Poster No:
2166
Submission Type:
Abstract Submission
Authors:
Marina Saito1,2,3, Lucjia Rapan4, Meiqi Niu4, Ling Chao4, Sei-ichi Tsujimura2, Nicola Palomero-Gallagher4,5, Hiromasa Takemura1,6,7
Institutions:
1National Institute for Physiological Sciences, Okazaki, Japan, 2Nagoya City University, Nagoya, Japan, 3Japan Society for the Promotion of Science, Tokyo, Japan, 4Research Centre Jülich, Jülich, Germany, 5Heinrich Heine University Düsseldorf, Düsseldorf, Germany, 6The Graduate Institute of Advanced Studies, SOKENDAI, Hayama, Japan, 7Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, NICT, Suita, Japan
First Author:
Marina Saito
National Institute for Physiological Sciences|Nagoya City University|Japan Society for the Promotion of Science
Okazaki, Japan|Nagoya, Japan|Tokyo, Japan
Co-Author(s):
Meiqi Niu
Research Centre Jülich
Jülich, Germany
Ling Chao
Research Centre Jülich
Jülich, Germany
Hiromasa Takemura
National Institute for Physiological Sciences|The Graduate Institute of Advanced Studies, SOKENDAI|Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, NICT
Okazaki, Japan|Hayama, Japan|Suita, Japan
Introduction:
The lateral geniculate nucleus (LGN) is one of the thalamic nuclei which process visual information from the retina and transmit visual signals to the cortex. The LGN is composed of three subdivisions: the magnocellular, parvocellular and koniocellular (Figure 1A). Although the structure and function of LGN has been extensively studied through various methods (Hubel & Livingstone, 1990; Denison et al., 2014; Oishi et al., 2023), its receptor architecture has not yet been fully understood so far. As the regional and laminar distribution patterns of different receptors in the cerebral cortex has been shown to be a powerful tool for understanding the potential functional segregation as well as hierarchical organization among different brain areas (Rapan et al., 2022), here we aim to elucidate the receptor architecture of the LGN by analyzing receptor autoradiographs obtained from macaque monkey brains for better understanding underlying relationship between receptor architecture and function of the LGN (Palomero-Gallagher & Zilles, 2018).

Methods:
We analyzed coronal sections from four hemispheres of three adult male Macaca fascicularis (Rapan et al., 2022). We quantified the densities of 15 receptors visualized by in vitro receptor autoradiography using previously established protocols (Palomero-Gallagher & Zilles, 2018; Zilles et al., 2002). We delineated LGN sublayers as regions-of-interest (ROIs) in both cell-body stained sections and the adjacent receptor autoradiographs and extracted the density of each receptor type in each, the borders between each layer were charted on the pseudocolor-coded autoradiographs (Figure 1B). The magnocellular, parvocellular, and koniocellular layers were defined as ROIs, the densities of each ROI were measured by calculating the average receptor densities from their component sublayers, respectively. Finally, the mean densities of all receptors were visualized for each ROI separately in a polar plot as a 'receptor fingerprint'. To evaluate differences in receptor architecture between the LGN and visual cortex, we compared the receptor fingerprints with those of primary visual areas published by Rapan et al., 2021.
Results:
The color-coded autoradiographs of representative sections in Figure 1B show the exemplary receptor distribution patterns of the LGN. We found a considerably higher concentration of acetylcholine M2 and α4β2 receptors in the LGN than in the cortex. Furthermore, the distinct cytoarchitectonic layers in the LGN are clearly reflected by the receptor architecture. The receptor fingerprints of all three ROIs were similar in shape, but differ in size (Figure 2A): magnocellular part exhibited the highest densities, while the koniocellular ones exhibited the lowest densities, whereby M2 receptors presented the highest overall receptor densities, followed by the M3, α4β2. Lowest densities were measured for the AMPA and M1 receptors. This balance resulted in fingerprints that differed considerably in both size and shape from those of the dorsal and ventral parts of V1 (Figure 2B), where the highest densities are reached by the GABAergic and glutamate receptors.
Conclusions:
The present study showed that the LGN presents considerably higher M2, and α4β2 receptor densities than does V1 and its three subdivisions differ in their averaged densities. The high density of LGN in M2 and α4β2 receptors may be related to the functional roles of acetylcholine during sensory information processing, such as involvement with improving signal-to-noise ratio of neural response to sensory stimuli, by decreasing noise correlation among neurons and improving encoding efficiency (Minces et al., 2017). Our results suggest that the receptor architecture of the LGN may be geared towards improving the encoding efficiency of visual information before it is forwarded to cortical visual areas.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Receptors 1
Perception, Attention and Motor Behavior:
Perception: Visual 2
Keywords:
Acetylcholine
GABA
Neurotransmitter
RECEPTORS
Vision
1|2Indicates the priority used for review
Provide references using author date format
Denison, R. N. (2014), ‘Functional mapping of the magnocellular and parvocellular subdivisions of human LGN’, NeuroImage, vol.102, part 2, 358-369.
Hubel, D.H. (1990), ‘Color and contrast sensitivity in the lateral geniculate body and primary visual cortex of the macaque monkey’, Journal of Neuroscience, 10 (7), 2223-2237
Hubel, D.H. (1995), ‘Eye, brain and vision’, American Library / Scientific American Books.
Minces, V. (2017), ‘Cholinergic shaping of neural correlations’, Proceedings of the National Academy of Sciences, 114(22), 5725-5730.
Oishi, H. (2023), ‘Macromolecular tissue volume mapping of lateral geniculate nucleus subdivisions in living human brains’, NeuroImage, vol.265, 119777.
Palomero-Gallagher, N. (2018) ‘Cyto-and receptor architectonic mapping of the human brain’, Handbook of Clinical Neurology, 150, 355-387.
Rapan, L. (2022), ‘Receptor architecture of macaque and human early visual areas: not equal, but comparable’, Brain Structure Function, 227, 1247-1263.
Zilles, K. (2002), ‘Quantitative analysis of cyto-and receptor architecture of the human brain’, Brain mapping: the methods, 573-602.