors declare no competing interests.
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Glossary
- Brain cartography
-
Analogous to geographic cartography mapping terrain, brain cartography refers to efforts to deliver a detailed mapping of the brain’s anatomy and function.
- Brain map
-
As the product of brain cartography, brain maps render structural or functional properties of the brain across its anatomical landscape.
- Effective connectivity
-
In the context of neuroimaging, effective connectivity aims to capture the directed, causal influence that one brain region exerts over another. This is distinct from the statistical association captured by functional connectivity, which describes the temporal co-variance or correlation between neural signals without regard to directionality. Effective connectivity models the direction and magnitude of information flow and is therefore model dependent: estimates are derived by fitting a generative or causal model to observed data and inferring the underlying circuit architecture that best explains the measured activity. Common analytical approaches include dynamic causal modelling and Granger causality analysis.
- Functional connectivity
-
Statistical relationships (such as correlated or coherent fluctuations) between neural activity time series from different regions, commonly estimated with functional MRI, electroencephalography or magnetoencephalography; these relationships do not imply direct structural connections or causality.
- HARKing
-
A questionable research practice consisting of hypothesizing after the results are known.
- Interpolation
-
When using sparsely sampled data (for example, transcriptomic samples or intracranial electroencephalography recordings), values in an unsampled location may be estimated by generalizing from spatially neighbouring data points. This interpolation can be necessary in certain analytical pipelines when sparse data is contextualized against more densely sampled brain maps (for example, collected using MRI).
- Multivariate dominance analysis
-
A statistical method used to determine the relative importance of multiple predictor variables in explaining variance in one or more outcome variables within a multivariate regression framework.
- Multiple imputation
-
Multiple imputation is a statistical procedure for handling missing or withheld data, notably used in psychometrics and survey data analysis. It replaces each missing value with a set of m plausible values drawn from a predictive distribution conditioned on the observed data. Extensions to neuroimaging-focused applications have notably aimed to replace sensitive identifying features (for example, combinations of demographic variables that uniquely identify a participant) with statistically plausible synthetic values. Another example application has focused on generating multiple plausible completions of partially incomplete feature sets (for example, missing longitudinal timepoints).
- Sensory–association axis
-
A topographic gradient of brain organization recapitulated in several data modalities that spans from primary sensory areas to higher-order association areas, reflecting how input from the external environment is processed and integrated into abstract mental representations.
- Spatial autocorrelation
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A property of brain maps by which data points that are proximally located in space are non-independent; that is, a correlation between spatial proximity and measurement similarity of data points making up a given brain map is observed.
- Spatial normalization
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The process of transforming individual brain images into a common anatomical space or template, so that brain structures are aligned across different participants or scans. This allows for comparisons across individuals by accounting for differences in brain size, shape and orientation. Spatial normalization may involve linear (translations, scaling or rotations) and nonlinear (warping) transformations to match input images to a standard brain template reference.
- Stereotactic space
-
A standard three-dimensional coordinate system to precisely locate or refer to a given area of the brain.
- Structural connectivity
-
The brain’s physical network of anatomical links between regions, typically quantified in humans in vivo by mapping white matter pathways with diffusion MRI and tractography.
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Royer, J., Paquola, C., Larivière, S. et al. Opportunities and pitfalls of data contextualization in neuroimaging. Nat. Rev. Neurosci. (2026). https://doi.org/10.1038/s41583-026-01038-0
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DOI: https://doi.org/10.1038/s41583-026-01038-0
