Computer assisted detection of axonal bouton structural plasticity in in vivo time-lapse images

Gala, Rohan; Lebrecht, Daniel; Sahlender, Daniela A.; Jorstad, Anne; Knott, Graham; Holtmaat, Anthony; Stepanyants, Armen

doi:10.7554/elife.29315

Cited by 27 publications

(31 citation statements)

References 34 publications

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“…5d, supplementary image 229 stack). These structures always contained mitochondria which are typically found in presynaptic 230 structures (n = 7 bulbs) (Gala et al 2017). However, compared with the glutamate-filled vesicles in the 231 cone axon terminal, the vesicles in both invaginating tips and bulbs of HCs were barely detectable 232 ( Fig.…”

Section: If Bulbs Are the Site Of Gabaergic Synapses Between Hcs And mentioning

confidence: 96%

Retinal horizontal cells use different synaptic sites for global feedforward and local feedback signaling

Behrens

Zhang

Yadav

et al. 2019

Preprint

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20In the outer plexiform layer (OPL) of the mouse retina, two types of cone photoreceptors (cones) 21 provide input to more than a dozen types of cone bipolar cells (CBCs). This transmission is modulated 22 by a single horizontal cell (HC) type, the only interneuron in the outer retina. Horizontal cells form 23 feedback synapses with cones and feedforward synapses with CBCs. However, the exact 24 computational role of HCs is still debated. Along with performing global signaling within their 25 laterally coupled network, HCs also provide local, cone-specific feedback. Specifically, it has not been 26 clear which synaptic structures HCs use to provide local feedback to cones and global forward 27 signaling to CBCs. 28Here, we reconstructed in a serial block-face electron microscopy volume the dendritic trees of 29 five HCs as well as cone axon terminals and CBC dendrites to quantitatively analyze their 30 connectivity. In addition to the fine HC dendritic tips invaginating cone axon terminals, we also 31 identified "bulbs", short segments of increased dendritic diameter on the primary dendrites of HCs. 32These bulbs are located well below the cone axon terminal base and make contact to other cells mostly 33 identified as other HCs or CBCs. Using immunolabeling we show that HC bulbs express vesicular 34 gamma-aminobutyric acid transporters and co-localize with GABA receptor γ2 subunits. Together, 35 this suggests the existence of two synaptic strata in the mouse OPL, spatially separating cone-specific 36 feedback and feedforward signaling to CBCs. A biophysics-based computational model of a HC 37 dendritic branch supports the hypothesis that the spatial arrangement of synaptic contacts allows 38 simultaneous local feedback and global feedforward signaling. 39

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Section: If Bulbs Are the Site Of Gabaergic Synapses Between Hcs And mentioning

confidence: 96%

Retinal horizontal cells use different synaptic sites for global feedforward and local feedback signaling

Behrens

Zhang

Yadav

et al. 2019

Preprint

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“…We estimated the numerical density and size of en passant presynaptic terminals along afferent axons within the mPFC by labeling them with mCherry delivered by AAV2 injected either into the vHIP or the c-mPFC, and then performing immunohistochemistry of the presynaptic vesicle marker VGLUT1 ( Figures 7A-E). We performed automated detection and size analysis using Bouton Analyzer (Gala et al, 2017) ( Figure 7E). The numerical densities of mCherry-expressing en passant presynaptic terminals belonging to vHIP neurons and those belonging to c-mPFC neurons were comparable between WT and Mecp2 KO mice across all cortical layers of the mPFC (p>0.05, Oneway ANOVA with Tukey's Multiple Comparisons; Figure 7F).…”

Section: Enhanced Vhip-mpfc Synaptic Strength In Mecp2 Ko Micementioning

confidence: 99%

“…All images were acquired using a 63X (1.4 NA) oil immersion objective in an LSM-800 Airyscan confocal microscope (Zeiss, Oberkochen, Germany) using identical settings (laser power, pinhole, photomultiplier tube, current, gain, and offset). Axons were semi-manually traced using NCTracer in FIJI (Longair et al, 2011), and boutons were quantified using BoutonAnalyzer (Gala et al, 2017) running in Matlab (MathWorks, Natick, MA).…”

Section: Immunohistochemistrymentioning

confidence: 99%

Ventral hippocampal projections to the medial prefrontal cortex regulate social memory

Phillips

Robinson

Pozzo-Miller

2018

Preprint

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Inputs from the ventral hippocampus (vHIP) to the medial prefrontal cortex (mPFC) have been implicated in several neuropsychiatric disorders. Here, we show that the long-range vHIP-mPFC projection is hyperactive in the Mecp2 knockout (KO) mouse model of the autism spectrum disorder Rett syndrome, which has deficits in social memory. Chronically mimicking vHIP-mPFC hyperexcitability in wild-type mice impaired social memory, whereas chronic inhibition of mPFC-projecting vHIP neurons in Mecp2 KO mice rescued social memory deficits; the extent of memory rescue was negatively correlated with the strength of vHIP input to the mPFC. Acute manipulations of the vHIP-mPFC projection also affected social memory in a specific and selective manner, suggesting that proper vHIP-mPFC signaling is necessary to recall social memories. In addition, we identified an altered vHIP-mPFC innervation pattern and increased synaptic strength onto layer 5 pyramidal neurons as contributing factors in aberrant vHIP-mPFC signaling in Mecp2 KO mice. Phillips et al. vHIP-mPFC projection regulates social memoryPREPRINT dyes, intracellular recordings, and dual-color optogenetics, we showed that the long-range vHIP-mPFC projection is hyperactive in RTT mice, which results in social memory deficits. Furthermore, chemogenetic manipulation of mPFC-projecting vHIP neurons in wild-type (WT) and RTT mice correlated with social memory performance in a specific and selective manner. Lastly, these behavioral consequences arose from alterations in the morphology and function of excitatory synapses between vHIP axons and pyramidal neurons in layer 5 of the mPFC. RESULTS mPFC-projecting vHIP neurons are selectively activated during social encountersBecause both the vHIP and mPFC have been independently implicated in different aspects of social behavior (Hitti and Siegelbaum, 2014;Liang et al., 2018;Meira et al., 2018;Okuyama et al., 2016;Selimbeyoglu et al., 2017;Yizhar et al., 2011), we first tested if the vHIP projection to the mPFC is selectively engaged during social encounters. To identify vHIP neurons based on their long-range projections, we bilaterally injected green RetroBeads into the prelimbic (PL) region of the mPFC and red RetroBeads into the lateral hypothalamus (LH) of male WT and Mecp2 KO mice at postnatal day 31 (P31), and then we allowed 14 days for RetroBead transfer until P45, when male Mecp2 KO mice are symptomatic (Chen et al., 2001) (Figures 1A and 1B). We placed mice into a testing chamber, where they were sequentially exposed to either two inanimate objects (two novel fake mice) or to other live mice (a co-housed WT littermate and a novel age-matched WT mouse). Forty-five minutes after the last interaction, we perfused the mice and prepared the brain for quantitative immunohistochemistry of the immediate early gene c-Fos as an estimate of neuronal activity ( Figures 1C and 1D). All vHIP neurons in WT mice that interacted with other mice showed higher c-Fos intensities than those in mice that interacted with inanimate objects, irrespect...

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“…Brain networks can reliably store and retrieve long-term memories despite the facts that various sources of errors and noise accompany every step of signal transmission through the network (1), synaptic connectivity changes over time (2)(3)(4), and extraneous sensory inputs are usually present during memory recall. The brain can reduce the effects of noise and extraneous sensory inputs by attending to the memory retrieval process (5,6), but such hinderances cannot be eliminated entirely.…”

Section: Introductionmentioning

confidence: 99%

“…We would like to mention other studies that incorporated sources of noise into the associative learning model and examined the effects of learning on neural network properties. In these studies, the basic associative learning model (7,(19)(20)(21)(22)(23)(24) was extended to include biologically inspired 4 elements, such as sign-constrained postsynaptic connections (inhibitory and excitatory) [see e.g. (9,25,26)], homeostatically constrained presynaptic connections [see e.g.…”

Section: Introductionmentioning

confidence: 99%

Noise in neurons and synapses enables reliable associative memory storage in local cortical circuits

Zhang

Stepanyants

2019

Preprint

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Neural networks in the brain can function reliably despite various sources of errors and noise present at every step of signal transmission. These sources include errors in the presynaptic inputs to the neurons, noise in synaptic transmission, and fluctuations in the neurons' postsynaptic potentials. Collectively they lead to errors in the neurons' outputs which are, in turn, injected into the network. Does unreliable network activity hinder fundamental functions of the brain, such as learning and memory retrieval? To explore this question, this article examines the effects of errors and noise on properties of biologically constrained networks of inhibitory and excitatory neurons involved in associative sequence learning. The associative learning problem is solved analytically and numerically, and it is also shown how memory sequences can be loaded into the network with a more biologically plausible perceptron-type learning rule. Interestingly, the results reveal that errors and noise during learning increase the probability of memory recall. There is a tradeoff 5 the network in an online manner and show that this biologically more plausible method leads to similar network properties as those obtained with the nonlinear optimization and replica methods.Finally, we examine the properties of networks of heterogeneous neurons and make predictions regarding network connectivity. The details of the replica calculation and numerical solutions of the associative memory storage model are provided in SI. RESULTS A. Network model of associative memory storage in the presence of errors and noiseWe modeled associative sequence learning by a local (~100 μm in size), all-to-all potentially (structurally) connected (29, 30) cortical network. The model network consisted of Ninh inhibitory and (N − Ninh) excitatory McCulloch and Pitts neurons (17) ( Figure 1A) and was faced with a task of learning a sequence of consecutive network states, 1 2 1 ... m X X X + →→ , in which X  is a binary vector representing target activities of all neurons at a time step μ, and the ratio m/N is referred to as the memory load. During learning, individual neurons had to independently learn to associate inputs they received from the network with the corresponding target outputs derived from the associative memory sequence. The neurons learned these input-output associations by adjusting the weights of their input connections, ij J (weight of connection from neuron j to neuron i). In contrast to previous studies, we accounted for the fact that learning in the brain is accompanied by several sources of errors and noise. Within the model, these sources are divided into three categories (orange lightning signs in Figure 1A): (1) spiking errors, or errors in X  , (2) synaptic noise, or noise in ij J , and (3) intrinsic noise, which combines all other sources of noise affecting the neurons' postsynaptic potentials. The last category includes background synaptic activity and the stochasticity of ion channels. In the model, this category is equivalent to n...

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Computer assisted detection of axonal bouton structural plasticity in in vivo time-lapse images

Cited by 27 publications

References 34 publications

Retinal horizontal cells use different synaptic sites for global feedforward and local feedback signaling

Retinal horizontal cells use different synaptic sites for global feedforward and local feedback signaling

Ventral hippocampal projections to the medial prefrontal cortex regulate social memory

Noise in neurons and synapses enables reliable associative memory storage in local cortical circuits

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