Functional implications of inhibitory synapse placement on signal processing in pyramidal neuron dendrites

KIF5B plays important roles in dendritic spine plasticity and dendritic localization of PSD95 and FMRP in the mouse cortex in vivo

Kinesin 1 (KIF5) is one major type of motor protein in neurons, but its members' function in the intact brain remains less studied. Using in vivo two-photon imaging, we find that conditional knockout of Kif5b (KIF5B cKO) in CaMKIIα-Cre-expressing neurons shows heightened turnover and lower stability of dendritic spines in layer 2/3 pyramidal neurons with reduced spine postsynaptic density protein 95 acquisition in the mouse cortex. Furthermore, the RNA-binding protein fragile X mental retardation protein (FMRP) is translocated to the proximity of newly formed spines several hours before the spine formation events in vivo in control mice, but this preceding transport of FMRP is abolished in KIF5B cKO mice. We further find that FMRP is localized closer to newly formed spines after fear extinction, but this learning-dependent localization is disrupted in KIF5B cKO mice. Our findings provide the crucial in vivo evidence that KIF5B is involved in the dendritic targeting of synaptic proteins that underlies dendritic spine plasticity.

Synaptic and dendritic architecture of different types of hippocampal somatostatin interneurons

GABAergic inhibitory neurons fundamentally shape the activity and plasticity of cortical circuits. A major subset of these neurons contains somatostatin (SOM); these cells play crucial roles in neuroplasticity, learning, and memory in many brain areas including the hippocampus, and are implicated in several neuropsychiatric diseases and neurodegenerative disorders. Two main types of SOM-containing cells in area CA1 of the hippocampus are oriens-lacunosum-moleculare (OLM) cells and hippocampo-septal (HS) cells. These cell types show many similarities in their soma-dendritic architecture, but they have different axonal targets, display different activity patterns in vivo, and are thought to have distinct network functions. However, a complete understanding of the functional roles of these interneurons requires a precise description of their intrinsic computational properties and their synaptic interactions. In the current study we generated, analyzed, and make available several key data sets that enable a quantitative comparison of various anatomical and physiological properties of OLM and HS cells in mouse. The data set includes detailed scanning electron microscopy (SEM)-based 3D reconstructions of OLM and HS cells along with their excitatory and inhibitory synaptic inputs. Combining this core data set with other anatomical data, patch-clamp electrophysiology, and compartmental modeling, we examined the precise morphological structure, inputs, outputs, and basic physiological properties of these cells. Our results highlight key differences between OLM and HS cells, particularly regarding the density and distribution of their synaptic inputs and mitochondria. For example, we estimated that an OLM cell receives about 8,400, whereas an HS cell about 15,600 synaptic inputs, about 16% of which are GABAergic. Our data and models provide insight into the possible basis of the different functionality of OLM and HS cell types and supply essential information for more detailed functional models of these neurons and the hippocampal network.

Role of glia and extracellular matrix in controlling neuroplasticity in the central nervous system

Neuronal plasticity is critical for the maintenance and modulation of brain activity. Emerging evidence indicates that glial cells actively shape neuroplasticity, allowing for highly flexible regulation of synaptic transmission, neuronal excitability, and network synchronization. Astrocytes regulate synaptogenesis, stabilize synaptic connectivity, and preserve the balance between excitation and inhibition in neuronal networks. Microglia, the brain-resident immune cells, continuously monitor and sculpt synapses, allowing for the remodeling of brain circuits. Glia-mediated neuroplasticity is driven by neuronal activity, controlled by a plethora of feedback signaling mechanisms and crucially involves extracellular matrix remodeling in the central nervous system. This review summarizes the key findings considering neurotransmission regulation and metabolic support by astrocyte-neuronal networks, and synaptic remodeling mediated by microglia. Novel data indicate that astrocytes and microglia are pivotal for controlling brain function, indicating the necessity to rethink neurocentric neuroplasticity views.

Regulation of circuit organization and function through inhibitory synaptic plasticity

Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics through mechanisms of synaptic plasticity. Inhibitory plasticity can establish excitation/inhibition (E/I) balance, control neuronal firing, and affect local calcium concentration, hence regulating neuronal activity at the network, single neuron, and dendritic level. Computational models can synthesize multiple experimental results and provide insight into how inhibitory plasticity controls circuit dynamics and sculpts connectivity by identifying phenomenological learning rules amenable to mathematical analysis. We highlight recent studies on the role of inhibitory plasticity in modulating excitatory plasticity, forming structured networks underlying memory formation and recall, and implementing adaptive phenomena and novelty detection. We conclude with experimental and modeling progress on the role of interneuron-specific plasticity in circuit computation and context-dependent learning.

The glucocorticoid footprint on the memory engram

The complexity of the classical inverted U-shaped relationship between cortisol levels and responses transposable to stress reactivity has led to an incomplete understanding of the mechanisms enabling healthy and toxic effects of stress on brain and behavior. A clearer, more detailed, picture of those relationships can be obtained by integrating cortisol effects on large-scale brain networks, in particular, by focusing on neural network configurations from the perspective of inhibition and excitation. A unifying view of Semon and Hebb's theories of cellular memory links the biophysical and metabolic changes in neuronal ensembles to the strengthening of collective synapses. In that sense, the neuronal capacity to record, store, and retrieve information directly relates to the adaptive capacity of its connectivity and metabolic reserves. Here, we use task-activated cell ensembles or simply engram cells as an example to demonstrate that the adaptive behavioral responses to stress result from collective synapse strength within and across networks of interneurons and excitatory ones.

Non-zero mean alpha oscillations revealed with computational model and empirical data

Ongoing oscillations and evoked responses are two main types of neuronal activity obtained with diverse electrophysiological recordings (EEG/MEG/iEEG/LFP). Although typically studied separately, they might in fact be closely related. One possibility to unite them is to demonstrate that neuronal oscillations have non-zero mean which predicts that stimulus- or task-triggered amplitude modulation of oscillations can contribute to the generation of evoked responses. We validated this mechanism using computational modelling and analysis of a large EEG data set. With a biophysical model, we indeed demonstrated that intracellular currents in the neuron are asymmetric and, consequently, the mean of alpha oscillations is non-zero. To understand the effect that neuronal currents exert on oscillatory mean, we varied several biophysical and morphological properties of neurons in the network, such as voltage-gated channel densities, length of dendrites, and intensity of incoming stimuli. For a very large range of model parameters, we observed evidence for non-zero mean of oscillations. Complimentary, we analysed empirical rest EEG recordings of 90 participants (50 young, 40 elderly) and, with spatio-spectral decomposition, detected at least one spatially-filtred oscillatory component of non-zero mean alpha oscillations in 93% of participants. In order to explain a complex relationship between the dynamics of amplitude-envelope and corresponding baseline shifts, we performed additional simulations with simple oscillators coupled with different time delays. We demonstrated that the extent of spatial synchronisation may obscure macroscopic estimation of alpha rhythm modulation while leaving baseline shifts unchanged. Overall, our results predict that amplitude modulation of neural oscillations should at least partially explain the generation of evoked responses. Therefore, inference about changes in evoked responses with respect to cognitive conditions, age or neuropathologies should be constructed while taking into account oscillatory neuronal dynamics.

Phosphorylation of neuroligin-2 by PKA regulates its cell surface abundance and synaptic stabilization

The trans-synaptic adhesion molecule neuroligin-2 (NL2) is essential for the development and function of inhibitory synapses. NL2 recruits the postsynaptic scaffold protein gephyrin, which, in turn, stabilizes γ-aminobutyric acid type A receptors (GABA_ARs) in the postsynaptic domain. Thus, the amount of NL2 at the synapse can control synaptic GABA_AR concentration to tune inhibitory neurotransmission efficacy. Here, using biochemistry, imaging, single-particle tracking, and electrophysiology, we uncovered a key role for cAMP-dependent protein kinase (PKA) in the synaptic stabilization of NL2. We found that PKA-mediated phosphorylation of NL2 at Ser⁷¹⁴ caused its dispersal from the synapse and reduced NL2 surface amounts, leading to a loss of synaptic GABA_ARs. Conversely, enhancing the stability of NL2 at synapses by abolishing PKA-mediated phosphorylation led to increased inhibitory signaling. Thus, PKA plays a key role in regulating NL2 function and GABA-mediated synaptic inhibition.

The synaptic scaffold protein MPP2 interacts with GABAA receptors at the periphery of the postsynaptic density of glutamatergic synapses

Recent advances in imaging technology have highlighted that scaffold proteins and receptors are arranged in subsynaptic nanodomains. The synaptic membrane-associated guanylate kinase (MAGUK) scaffold protein membrane protein palmitoylated 2 (MPP2) is a component of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor–associated protein complexes and also binds to the synaptic cell adhesion molecule SynCAM 1. Using superresolution imaging, we show that—like SynCAM 1—MPP2 is situated at the periphery of the postsynaptic density (PSD). In order to explore MPP2-associated protein complexes, we used a quantitative comparative proteomics approach and identified multiple γ-aminobutyric acid (GABA)_A receptor subunits among novel synaptic MPP2 interactors. In line with a scaffold function for MPP2 in the assembly and/or modulation of intact GABA_A receptors, manipulating MPP2 expression had effects on inhibitory synaptic transmission. We further show that GABA_A receptors are found together with MPP2 in a subset of dendritic spines and thus highlight MPP2 as a scaffold that serves as an adaptor molecule, linking peripheral synaptic elements critical for inhibitory regulation to central structures at the PSD of glutamatergic synapses.

This study shows that the MAGUK scaffold protein MPP2 is located at the periphery of postsynaptic densities in excitatory neurons, where it interacts with GABA-A receptors, thereby serving as a functional adaptor that links excitatory and inhibitory components of synaptic transmission at glutamatergic synapses.

Emergence of synaptic organization and computation in dendrites

Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons' dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.

Unique properties of dually innervated dendritic spines in pyramidal neurons of the somatosensory cortex uncovered by 3D correlative light and electron microscopy

Pyramidal neurons (PNs) are covered by thousands of dendritic spines receiving excitatory synaptic inputs. The ultrastructure of dendritic spines shapes signal compartmentalization, but ultrastructural diversity is rarely taken into account in computational models of synaptic integration. Here, we developed a 3D correlative light-electron microscopy (3D-CLEM) approach allowing the analysis of specific populations of synapses in genetically defined neuronal types in intact brain circuits. We used it to reconstruct segments of basal dendrites of layer 2/3 PNs of adult mouse somatosensory cortex and quantify spine ultrastructural diversity. We found that 10% of spines were dually innervated and 38% of inhibitory synapses localized to spines. Using our morphometric data to constrain a model of synaptic signal compartmentalization, we assessed the impact of spinous versus dendritic shaft inhibition. Our results indicate that spinous inhibition is locally more efficient than shaft inhibition and that it can decouple voltage and calcium signaling, potentially impacting synaptic plasticity.

Reelin Affects Signaling Pathways of a Group of Inhibitory Neurons and the Development of Inhibitory Synapses in Primary Neurons

Reelin is a secretory protein involved in a variety of processes in forebrain development and function, including neuronal migration, dendrite growth, spine formation, and synaptic plasticity. Most of the function of Reelin is focused on excitatory neurons; however, little is known about its effects on inhibitory neurons and inhibitory synapses. In this study, we investigated the phosphatidylinositol 3-kinase/Akt pathway of Reelin in primary cortical and hippocampal neurons. Individual neurons were visualized using immunofluorescence to distinguish inhibitory neurons from excitatory neurons. Reelin-rich protein supplementation significantly induced the phosphorylation of Akt and ribosomal S6 protein in excitatory neurons, but not in most inhibitory neurons. In somatostatin-expressing inhibitory neurons, one of major subtypes of inhibitory neurons, Reelin-rich protein supplementation induced the phosphorylation of S6. Subsequently, we investigated whether or not Reelin-rich protein supplementation affected dendrite development in cultured inhibitory neurons. Reelin-rich protein supplementation did not change the total length of dendrites in inhibitory neurons in vitro. Finally, we examined the development of inhibitory synapses in primary hippocampal neurons and found that Reelin-rich protein supplementation significantly reduced the density of gephyrin-VGAT-positive clusters in the dendritic regions without changing the expression levels of several inhibitory synapse-related proteins. These findings indicate a new role for Reelin in specific groups of inhibitory neurons and the development of inhibitory synapses, which may contribute to the underlying cellular mechanisms of RELN-associated neurological disorders.

Emergence of local and global synaptic organization on cortical dendrites

Synaptic inputs on cortical dendrites are organized with remarkable subcellular precision at the micron level. This organization emerges during early postnatal development through patterned spontaneous activity and manifests both locally where nearby synapses are significantly correlated, and globally with distance to the soma. We propose a biophysically motivated synaptic plasticity model to dissect the mechanistic origins of this organization during development and elucidate synaptic clustering of different stimulus features in the adult. Our model captures local clustering of orientation in ferret and receptive field overlap in mouse visual cortex based on the receptive field diameter and the cortical magnification of visual space. Including action potential back-propagation explains branch clustering heterogeneity in the ferret and produces a global retinotopy gradient from soma to dendrite in the mouse. Therefore, by combining activity-dependent synaptic competition and species-specific receptive fields, our framework explains different aspects of synaptic organization regarding stimulus features and spatial scales.

Experience and activity-dependent control of glucocorticoid receptors during the stress response in large-scale brain networks

The diversity of actions of the glucocorticoid stress hormones among individuals and within organs, tissues and cells is shaped by age, gender, genetics, metabolism, and the quantity of exposure. However, such factors cannot explain the heterogeneity of responses in the brain within cells of the same lineage, or similar tissue environment, or in the same individual. Here, we argue that the stress response is continuously updated by synchronized neural activity on large-scale brain networks. This occurs at the molecular, cellular and behavioral levels by crosstalk communication between activity-dependent and glucocorticoid signaling pathways, which updates the diversity of responses based on prior experience. Such a Bayesian process determines adaptation to the demands of the body and external world. We propose a framework for understanding how the diversity of glucocorticoid actions throughout brain networks is essential for supporting optimal health, while its disruption may contribute to the pathophysiology of stress-related disorders, such as major depression, and resistance to therapeutic treatments.

Shedding light on learning and memory: optical interrogation of the synaptic circuitry

In the quest for the physical substrate of learning and memory, a consensus gradually emerges that memory traces are stored in specific neuronal populations and the synaptic circuits that connect them. In this review, we discuss recent progresses in understanding the reorganization of synaptic circuits and neuronal assemblies associated with learning and memory, with an emphasis on optical techniques for in vivo interrogations. We also highlight some open questions on the missing link between synaptic modifications and neuronal coding, and how stable memory persists despite synaptic and neuronal fluctuations.

Novel Therapeutic Approach for Excitatory/Inhibitory Imbalance in Neurodevelopmental and Neurodegenerative Diseases

Excitatory/inhibitory (E/I) balance, defined as the balance between excitation and inhibition of synaptic activity in a neuronal network, accounts in part for the normal functioning of the brain, controlling, for example, normal spike rate. In many pathological conditions, this fine balance is perturbed, leading to excessive or diminished excitation relative to inhibition, termed E/I imbalance, reflected in network dysfunction. E/I imbalance has emerged as a contributor to neurological disorders that occur particularly at the extremes of life, including autism spectrum disorder and Alzheimer's disease, pointing to the vulnerability of neuronal networks at these critical life stages. Hence, it is important to develop approaches to rebalance neural networks. In this review, we describe emerging therapies that can normalize the E/I ratio or the underlying abnormality that contributes to the imbalance in electrical activity, thus improving neurological function in these maladies.

Efficient phase coding in hippocampal place cells

Neural codes have been postulated to build efficient representations of the external world. The hippocampus, an encoding system, employs neuronal firing rates and spike phases to encode external space. Although the biophysical origin of such codes is at a single neuronal level, the role of neural components in efficient coding is not understood. The complexity of this problem lies in the dimensionality of the parametric space encompassing neural components, and is amplified by the enormous biological heterogeneity observed in each parameter. A central question that spans encoding systems therefore is how neurons arrive at efficient codes in the face of widespread biological heterogeneities. To answer this, we developed a conductance-based spiking model for phase precession, a phase code of external space exhibited by hippocampal place cells. Our model accounted for several experimental observations on place cell firing and electrophysiology: the emergence of phase precession from exact spike timings of conductance-based models with neuron-specific ion channels and receptors; biological heterogeneities in neural components and excitability; the emergence of subthreshold voltage ramp, increased firing rate, enhanced theta power within the place field; a signature reduction in extracellular theta frequency compared to its intracellular counterpart; and experience-dependent asymmetry in firing-rate profile. We formulated phase-coding efficiency, using Shannon's information theory, as an information maximization problem with spike phase as the response and external space within a single place field as the stimulus. We employed an unbiased stochastic search spanning an 11-dimensional neural space, involving thousands of iterations that accounted for the biophysical richness and neuron-to-neuron heterogeneities. We found a small subset of models that exhibited efficient spatial information transfer through the phase code, and investigated the distinguishing features of this subpopulation at the parametric and functional scales. At the parametric scale, which spans the molecular components that defined the neuron, several nonunique parametric combinations with weak pairwise correlations yielded models with similar high phase-coding efficiency. Importantly, placing additional constraints on these models in terms of matching other aspects of hippocampal neural responses did not hamper parametric degeneracy. We provide quantitative evidence demonstrating this parametric degeneracy to be a consequence of a many-to-one relationship between the different parameters and phase-coding efficiency. At the functional scale, involving the cellular-scale neural properties, our analyses revealed an important higher-order constraint that was exclusive to models exhibiting efficient phase coding. Specifically, we found a counterbalancing negative correlation between neuronal gain and the strength of external synaptic inputs as a critical functional constraint for the emergence of efficient phase coding. These observations implicate intrinsic neural properties as important contributors in effectuating such counterbalance, which can be achieved by recruiting nonunique parametric combinations. Finally, we show that a change in afferent statistics, manifesting as input asymmetry onto these neuronal models, induced an adaptive shift in the phase code that preserved its efficiency. Together, our analyses unveil parametric degeneracy as a mechanism to harness widespread neuron-to-neuron heterogeneity towards accomplishing stable and efficient encoding, provided specific higher-order functional constraints on the relationship of neural gain to external inputs are satisfied.

Dendritic inhibition differentially regulates excitability of dentate gyrus parvalbumin-expressing interneurons and granule cells

Fast-spiking parvalbumin-expressing interneurons (PVIs) and granule cells (GCs) of the dentate gyrus receive layer-specific dendritic inhibition. Its impact on PVI and GC excitability is, however, unknown. By applying whole-cell recordings, GABA uncaging and single-cell-modeling, we show that proximal dendritic inhibition in PVIs is less efficient in lowering perforant path-mediated subthreshold depolarization than distal inhibition but both are highly efficient in silencing PVIs. These inhibitory effects can be explained by proximal shunting and distal strong hyperpolarizing inhibition. In contrast, GC proximal but not distal inhibition is the primary regulator of their excitability and recruitment. In GCs inhibition is hyperpolarizing along the entire somato-dendritic axis with similar strength. Thus, dendritic inhibition differentially controls input-output transformations in PVIs and GCs. Dendritic inhibition in PVIs is suited to balance PVI discharges in dependence on global network activity thereby providing strong and tuned perisomatic inhibition that contributes to the sparse representation of information in GC assemblies.

Fast-spiking parvalbumin-expressing interneurons (PVIs) and granule cells of the dentate gyrus receive layer-specific dendritic inhibition. The authors show that distal and proximal dendritic inhibition differentially control input-output transformations in PVIs and granule cells.

Traumatic Stress Produces Delayed Alterations of Synaptic Plasticity in Basolateral Amygdala

Acute traumatic event exposure is a direct cause of post-traumatic stress disorder (PTSD). Amygdala is suggested to be associated with the development of PTSD. In our previous findings, different activation patterns of GABAergic neurons and glutamatergic neurons in early or late stages after stress were found. However, the neural plastic mechanism underlying the role of basolateral amygdala (BLA) in post-traumatic stress disorder remains unclear. Therefore, this study mainly aimed at investigating time-dependent morphologic and electrophysiological changes in BLA during the development of PTSD. We used single prolonged stress (SPS) procedure to establish PTSD model of rats. The rats showed no alterations in anxiety behavior as well as in dendritic spine density or synaptic transmission in BLA 1 day after SPS. However, 10 days after SPS, rats showed enhancement of anxiety behavior, and spine density and frequency of miniature excitatory and inhibitory postsynaptic currents in BLA. Our results suggested that after traumatic stress, BLA displayed delayed increase in both spinogenesis and synaptic transmission, which seemed to facilitate the development of PTSD.