Skip to main content
Log in

InSpectro-Gadget: A Tool for Estimating Neurotransmitter and Neuromodulator Receptor Distributions for MRS Voxels

  • Research
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

Magnetic resonance spectroscopy (MRS) is widely used to estimate concentrations of glutamate and \(\gamma\)-aminobutyric acid (GABA) in specific regions of the living human brain. As cytoarchitectural properties differ across the brain, interpreting these measurements can be assisted by having knowledge of such properties for the MRS region(s) studied. In particular, some knowledge of likely local neurotransmitter receptor patterns can potentially give insights into the mechanistic environment GABA- and glutamatergic neurons are functioning in. This may be of particular utility when comparing two or more regions, given that the receptor populations may differ substantially across them. At the same time, when studying MRS data from multiple participants or timepoints, the homogeneity of the sample becomes relevant, as measurements taken from areas with different cytoarchitecture may be difficult to compare. To provide insights into the likely cytoarchitectural environment of user-defined regions-of-interest, we produced an easy to use tool - InSpectro-Gadget - that interfaces with receptor mRNA expression information from the Allen Human Brain Atlas. This Python tool allows users to input masks and automatically obtain a graphical overview of the receptor population likely to be found within. This includes comparison between multiple masks or participants where relevant. The receptors and receptor subunit genes featured include GABA- and glutamatergic classes, along with a wide range of neuromodulators. The functionality of the tool is explained here and its use is demonstrated through a set of example analyses. The tool is available at https://github.com/lizmcmanus/Inspectro-Gadget.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of Data and Materials

Data for reproducing the analyses described here are available at https://osf.io/rvt2j/.

Code Availability

Code is available at https://github.com/lizmcmanus/Inspectro-Gadget.

References

Download references

Acknowledgements

This work was supported by grants from the Taiwan Ministry of Science and Technology to NWD (110-2628-H-038-001-MY4) and by the Taiwan Ministry of Education Higher Education Sprout Project.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

EM: Conceptualization, Formal Analysis, Methodology, Visualization, Writing - original draft; NM: Conceptualization, Writing - original draft; NWD: Conceptualization, Formal Analysis, Methodology, Visualization, Writing - original draft.

Corresponding author

Correspondence to Niall W. Duncan.

Ethics declarations

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Conflict of Interest/Competing Interests

The authors declare no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McManus, E., Muhlert, N. & Duncan, N.W. InSpectro-Gadget: A Tool for Estimating Neurotransmitter and Neuromodulator Receptor Distributions for MRS Voxels. Neuroinform 22, 135–145 (2024). https://doi.org/10.1007/s12021-024-09654-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-024-09654-w

Keywords

Navigation