NiSpace: neuroimaging spatial colocalization environment

Most neuroimaging research focuses on brain-regional effect estimates as outcomes of interest. Spatial patterns across the whole brain or cortex may convey equally important information missed by regionally constrained analysis approaches. These patterns are found in any given brain map, from individual brain development over time [1], to effect size maps reflecting case-control differences on the individual [2] or group level [3], to within-subject estimates of pharmacological challenges [4]. By testing such maps for their colocalization with reference atlases of known biological entities (e.g., neurotransmitter receptors, gene expression, or cortical cytoarchitecture), one can derive meaningful inferences from in-vivo human neuroimaging data. While spatial colocalization approaches become more common in the neuroimaging field, the underlying methodology suffers from a lack of standardization and defined best practices. Two common tools, JuSpace and neuromaps, provide either reduced flexibility for advanced use cases, or are designed to provide robust core functionality, but are limited in terms of statistics and analysis workflows.

Here, we introduce NiSpace (“NeuroImaging SPAtial Colocalization Environment”), a user-friendly toolbox integrated into the Python neuroimaging ecosystem that allows for robust and standardized, yet flexible colocalization analysis. NiSpace employs a pipeline-style API, extended by simplified command line and graphical interfaces for less experienced users. It advances currently available software in that it (i) integrates standardized multimodal human brain datasets in different common imaging spaces, (ii) handles surface and volumetric imaging data types, (iii) defines robust analysis workflows but allows for flexible adaptations, (iv) incorporates vectorized implementations of all currently used spatial colocalization statistics, (v) provides permutation-based significance estimates, and (vi) incorporates publication-ready visualizations.

NiSpace addresses use cases across clinical and basic neuroscience – from contextualizing individual brain maps within the landscape of known neurobiological systems, to comparing spatial phenotypes across diagnostic groups, to exploring meta-analytic patterns at the population level. The toolbox is particularly applicable to translational psychiatry and neurology, where identifying potential biological substrates of disease-related brain alterations is a central question, but equally serves the investigation of fundamental brain organizational principles. Analyses can be applied to structural or functional MRI, PET, or other neuroimaging data without requiring deep statistical expertise, while full flexibility is retained for advanced use cases. We demonstrate NiSpace’s utility across representative scenarios: identifying potential biological underpinnings of meta-analytic cortical thickness alteration patterns, characterizing pharmacologically induced changes in resting-state brain activity in terms of known neurotransmitter receptor distributions, and mapping altered functional connectivity patterns in autism spectrum disorder onto neural cell type distributions. Future developments will extend the framework to connectome colocalization, enabling spatial interrogation of structural and functional connectivity matrices against biological reference maps [5].

Spatial colocalization approaches gain popularity in research on fundamental brain-organizational principles, as well as in more applied clinical and pharmacological fields. With NiSpace, we present a Python toolbox with dedicated programming and graphical interfaces that makes spatial colocalization analysis accessible, reproducible, and statistically rigorous – providing clinical and basic researchers alike with a principled framework for interrogating the biological basis of brain phenotypes.

References

1. Lotter et al. Regional patterns of human cortex development correlate with underlying neurobiology. Nature Communications. 2024. https://doi.org/10.1038/s41467-024-52366-7
2. Dukart et al. JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Human Brain Mapping. 2021. https://doi.org/10.1002/hbm.25244
3. Hansen et al. Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nature Neuroscience. 2022. https://doi.org/10.1038/s41593-022-01186-3
4. Dukart et al. Cerebral blood flow predicts differential neurotransmitter activity. Scientific Reports. 2018. https://doi.org/10.1038/s41598-018-22444-0
5. Lotter et al. Linking human brain functional connectivity to underlying neurotransmission. bioRxiv. 2026. https://doi.org/10.64898/2026.04.28.721294