Mapping neurotransmitter receptor distributions in the macaque cortex

Poster No:


Submission Type:

Abstract Submission 


Thomas Funck1, Ting Xu1, Konrad Wagstyl2, Meiqi Niu3, Lucjia Rapan3, Nicola Palomero-Gallagher4


1Child Mind Institute, New York, NY, 2UCL, London, London, 3Research Centre Jülich, Jülich, Germany, 4Research Centre Jülich, Jülich, Jülich

First Author:

Thomas Funck  
Child Mind Institute
New York, NY


Ting Xu  
Child Mind Institute
New York, NY
Konrad Wagstyl, Dr.  
London, London
Meiqi Niu  
Research Centre Jülich
Jülich, Germany
Lucjia Rapan  
Research Centre Jülich
Jülich, Germany
Nicola Palomero-Gallagher  
Research Centre Jülich
Jülich, Jülich


Quantitative maps of neurotransmitter receptor densities are important tools for characterizing the molecular organization of the brain. We have previously presented a 3D reconstruction pipeline for 2D autoradiographs to create 3D atlases at up to 50μm resolution (Funck, et al 2022). We here use the 3D reconstruction of autoradiographs from a macaque hemisphere to investigate patterns of receptor distribution and the balance of inhibitory, excitatory, and modulatory neurotransmitter receptors.


The brain of an adult male Macaca fascicularis was removed from the skull, the hemispheres separated and each cut into a rostral and a caudal block. The unfixed tissue blocks were frozen at −40 °C and serially sectioned in the coronal plane using a cryostat microtome (CM 3050, Leica, Germany), obtaining sections of 20 μm thickness which were thaw-mounted on gelatine-coated slides and freeze-dried overnight.
Alternating sections were processed for the visualization of 15 different neurotransmitter receptor binding sites using quantitative in vitro receptor autoradiography and previously published protocols (Rappan, et al. 2021). Sections were sparsely sampled from the tissue blocks such that groups of 25 sections were collected throughout the slab with 20 sections discarded between groups. Receptor autoradiographs were digitized using Axiovision (video-based image analysis system; Zeiss, Germany) resulting in 8 bit images with an in-plane resolution of 20µm per pixel, respectively (Palomero-Gallgher & Zilles, 2018).
As no MRI was acquired for the macaque brain, the MEBRAINS template brain (Balan, et al. 2023) was used as the anatomic template to which the autoradiographs were reconstructed with our pipeline. The 3D volumes were reconstructed at 1mm resolution for inhibitory (GABAA, GABAB), excitatory (AMPA, NMDA, Kainate), and modulatory (5-HT1A, 5HT2, M1, M2, D1, α2) receptors.
Reconstructed receptor volumes were smoothed with a gaussian filter (FWHM=3). Receptor densities were then projected onto the 10k vertex Yerkes19 (v1.2) group average mid surface (Donahue et al., 2016, 2018) and normalized by z-score. Vertex-wise receptor gradients were calculated using all the available receptors with a principal component analysis embedding and pearson correlation kernel. The ratios of excitatory to inhibitory receptors, GABAA/GABAB, and inhibitory plus excitatory to modulatory receptors were also computed.


The first and second components of the gradients explained 29% and 16% of the variance respectively (Fig.1.A). The first component highlights the visual cortex (Fig.1.A). The second component illustrates a gradient separating the precuneus, the posterior inferior parietal cortex and the posterior superior temporal cortex from rostrally and caudally adjacent regions (Fig.1.A). The distribution of neurotransmitter receptor ratios indicates that the first component appears to be driven by the ratio of glutamate to GABA receptors and the second component by the proportion of modulatory vs. glutamate and GABA receptors (Fig.1.B). The ratio of fast acting ionotropic GABAA to slower GABAB metabotropic receptors appeared less informative of the observed gradients.
Supporting Image: ohbm2024_figure.png
   ·1.A) Vertex-wise gradients were calculated based on neurotransmitter receptor profiles at each vertex. B) The ratios of inhibitory, excitatory, and modulatory receptors were calculated.


We demonstrate gradients of receptor distribution across the macaque cortex with a particularly strong axis separating the visual cortex, which presents a conspicuously low excitatory to inhibitory ratio. The secondary axis highlights the precuneus and posterior parietal cortex, both of which are part of the default mode network (Raichle 2015). The 2D autoradiograph sections were presently only reconstructed to 1mm resolution to provide gross anatomic information. The data, however, supports reconstruction up to 50μm resolution. We will therefore be able to investigate microscale patterns of receptor distributions to elucidate the molecular architecture of the macaque brain at a previously inaccessible resolution.

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural)

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Anatomy and Functional Systems
Cortical Anatomy and Brain Mapping 1
Transmitter Receptors 2

Neuroinformatics and Data Sharing:

Brain Atlases


Other - Gradient

1|2Indicates the priority used for review

Provide references using author date format

Balan PF, et al. (2023) MEBRAINS: a new population-based monkey template (v1.0) [Data set]. EBRAINS.
Donahue, C., et al. 2016. Using diffusion tractography to predict cortical connection strength and distance: A quantitative comparison with tracers in the monkey. J. Neuroscience 36:6758-6770. PMID: 27335406
Donahue, C.J., et al. 2018. Quantitative assessment of prefrontal cortex in humans relative to nonhuman primates. Proc Natl Acad Sci. 115: E5183-E5192
Funck, T, et al. 2022. 3D reconstruction of ultra-high resolution neurotransmitter receptor atlases in human and non-human primate brains. biorxiv:
Rapan L, et al. 2021. NeuroImage, 226:117574.
Palomero-Gallagher N, Zilles K. 2018. Cyto- and receptorarchitectonic mapping of the human brain. Handbook of Clinical Neurology 150: 355-387.
Raichle ME. The brain's default mode network. Annu Rev Neurosci. 2015 Jul 8;38:433-47. doi: 10.1146/annurev-neuro-071013-014030. Epub 2015 May 4. PMID: 25938726.
Vos de Wael R, et al. (2020). BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol 3, 103.