The Structure, Dynamics and Function of Neural Micro-Circuits for Perception and Behavior

Editors

Yoshiyuki Kubota

National Institute for Physiological Sciences (NIPS)

Allan T Gulledge

Dartmouth College

M. Victoria Puig

Catalan Institute of Nanoscience and Nanotechnology (CIN2)

Impact

Anatomical connectivity between pairs of mitral cells via one granule cell. (A) Top: Top view of the dendritic fields of MC A (magenta), a second MC B (gray) at distance dAB, and a GC (green) within the overlap area F of the MCs’ dendritic fields. Middle: Illustration of symmetry in NA(r) and NB(r). Bottom: Geometrical parameters relevant for the calculus in Eqs 9, 10 (see also Table 2). rA, rB: distance of GC from MC A resp. B. α: angle between the position of GC soma rA and dAB. αMAX: maximally possible α for the respective dAB. x: distance of projection of GC position onto dAB from MC A; relevant for integration. The gray rectangles represent the integration increments dF. (B) Probability PAB of a GC at position rA to connect to both A and B (Eq. 6), plotted for a set of distances dAB between mitral cells A and B (0–1400 μm). The righthand peak in the curves for 200–700 μm is due to the increased synaptic density around the soma of MC B. Parameters as given in Table 1. (C) IAB: correction of PAB for a distance-dependent reduction in IPSP impact because of attenuation (Figure 2C, Eq. 7).

Original Research

22 July 2022

Anatomical and Functional Connectivity at the Dendrodendritic Reciprocal Mitral Cell–Granule Cell Synapse: Impact on Recurrent and Lateral Inhibition

S. Sara Aghvami

, 1 more and

Veronica Egger

4,308 views

8 citations

Placing the agar cubes farther away made the task more difficult. (A) Examples of different agar cube positions are shown. In each image, the distance from the jaw of the mouse to the agar top is shown. White squares indicate the position of the agar. (B) Example of changes in success scores in three mice during test sessions when the agar cube was presented at 1.3 mm from the jaw. (C) Left, the relationship between session number and success scores from three mice shown in panel (B). Data plotted in light color represent data from individual mice. Red plots and error bars represent mean and standard error, respectively. Right, Success scores in the second half of the session increased compared to the first half (p < 0.001, one-way ANOVA). (D) Success scores are shown in color plots when the distance from the mouse jaw to the ager was changed from 1.3 to 4.2 mm. Data obtained from two mice are shown. The black and pink bar lines indicate sessions in which the agars were located at 1.3 and 4.2 mm, respectively. (E) Top, from the data in panel (D), the relationship between session number and mean success score are shown. Gray and black lines indicate data from two different mice. Bottom, success scores decreased when the agar cube was placed at 4.2 mm compared to the condition with the agar cube placed at 1.3 mm (p < 0.001, one-way ANOVA). (F) The reaching movements depend on M1 activity. Left, reaching was suppressed by the photostimulation to M1 on the left (contralateral) side of VGAT-ChR2 mice; with photostim.: 55/80 trials (68.8%), without photostim.: 9/391 trials (2.3%), 2 mice, p < 0.001, Chi-square test. Middle, reaching was not suppressed by the photostimulation of M1 on the right (ipsilateral) side; with photostim.: 2/30 trials (6.7%), without photostim.: 1/133 trials (0.8%). Right, reaching was not suppressed by the photostimulation of M1 on the left (contralateral) side of wild-type mice; with photostim.: 0/36 trials (0%), without photostim.: 2/161 trials (1.2%).

Methods

12 May 2022

A Novel Device of Reaching, Grasping, and Retrieving Task for Head-Fixed Mice

Satoshi Manita

, 1 more and

Kazuo Kitamura

4,714 views

1 citations

Spatial frequency tuning of LPN and LGN boutons in V1. (A) The proportion of SPF-selective LPN and LGN boutons. The proportion of LPN boutons in the layers 1 and 5 was significantly different from that of LGN boutons in all layers (P ≤ 0.01, Mann–Whitney U-test with Bonferroni correction). The proportion of LPN boutons in layer 4 was significantly different from that of LGN boutons in all layers (layer 1, p = 0.024; layer 2/3, p = 0.032; layer 5, p = 0.047, Mann–Whitney U-test with Bonferroni correction). (B) The mean preferred SPF of responsive LPN and LGN boutons. Mean preferred frequency of LPN boutons of all the layers had significant difference from LGN boutons of all the layers (P ≤ 0.001, Mann–Whitney U-test with Bonferroni correction). Mean preferred frequency among LPN boutons had no significant difference (between layers 1 and 2/3, p = 0.896; other pairs, p = 1, Mann–Whitney U-test with Bonferroni correction). Mean preferred frequency of the layer 4 LGN boutons was significantly different from LGN boutons of layer 1 and V1 neurons in layer 4 (LGN boutons in layer 1, p = 0.037; V1 neurons in layer 4, p = 0.013, Mann–Whitney U-test with Bonferroni correction). (C) The distribution of the preferred SPFs of LPN boutons in the layer 1 (left, n = 893 boutons from six mice, 13 FOVs), layer 2/3 (second from left, n = 659 boutons from six mice, 9 FOVs), layer 4 (second from right, n = 844 boutons from five mice, 8 FOVs), and layer 5 (right, n = 1,000 boutons from three mice, 13 FOVs). (D) The distribution of the preferred SPFs of LGN boutons in the layer 1 (left, n = 3,817 boutons from 20 mice, 42 FOVs), layer 2/3 (second from left, n = 2,669 boutons from 18 mice, 29 FOVs), and layer 4 (second from right, n = 5,576 boutons from 20 mice, 55 FOVs), and V1 neurons in the layer 4 (right, n = 6,210 neurons from 9 mice, 12 volumes). LPN, lateral posterior nucleus; LGN, lateral geniculate nucleus; V1, primary visual cortex; SPF, spatial frequency; FOV, field-of-view; cpd, cycle per degree. *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001. The data of LGN axons and V1 neurons are re-use from the previous publication (Kondo and Ohki, 2016).

Original Research

28 February 2022

Response Selectivity of the Lateral Posterior Nucleus Axons Projecting to the Mouse Primary Visual Cortex

Satoru Kondo

, 1 more and

Kenichi Ohki

7,350 views

2 citations

Original Research

18 February 2022

Cell-Type Specific Neuromodulation of Excitatory and Inhibitory Neurons via Muscarinic Acetylcholine Receptors in Layer 4 of Rat Barrel Cortex

Guanxiao Qi

and

Dirk Feldmeyer

7,197 views

9 citations

Sensitivity of network dynamic to hub attack is mostly attributed to attacks on L5 PCs. (A) Percentages of synapses for excitatory (brown-red arrows) and inhibitory (blue-green arrows) connections in the NMC microcircuit (layer 1 omitted). Arrow width and corresponding numbers indicate the percentage of total synapses formed by this pathway (omitted for pathways with <1% of synapses). The total percentage of plotted synapses is 98%; the remaining 2% originate in layer 1. Rectangle sizes are proportional to the sizes of the corresponding number of excitatory or inhibitory populations. (B) The connectivity among layers and cell-types is visualized using the force-directed graph algorithm (see section “Materials and Methods”). The network is composed of 55 morphological cell-types (circular nodes, colors match to that in A); edges strength corresponds to pairwise connection probabilities. Strongly connected cell-types are displayed closer in space. (C) Same as (B), but for the network following an attack on 15,000 hub neurons. Note the disappearance of the large nodes in Layer 5. (D–G) Different quantification of network activity for layer-specific hub attack, global hub attack and random nodes attack. (D) Number of bursts/sec, (E) average network firing rate, (F) coefficient of variation, (G) global SPIKE synchronization measure (see section “Materials and Methods”). Note that attack of L5 pyramidal cells is the most disruptive layer attack.

Original Research

24 September 2021

The Role of Hub Neurons in Modulating Cortical Dynamics

Eyal Gal

, 5 more and

Idan Segev

9,033 views

15 citations

Proposed mechanisms for establishing stereotyped patterns of synaptic connectivity in the neocortex. (A) Predictable patterns of synaptic connectivity may be defined by anatomical relationships between presynaptic axons and postsynaptic dendrites. For example, the axodendritic overlap between Input 1 (green) and the two pyramidal cell types (magenta, blue) precludes synapse formation with the blue cell type. The axodendritic overlap of Input 2 (orange) and the two pyramidal cell types predicts a higher probability of connection with the blue type than the magenta type. (B) Fine-scale neurite geometry such as axon tortuosity and dendritic spine outgrowth may contribute to synaptic partner selection. Growth patterns of axons and dendrites at small scales alter the amount of apposed membrane between cells, and thus the number of potential synaptic sites. Inhibitory interneuron axons (Inh, blue) in the cortex are highly tortuous, which can increase potential sites of contact with the dendrites of preferred synaptic partners, allowing the formation of more synapses between them. The axons of excitatory pyramidal neurons (Exc, orange) are more linear but directed dendritic spine growth in postsynaptic neurons (Exc, magenta) could also allow for the preferential formation of synaptic sites. (C) Lineage relationships affect synaptic connectivity. Clonally related pyramidal cells (sister cells) arising from the same neural progenitor lineage are preferentially interconnected by gap junctions () during the first postnatal week (postnatal day 0–6; P0–P6). These gap junctions disappear during the second postnatal week (∼P10+), and chemical synapses are preferentially formed between clonally related pyramidal cells. (D) Activity-dependent plasticity may guide cell-type biased synaptic targeting. Different axonal inputs to a cortical cell type may have distinct neural activity patterns, and synapses between specific cell-type combinations may be selectively strengthened (orange input) or weakened (green input) through activity-dependent mechanisms, leading to preferential connectivity between cortical cell types. (E) Specific expression of recognition molecules may mediate cell-type or domain-specific synaptic targeting and synapse-type-specific functional properties. During development, preferred synaptic partner cell types may express cognate receptors and ligands (red) on the pre- (orange) and postsynaptic processes (purple). These molecular signals guide growth and synaptogenesis, leading to cell-type-biased connectivity and function in adulthood.

Review

24 September 2021

Mechanisms Underlying Target Selectivity for Cell Types and Subcellular Domains in Developing Neocortical Circuits

Alan Y. Gutman-Wei

and

Solange P. Brown

8,946 views

7 citations

Review

12 July 2021

Prefrontal GABAergic Interneurons Gate Long-Range Afferents to Regulate Prefrontal Cortex-Associated Complex Behaviors

Sha-Sha Yang

, 2 more and

Wen-Jun Gao

Prefrontal cortical GABAergic interneurons (INs) and their innervations are essential for the execution of complex behaviors such as working memory, social behavior, and fear expression. These behavior regulations are highly dependent on primary long-range afferents originating from the subcortical structures such as mediodorsal thalamus (MD), ventral hippocampus (vHPC), and basolateral amygdala (BLA). In turn, the regulatory effects of these inputs are mediated by activation of parvalbumin-expressing (PV) and/or somatostatin expressing (SST) INs within the prefrontal cortex (PFC). Here we review how each of these long-range afferents from the MD, vHPC, or BLA recruits a subset of the prefrontal interneuron population to exert precise control of specific PFC-dependent behaviors. Specifically, we first summarize the anatomical connections of different long-range inputs formed on prefrontal GABAergic INs, focusing on PV versus SST cells. Next, we elaborate on the role of prefrontal PV- and SST- INs in regulating MD afferents-mediated cognitive behaviors. We also examine how prefrontal PV- and SST- INs gate vHPC afferents in spatial working memory and fear expression. Finally, we discuss the possibility that prefrontal PV-INs mediate fear conditioning, predominantly driven by the BLA-mPFC pathway. This review will provide a broad view of how multiple long-range inputs converge on prefrontal interneurons to regulate complex behaviors and novel future directions to understand how PFC controls different behaviors.

14,660 views

33 citations

Summary diagram: long-range VIP-expressing neuron projections. Top right: schematic representation of auditory cortex injection with AAV-flex-ChRimson-tdTomato. VIP long-range axons were found in several areas: AC, auditory cortex, right, and left; cc, corpus callosum; ipsilateral ICe, inferior colliculus, external; ipsilateral IC, inferior colliculus; ipsilateral LA, lateral amygdala; ipsilateral MGB, medial geniculate body, ipsilateral SC, superior colliculus; ipsilateral STR, striatum; ipsilateral TeA, temporal association cortex.

Original Research

23 July 2021

Corticofugal VIP Gabaergic Projection Neurons in the Mouse Auditory and Motor Cortex

Alice Bertero

, 1 more and

Alfonso junior Apicella

5,886 views

9 citations

Distribution patterns of bilateral inputs in PAL subregions between the two CA1 tracing groups. (A) Representative images showing MS and NDB inputs to the dorsal or ventral CA1 PNs. Scale bar: 500 μm. (B) CSI of the inputs in bilateral PAL subregions. (C) Distribution patterns of the input neurons along the RC axis in the bilateral MS or NDB. (D) The contralateral-ipsilateral distribution patterns of monosynaptic inputs in MS or NDB of two tracing groups. (E) The dorsal-ventral distribution patterns of monosynaptic inputs in MS or NDB of two tracing groups. n.s., no significant difference; **P < 0.01.

Original Research

14 April 2021

Whole-Brain Mapping the Direct Inputs of Dorsal and Ventral CA1 Projection Neurons

Sijue Tao

, 7 more and

Fuqiang Xu

16,917 views

41 citations

$The NLGN3-R451C mutation causes the enhancement of inhibitory synaptic transmission and the elevation of inhibition/excitation ratio of synaptic inputs to PCs. (A) Representative traces of mIPSC recorded from PCs of wild-type (upper trace) and NLGN3-R451C mutant (lower trace) mice (top) at Vh of −70 mV in the presence of 1 μM tetrodotoxin, 10 μM NBQX, and 5 μM R-CPP (N = 3/group). Scale bars, 250 ms and 50 pA. Cumulative distribution plots and summary bar graphs for the mIPSC amplitude (lower left; inset shows the average mIPSC amplitude) and for the inter-event interval (lower right; inset shows the average mIPSC frequency) in PCs of wild-type and NLGN3-R451C mutant mice aged P21–P35. For the mIPSC amplitude, ***p < 0.0001 by Kolmogorov-Smirnov test in the cumulative distribution plot and ***p = 0.0002 by Mann-Whitney U test in the bar graph. For the inter-event interval, p = 0.99 by Kolmogorov-Smirnov test in the cumulative distribution plot and p = 0.9 by Mann-Whitney U test in the bar graph. (B) Summary graph for the mIPSC amplitude of wild-type (open columns) and NLGN3-R451C mutant (orange columns) mice at indicated ages. Note that the mean amplitudes of mIPSCs in NLGN3-R451C mutant mice are significantly larger than those of wild-type mice during postnatal development (P7–P9, *p = 0.029; P10–P12, **p = 0.0053; P13–P15, **p = 0.0083; P16–P20, *p = 0.024 by Mann-Whitney U test). (C) Representative traces of mEPSC recorded from PCs of wild-type (upper trace) and NLGN3-R451C mutant (lower trace) mice (top) at Vh of −70 mV in the presence of 1 μM tetrodotoxin and 100 μM PTX (N = 3/group). Scale bars, 250 ms and 20 pA. Cumulative distribution plots for the mEPSC amplitude (lower left; inset shows the average mEPSC amplitude) and for the inter-event interval (lower right; inset shows the average mEPSC frequency) in PCs of wild-type and NLGN3-R451C mutant mice aged P21–P35. For the mEPSC amplitude, p = 0.17 by Kolmogorov-Smirnov test in the cumulative distribution plot and p = 0.40 by Mann-Whitney U test in the bar graph. For the inter-event interval, p = 0.84 by Kolmogorov-Smirnov test in the cumulative distribution plot and p = 0.59 by Mann-Whitney U test in the bar graph. (D) Summary graph for the mEPSC amplitude of wild-type (open columns) and NLGN3-R451C mutant (orange columns) mice at indicated ages (P6–P9, p = 0.074; P10–P12, p = 0.76; P13–P15, p = 0.350; P16–P20, p = 0.10 by Mann-Whitney U test). (E) Representative traces of mIPSC recorded from PCs of wild-type (upper) and NLGN3-R451C mutant (lower) mice at P10 in the presence of 1 μM tetrodotoxin, 10 μM NBQX, and 5 μM R-CPP. Vh = −70 mV. Scale bars, 250 ms and 100 pA. (F) Cumulative fractions of the rise time of small (<100 pA) (gray dotted line) and large (>100 pA) (black line) mIPSCs in wild-type (left) and NLGN3-R451C mutant (right) mice from P10 to P12. ***p < 0.0001 by Kolmogorov-Smirnov test for both genotypes. (G) Summary bar graphs for the mIPSC amplitude for all, small and large events in wild-type (open columns) and NLGN3-R451C mutant (orange columns) mice from P10 to P12. **p = 0.0053 (All); **p = 0.0014 (Small); *p = 0.0484 (Large) by Mann-Whitney U test. (H) Experimental protocol used to measure the inhibition/excitation ratio. (I) Representative traces of evoked AMPAR- and GABAAR-mediated synaptic currents in wild-type (left) and NLGN3-R451C mutant (right) mice. Scale bars, 10 ms and 200 pA. (J) Amplitudes of AMPAR- (left) and GABAAR- (right) mediated synaptic currents from wild-type (black) and NLGN3-R451C mutant (orange) mice. (K) Average inhibition/excitation ratio from wild-type (open column) and NLGN3-R451C mutant (orange column) mice aged P19–P25. ***p = 0.0004 by Mann-Whitney U test. Data are expressed as mean ± SD. Total number of cells recorded/total number of mice used is indicated within or beneath individual columns.$

Original Research

28 June 2021

An Autism-Associated Neuroligin-3 Mutation Affects Developmental Synapse Elimination in the Cerebellum

Esther Suk King Lai

, 6 more and

Masanobu Kano

9,083 views

24 citations

$Aging decreases neuronal calcium activity in the motor cortex during resting and movement states. (A) Analysis of the animals’ gait patterns when walking on a treadmill. Old mice displayed higher proportions of drag, wobble, and sweep but less steady run after 1-h training. (B) Treadmill performance in young adults, mature adults, and old mice. Treadmill performance is expressed as a percent increase in the fraction of time spent in a steady run between post-training and pre-training (n = 10, 8, 13 mice per group). (C) Schematic showing two-photon Ca2+ imaging in the primary motor cortex of head-restrained mice walking on a treadmill. (D) Representative images of L5 somas expressing GCaMP6s when the treadmill is off and on. Scale bar, 20 μm. (E) Calcium fluorescence traces of representative pyramidal somas in young adult and old mice. (F) The average integrated activity of somatic calcium transients while the treadmill is off and on (Young: n = 116 cells from 6 mice; Old: n = 187 cells from 9 mice). (G) Normalized somatic calcium activity in young adult and old mice during treadmill training. Individual circles represent data from a single cell. Summary data are presented as mean ± SEM. *P < 0.05, ***P < 0.001, ****P < 0.0001 by two-tailed Student’s t-test in panel (B) and two-tailed Mann–Whitney tests in panels (F,G).$

Original Research

26 November 2020

Learning-Dependent Dendritic Spine Plasticity Is Reduced in the Aged Mouse Cortex

Lianyan Huang

, 3 more and

Guang Yang

Aging is accompanied by a progressive decrease in learning and memory function. Synaptic loss, one of the hallmarks of normal aging, likely plays an important role in age-related cognitive decline. But little is known about the impact of advanced age on synaptic plasticity and neuronal function in vivo. In this study, we examined the structural dynamics of postsynaptic dendritic spines as well as calcium activity of layer 5 pyramidal neurons in the cerebral cortex of young and old mice. Using transcranial two-photon microscopy, we found that in both sensory and motor cortices, the elimination rates of dendritic spines were comparable between young (3–5 months) and mature adults (8–10 months), but seemed higher in old mice (>20 months), contributing to a reduction of total spine number in the old brain. During the process of motor learning, old mice compared to young mice had fewer new spines formed in the primary motor cortex. Motor training-evoked somatic calcium activity in layer 5 pyramidal neurons of the motor cortex was also lower in old than young mice, which was associated with the decline of motor learning ability during aging. Together, these results demonstrate the effects of aging on learning-dependent synapse remodeling and neuronal activity in the living cortex and suggest that synaptic deficits may contribute to age-related learning impairment.

5,813 views

22 citations