3D Clustering of GABAergic Neurons Enhances Inhibitory Actions on Excitatory Neurons in the Mouse Visual Cortex

Ebina, Teppei; Sohya, Kazuhiro; Imayoshi, Itaru; Yin, Shuting; Kimura, Rui; Yanagawa, Yuchio; Kubo, Hiroshi; Hioki, Hiroyuki; Kaneko, Takeshi; Tsumoto, Tadaharu

doi:10.1016/j.celrep.2014.10.057

Cited by 19 publications

(18 citation statements)

References 47 publications

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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. This may be due to larger, more proximal and/or the higher density of PV synapses onto PNs (Kubota et al, 2015;Stüber et al, 2015).…”

Section: Subnetwork-selective Targeting Of Pns and Pvssupporting

confidence: 81%

“…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: 81%

“…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%

See 1 more Smart Citation

Spatial clustering of inhibition in mouse primary visual cortex

Bista¹,

D’Souza²,

Meier³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…However, SLS can achieve higher frame rates and it is characterized by an inherently higher ratio between the cellular dwell time and total frame time than imaging with resonant mirrors. For this reason, SLS may be particularly useful in the presence of sparse cellular labeling such that observed in some transgenic animal models expressing GECIs Song et al, 2017) and when imaging specific classes of interneurons (Ebina et al, 2014;Kato et al, 2013) or small astrocyte structures displaying fast calcium kinetics (Bindocci et al, 2017;Stobart et al, 2018). Future developments of SLS may include 3D SLS using electrically tunable lenses (Grewe et al, 2011), two-color SLS, and SLS of functional indicators combined with holographic optogenetic stimulation (Bovetti and Fellin, 2015;Forli et al, 2018;Mardinly et al, 2018;Packer et al, 2015;Papagiakoumou et al, 2010;Yang et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

High-Accuracy Detection of Neuronal Ensemble Activity in Two-Photon Functional Microscopy Using Smart Line Scanning

et al. 2020

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Graphical AbstractHighlights d Smart line scanning (SLS) restricts imaging to the most informative image pixels d SLS substantially increases the signal-to-noise ratio of population GCaMP6 imaging d SLS leads to higher probability of detecting calcium events in population imaging d SLS achieves more precise identification of functional neuronal ensembles SUMMARY Two-photon functional imaging using genetically encoded calcium indicators (GECIs) is one prominent tool to map neural activity. Under optimized experimental conditions, GECIs detect single action potentials in individual cells with high accuracy. However, using current approaches, these optimized conditions are never met when imaging large ensembles of neurons. Here, we developed a method that substantially increases the signal-to-noise ratio (SNR) of population imaging of GECIs by using galvanometric mirrors and fast smart line scan (SLS) trajectories. We validated our approach in anesthetized and awake mice on deep and dense GCaMP6 staining in the mouse barrel cortex during spontaneous and sensory-evoked activity. Compared to raster population imaging, SLS led to increased SNR, higher probability of detecting calcium events, and more precise identification of functional neuronal ensembles. SLS provides a cheap and easily implementable tool for high-accuracy population imaging of neural GCaMP6 signals by using galvanometricbased two-photon microscopes.

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“…In addition to these anatomical differences between mice and higher species, one notable functional difference that has been found is the percentage of responsive neurons in the cortex. Studies have consistently reported that around half of the neurons in mouse V1 respond significantly to grating stimuli (Ebina et al, 2014;Kerlin et al, 2010;Bonin et al, 2011;Mrsic-Flogel et al, 2007;Sohya et al, 2007;Smith and Häusser, 2010;Ayzenshtat et al, 2016;Palagina et al, 2017). However, the few studies done in V1 in primates and cats indicate a much higher percentage of responsive neurons to grating stimuli -typically more than 90% of the cells are responding (Nauhaus et al, 2012;Ikezoe et al, 2013;Kara and Boyd, 2009).…”

Section: Introductionmentioning

confidence: 99%

The influence of cortical depth on neuronal responses in mouse visual cortex

O’Herron

Woodward

Kara

2018

Preprint

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With the advent of two-photon imaging as a tool for systems neuroscience, the mouse has become a preeminent model system for studying sensory processing. One notable difference that has been found however, between mice and traditional model species like cats and primates is the responsiveness of the cortex. In the primary visual cortex of cats and primates, nearly all neurons respond to simple visual stimuli like drifting gratings. In contrast, imaging studies in mice consistently find that only around half of the neurons respond to such stimuli. Here we show that visual responsiveness is strongly dependent on the cortical depth of neurons. Moving from superficial layer 2 down to layer 4, the percentage of responsive neurons increases dramatically, ultimately reaching levels similar to what is seen in other species. Over this span of cortical depth, neuronal response amplitude also increases and orientation selectivity moderately decreases. These depth dependent response properties may be explained by the distribution of thalamic inputs in mouse V1. Unlike in cats and primates where thalamic projections to the granular layer are constrained to layer 4, in mice they spread up into layer 2/3, qualitatively matching the distribution of response properties we see. These results show that the analysis of neural response properties must take into consideration not only the overall cortical lamina boundaries but also the depth of recorded neurons within each cortical layer. Furthermore, the inability to drive the majority of neurons in superficial layer 2/3 of mouse V1 with grating stimuli indicates that there may be fundamental differences in the role of V1 between rodents and other mammals.

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3D Clustering of GABAergic Neurons Enhances Inhibitory Actions on Excitatory Neurons in the Mouse Visual Cortex

Cited by 19 publications

References 47 publications

Spatial clustering of inhibition in mouse primary visual cortex

Spatial clustering of inhibition in mouse primary visual cortex

High-Accuracy Detection of Neuronal Ensemble Activity in Two-Photon Functional Microscopy Using Smart Line Scanning

The influence of cortical depth on neuronal responses in mouse visual cortex

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