Neuromaps plugin

Neuromaps is a toolbox designed for assessing, transforming, and analyzing structural and functional brain annotations (Markello, Hansen et al., 2022). It marks a significant stride towards integrative analytics in multimodal, multiscale neuroscience by bridging the gap between various neuroimaging modalities and proposing analytical tools to establish connections among different brain features. The toolbox comprises a curated repository of brain maps in their native space, methods for generating transformations across multiple coordinate systems, and offers a systematic workflow for comprehensive structural and functional annotation enrichment analysis of the human brain.

For the first iteration of the implementation, we focused on the neurotransmitter receptors and transporters. Thirty different maps from the neuromaps toolbox were selected, covering nine different neurotransmitter systems: dopamine, norepinephrine, serotonin, acetylcholine, glutamate, GABA, histamine, cannabinoid, and opioid. These maps are sourced from open-access repositories, addressing the need for a comprehensive tool that integrates standardized analytic workflows for both surface and volumetric data.

Plugin Key Features

Once you have fetched the brain annotations, a new 'Neuromaps' node which contains the FSAverage anatomy will appear in the Anatomy view.

All the imported brain annotations will be visible in the Functional data (sorted by subjects) view.

By default, the new weighted-averaged file is named 'WAvg:' followed by the number of files averaged. If you wish to rename this or any other files from the Neuromaps plugin to provide more informative labels for your analysis, follow these steps:

• Click once on the file to select it.
• With a second click, you will enter renaming mode and the file name will become editable.
• Type in the new desired name for the file.
• Press the "Enter" key on your keyboard or click outside the file name to save the new name.

Alternatively, you can right-click > File > Rename.

Scouting values of annotations

[...] Please refer to Tutorial 23: Scouts for a more in-depth guide in creating, manipulating, and analyzing scouts for effective exploration and comparison of brain activity across different experimental conditions and regions of interest.

Statistical Analyses for Significance Testing

One of the main advantages of having these brain annotations in a common coordinate system is that we can statistically compare their spatial topographies. However, most statistical analyses (e.g., Pearson correlation) assume that the values of observations in each sample are independent of one another. Spatial autocorrelation violates this assumption because samples taken from nearby areas are related to each other and are not independent. Spatially-naive null models—both parametric and non-parametric—yield inflated p-values and are inappropriate for significance testing of neuroimaging brain maps. When applied to spatially-autocorrelated brain maps typical of most neuroimaging data, these models approach a false positive rate of >75% and their usage in the field is discouraged (Markello, R. D., & Misic, B., 2021).

We therefore use spatially informed null models to benchmark the statistical unexpectedness of specific features of interest.

The original neuromaps workflow integrates multiple methods of performing spatial permutations for significance testing. For the plugin, we implemented the most widely adopted of these approaches—the spin method, which involves randomizing the anatomical alignment between two cortical surface maps through spherical rotation by a random angle (Alexander-Bloch et al., 2018).

Spatial correlation with brain annotations

• 1-sample source estimation × brain annotations
• N-sample source estimation × brain annotations

Spatial correlation with any files

• 1-sample source estimation × 1-sample source estimation (or brain annotation)
• N-sample source estimation × 1-sample source estimation (or brain annotation)
• N-sample source estimation × N-sample source estimations

Number of spins

On the Hard Drive

to be added

Applications of Neuromaps for Integrative Research

The Neuromaps plugin opens up new and exciting avenues for research and can help address questions that depend crucially on anatomical localization. Below, we explore some potential uses of the plugin in advancing integrative research, supported by examples from the literature.

Annotating Structural Connectomes

• Hansen, J. Y., Shafiei, G., Voigt, K., Liang, E. X., Cox, S. M., Leyton, M., ... & Misic, B. (2023). Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease. Plos Biology, 21(9), e3002314.

  • With the advancements in neuroimaging techniques, which enables the simultaneous exploration of diverse interregional relationships across spatial and temporal scales, neuromaps emerge as an invaluable tool for advancing integrative research in connectomics. The researchers noted a consistent primacy of molecular connectivity modes, demonstrating the mapping of correlated receptor similarity onto multiple phenomena. The use of neuromaps revealed both the similar and complementary ways in which connectivity modes reflect cortical geometry, structure, and disease and assisted in constructing a multimodal wiring map.

Identification of Specific Neurochemical Targets for Potential Future Clinical Treatments

• da Silva Castanheira, J., Wiesman, A. I., Hansen, J. Y., Misic, B., Baillet, S., Network Quebec Parkinson, & PREVENT-AD Research Group. (2023). Neurophysiological brain-fingerprints of motor and cognitive decline in Parkinson’s disease. medRxiv.

  • Subject-level data can be contextualized against the brain annotations contained in the plugin to produce a subject-specific ‘fingerprint’ of how individuals express different structural and functional brain phenotypes. The authors were able to show the topographical alignment between spectral brain-fingerprints, the functional hierarchy of cortex, and neurotransmitter systems.

• Wiesman, A. I., da Silva Castanheira, J., Degroot, C., Fon, E. A., Baillet, S., Network, Q. P., & Prevent-Ad Research Group. (2023). Adverse and compensatory neurophysiological slowing in Parkinson’s disease. Progress in Neurobiology, 231, 102538.

  • Neuromaps has the potential to enhance the integration of neurophysiological and neurochemical data for a more comprehensive understanding of brain function, and assist in the identification of specific neurochemical targets for potential future clinical treatments. The authors demonstrated, for instance, that the clinical relationships of neurophysiological slowing in patients with Parkinson's disease are aligned topographically with the densities of selective neurotransmitter systems and the organization of sensory-association areas—namely, dopamine, serotonin, GABA, and norepinephrine systems.

Acknowledgements

• All credit for the conception of the original neuromaps algorithm is due to Ross D. Markello, Justine Y. Hansen, Zhen-Qi Liu, Vincent Bazinet, Golia Shafiei, Laura E. Suárez, Nadia Blostein, Jakob Seidlitz, Sylvain Baillet, Theodore D. Satterthwaite, M. Mallar Chakravarty, Armin Raznahan & Bratislav Misic. The appropriate citation for neuromaps is as follows:

• Markello, R.D., Hansen, J.Y., Liu, ZQ. et al. neuromaps: structural and functional interpretation of brain maps. Nat Methods 19, 1472–1479 (2022).

• If you used any of the included maps, please also cite the original papers that publish the data. References for each map can be found in this spreadsheet.

• If you used the surface maps which were transformed using the registration fusion framework, please also cite:

• Wu, J. et al. Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems. Hum. Brain Mapp. 39, 3793–3808 (2018).

Contributing

We welcome contributions from the community to help improve and expand the functionality of the Neuromaps plugin. Feel free to submit pull requests on the Github repository, report issues, or provide suggestions below! Your feedback is invaluable in ensuring a user-friendly experience for researchers worldwide. We believe that open science is most impactful when the countless everyone is provided with equal access to the newest and greatest resources in the field.