Lattice system of functionally distinct cell types in the neocortex

Maruoka, Hisato; Nakagawa, Nao; Tsuruno, Shun; Sakai, Seiichiro; Yoneda, Taisuke; Hosoya, Toshihiko

doi:10.1126/science.aam6125

Cited by 68 publications

(84 citation statements)

References 38 publications

(28 reference statements)

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“…Consistent with the nonrandom connectivity in L2/3, but counter to areal uniformity (Kim et al, 2017), we have found that PV somata and terminals in L1-2 are spatially clustered (Ichinhoe et al 2003;Maruoka et al, 2017;Znamenskiy et al, 2018). Similar to the spatial clustering of IPSC amplitude (Ebina et al, 2014) and consistent with clustering of visual response properties and strong inhibition between visually co-tuned PVs and PNs (Ji et al, 2015;Znamenskiy et al, 2018) the results show stronger local inhibition in interpatches than in patches.…”

Section: Subnetwork-selective Targeting Of Pns and Pvssupporting

confidence: 68%

“…Here, subcortically (i.e. pons, LP, superior colliculus, striatum) projecting L5B PNs overlap with PV clusters suggesting that they receive interpatch input onto thick dendrites in L1 (Kim et al, 2015;Maruoka et al 2017). These cells are sensitive to high TF and low SF, properties we have found in L2/3 of interpatches (Ji et al 2015;Kim et al, 2015).…”

Section: Subnetwork-selective Targeting Of Pns and Pvsmentioning

confidence: 86%

“…Motivated by the report of Maruoka et al, (2017) that L5 neurons in V1 of 6-day-old (P6) mice form 20 µm-wide microcolumns, we have looked for clustered M2 expression in early postnatal development. Our results in tangential sections through V1 of P4 Chrm2tdT mice show that M2 expression in L1 is patchy (10 -30 µm wide, 52-79 µm center-to-center, Figure S3A).…”

Section: Development Of M2 Clustersmentioning

confidence: 99%

“…GABAergic neurons in L2/3 of mouse V1 are clustered in the tangential plane and are radially aligned in L5 with subcortically projecting PNs (Ebina et al, 2014. Maruoka et al, 2017.…”

Section: Clustering Of Gabaergic Neurons It Has Been Reported That Pmentioning

confidence: 99%

“…This finding provides structural evidence for functionally discrete modules and raises the question whether the excitation (E) / inhibition (I) balance in patches and interpatches with diverse spatiotemporal stimulus preferences is spatially organized. That inhibition is not a uniform blanket across V1 (Karnani et al, 2014), but is deployed in spatially clustered patterns by subtypes of PV or somatostatin-(SOM) expressing GABAergic neurons (Ebina et al, 2014;Maruoka et al, 2017), and that activation of these cells including those which express vasoactive intestinal peptide can shape stimulus selectivities of PNs is gradually gaining acceptance (Ayzenshtat et al, 2016;Lee, et al, 2012;Lee et al 2014;Zhu et al, 2015). Whether the inhibitory network is tied to the spatially clustered patch/interpatch system in V1 and provides distinct subnetworks for processing visual information remains unknown.…”

Section: Introductionmentioning

confidence: 99%

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Spatial clustering of inhibition in mouse primary visual cortex

Bista¹,

D’Souza²,

Meier³

et al. 2019

Preprint

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Whether mouse visual cortex contains orderly feature maps is debated. The overlapping pattern of geniculocortical (dLGN) inputs with M2 muscarinic acetylcholine receptor-rich patches in layer 1 (L1) suggests a non-random architecture. Here, we found that L1 inputs from the lateral posterior thalamus (LP) avoid patches and target interpatches. Channelrhodopsin-assisted mapping of EPSCs in L2/3 shows that the relative excitation of parvalbumin-expressing interneurons (PVs) and pyramidal neurons (PNs) by dLGN, LP and cortical feedback are distinct and depend on whether the neurons reside in clusters aligned with patches or interpatches.Paired recordings from PVs and PNs shows that unitary IPSCs are larger in interpatches than patches. The spatial clustering of inhibition is matched by dense clustering of PV-terminals in interpatches. The results show that the excitation/inhibition balance across V1 is organized into patch and interpatch subnetworks which receive distinct long-range inputs and are specialized for the processing of distinct spatiotemporal features. S1), and measured EGFP intensity in patches (top 3 rd ) and interpatches (bottom 3 rd ). We then normalized the pixel values in interpatches to the mean intensity in patches and plotted the counts in different intensity bins. We found that the fluorescence intensity in patches was 2.1 ± 0.024fold (p = 8x10 -18 , Komolgorov-Smirnov test [KS]) higher in patches than interpatches ( Figure 1D). Similar to our previous findings, patches and interpatches in L1 were 60-80 µm wide and their centroids were 120-140 µm apart. dLGN projections to L4, and 5/6 appeared uniform (data not shown). Inputs to L1 from the LP thalamus exhibited a strikingly different pattern, showing 1.6 ± 0.14-fold (p = 1.33x10 -4 , KS) stronger projections to M2-interpatches ( Figure 1E-H).Simultaneous tracing of dLGN and LP inputs with AAV2/1hSyn.tdTomato.WPRE.bGH and AAV2/1.hSyn.EGFP, respectively, confirmed the interdigitating pattern of projections from primary and higher order thalamic nuclei, showing denser LP input to interpatches (p = 7.95 x 10 -4 , KS) ( Figure 1I-M). Clustering of intracortical inputs to L1 of V1We next compared feedback projections from the higher visual cortical ventral stream lateromedial area, LM, to V1 with inputs from the dLGN. Double viral tracings from the dLGN (AAV2/1.hSyn.EGFP) and LM (AAV2/1hSyn.tdTomato.WPRE.bGH) showed that inputs from both sources overlapped in presumtive M2+ patches of L1 ( Figure 1A-C; 2A). Quantitative analysis showed that LM inputs to patches were 1.7 ± 0.05-fold denser than to interpatches (p = 1.45x10 4 , KS) ( Figure 2B). We have shown previously that inputs from the dorsal stream anterolateral area, AL, terminate in M2+ patches (Ji et al., 2015), raising the question whether M2+ patches are the preferred targets of cortical feedback. To address this, we traced the connections to V1 from the posteromedial area, PM, another member of the dorsal stream (Wang et al. 2012). We found that inputs from PM were non-uniform, overlapped i...

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Section: Subnetwork-selective Targeting Of Pns and Pvssupporting

confidence: 68%

Section: Subnetwork-selective Targeting Of Pns and Pvsmentioning

confidence: 86%

Section: Development Of M2 Clustersmentioning

confidence: 99%

“…GABAergic neurons in L2/3 of mouse V1 are clustered in the tangential plane and are radially aligned in L5 with subcortically projecting PNs (Ebina et al, 2014. Maruoka et al, 2017.…”

Section: Clustering Of Gabaergic Neurons It Has Been Reported That Pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial clustering of inhibition in mouse primary visual cortex

Bista¹,

D’Souza²,

Meier³

et al. 2019

Preprint

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Self‐Folding Graphene‐Based Interface for Brain‐Like Modular 3D Tissue

Sakai

Teshima²,

Goto

et al. 2023

Adv Funct Materials

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Electrophysiology of 3D neuronal cultures is of rapidly growing importance for revealing cellular communications associated with neurodevelopment and neurological diseases in their brain‐like 3D environment. Despite that the brain also exhibits an inherent modular architecture that is essential for cortical processing, it remains challenging to interface a modular network consisting of multiple 3D neuronal tissues. Here, a self‐folding graphene‐based electrode array is proposed that enables to reconstruct modular 3D neuronal tissue and investigate firing dynamics among moduli. A graphene‐sandwiched parylene‐C film self‐folds into a cylindrical structure within which living cells can be encapsulated. Culture of encapsulated cells inside the folded graphene enables to spontaneously construct 3D cell aggregates and ensure firm contact between the graphene surface and encapsulated cells. As the inner graphene surface can be utilized as an electrode, the reliable cell–electrode contact allows for long‐term electrical recording from multiple 3D aggregates. Additionally, the modular network consisting of multiple 3D aggregates exhibits richer firing patterns than a conventional homogenous 2D network, which demonstrates that the approach enables measurements of firing dynamics in complex 3D neuronal networks. The deformable graphene electrode will be a powerful platform for investigating complex cellular communications in brain‐like 3D cultures.

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Diversity in spatial frequency, temporal frequency, and speed tuning across mouse visual cortical areas and layers

Wang

Dey

Lagos

et al. 2022

J of Comparative Neurology

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The mouse visual system consists of several visual cortical areas thought to be specialized for different visual features and/or tasks. Previous studies have revealed differences between primary visual cortex (V1) and other higher visual areas, namely, anterolateral (AL) and posteromedial (PM), and their tuning preferences for spatial and temporal frequency. However, these differences have primarily been characterized using methods that are biased toward superficial layers of cortex, such as two‐photon calcium imaging. Fewer studies have investigated cell types in deeper layers of these areas and their tuning preferences. Because superficial versus deep‐layer neurons and different types of deep‐layer neurons are known to have different feedforward and feedback inputs and outputs, comparing the tuning preferences of these groups is important for understanding cortical visual information processing. In this study, we used extracellular electrophysiology and two‐photon calcium imaging targeted toward two different layer 5 cell classes to characterize their tuning properties in V1, AL, and PM. We find that deep‐layer neurons, similar to superficial layer neurons, are also specialized for different spatial and temporal frequencies, with the strongest differences between AL and V1, and AL and PM, but not V1 and PM. However, we note that the deep‐layer neuron populations preferred a larger range of SFs and TFs compared to previous studies. We also find that extratelencephalically projecting layer 5 neurons are more direction selective than intratelencephalically projecting layer 5 neurons.

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Lattice system of functionally distinct cell types in the neocortex

Cited by 68 publications

References 38 publications

Spatial clustering of inhibition in mouse primary visual cortex

Spatial clustering of inhibition in mouse primary visual cortex

Self‐Folding Graphene‐Based Interface for Brain‐Like Modular 3D Tissue

Diversity in spatial frequency, temporal frequency, and speed tuning across mouse visual cortical areas and layers

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