Cerebral chemoarchitecture shares organizational traits with brain structure and function
Abstract
Chemoarchitecture, the heterogeneous distribution of neurotransmitter transporter and receptor molecules, is a relevant component of structure-function relationships in the human brain. Here, we studied the organization of the receptome, a measure of interareal chemoarchitectural similarity, derived from Positron-Emission Tomography imaging studies of 19 different neurotransmitter transporters and receptors. Nonlinear dimensionality reduction revealed three main spatial gradients of cortical chemoarchitectural similarity - a centro-temporal gradient, an occipito-frontal gradient, and a temporo-occipital gradient. In subcortical nuclei, chemoarchitectural similarity distinguished functional communities and delineated a striato-thalamic axis. Overall, the cortical receptome shared key organizational traits with functional and structural brain anatomy, with node-level correspondence to functional, microstructural, and diffusion MRI-based measures decreasing along a primary-to-transmodal axis. Relative to primary and paralimbic regions, unimodal and heteromodal regions showed higher receptomic diversification, possibly supporting functional flexibility.
Data availability
All data and software used in this study is openly accessible. PET data is available at https://github.com/netneurolab/hansen_receptors. FC, SC and MPC data is available at https://portal.conp.ca/dataset?id=projects/mica-mics. ENIGMA data is available through enigmatoolbox (https://github.com/MICA-MNI/ENIGMA). Meta-analytical functional activation data is available through Neurosynth (https://neurosynth.org/analyses/topics/v5-topics-50). The code used to perform the analyses can be found at https://github.com/CNG-LAB/cngopen/receptor_similarity.
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Mapping neurotransmitter systems to the structural and functional organization of the human neocortexgithub, https://doi.org/10.1101/2021.10.28.466336.
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MICA-MICs: a dataset for Microstructure-Informed ConnectomicsCONP, https://n2t.net/ark:/70798/d72xnk2wd397j190qv.
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The ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasetsgithub, https://doi.org/10.1038/s41592-021-01186-4.
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Large-scale automated synthesis of human functional neuroimaging dataneurosynth, https://doi.org/10.1038/nmeth.1635.
Article and author information
Author details
Funding
Max-Planck-Institut für Kognitions- und Neurowissenschaften (Open Access funding)
- Sofie Louise Valk
FRQ-S
- Boris C Bernhardt
Tier-2 Canada Research Chairs program
- Boris C Bernhardt
Human Brain Project
- Simon B Eickhoff
Max Planck Gesellschaft (Otto Hahn award)
- Sofie Louise Valk
Helmholtz International Lab grant agreement (InterLabs-0015)
- Boris C Bernhardt
- Simon B Eickhoff
- Sofie Louise Valk
Canada First Research Excellence Fund (CFREF Competition 2,2015-2016)
- Boris C Bernhardt
- Simon B Eickhoff
- Sofie Louise Valk
European Union's Horizon 2020 (No. 826421 TheVirtualBrain-Cloud"")
- Juergen Dukart
Helmholtz International BigBrain Analytics & Laboratory
- Justine Y Hansen
- Boris C Bernhardt
- Simon B Eickhoff
- Sofie Louise Valk
Natural Sciences and Engineering Research Council of Canada
- Justine Y Hansen
- Boris C Bernhardt
- Bratislav Misic
Canadian Institutes of Health Research
- Boris C Bernhardt
- Bratislav Misic
Brain Canada Foundation Future Leaders Fund
- Boris C Bernhardt
- Bratislav Misic
Canada Research Chairs
- Bratislav Misic
Michael J. Fox Foundation for Parkinson's Research
- Bratislav Misic
SickKids Foundation (NI17-039)
- Boris C Bernhardt
Azrieli Center for Autism Research (ACAR-TACC)
- Boris C Bernhardt
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Birte U Forstmann, University of Amsterdam, Netherlands
Ethics
Human subjects: The current research complies with all relevant ethical regulations as set by The Independent Research Ethics Committee at the Medical Faculty of the Heinrich-Heine-University of Duesseldorf (study number 2018-317). The current data was based on open access resources, and ethic approvals of the individual datasets are available in the original publications of each data source.
Version history
- Preprint posted: August 26, 2022 (view preprint)
- Received: September 30, 2022
- Accepted: July 12, 2023
- Accepted Manuscript published: July 13, 2023 (version 1)
- Version of Record published: July 26, 2023 (version 2)
Copyright
© 2023, Hänisch et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Neuroscience
Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.
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- Neuroscience
Subpopulations of neurons in the subthalamic nucleus have distinct activity patterns that relate to the three hypotheses of the Drift Diffusion Model.