Excitatory-inhibitory Circuits Research Articles

The volatile anesthetic isoflurane differentially inhibits voltage-gated sodium channel currents between pyramidal and parvalbumin neurons in the prefrontal cortex.

How volatile anesthetics work remains poorly understood. Modulations of synaptic neurotransmission are the direct cellular mechanisms of volatile anesthetics in the central nervous system. Volatile anesthetics such as isoflurane may reduce neuronal interaction by differentially inhibiting neurotransmission between GABAergic and glutamatergic synapses. Presynaptic voltage-dependent sodium channels (Nav), which are strictly coupled with synaptic vesicle exocytosis, are inhibited by volatile anesthetics and may contribute to the selectivity of isoflurane between GABAergic and glutamatergic synapses. However, it is still unknown how isoflurane at clinical concentrations differentially modulates Nav currents between excitatory and inhibitory neurons at the tissue level. In this study, an electrophysiological recording was applied in cortex slices to investigate the effects of isoflurane on Nav between parvalbumin (PV+) and pyramidal neurons in PV-cre-tdTomato and/or vglut2-cre-tdTomato mice. Isoflurane at clinically relevant concentrations produced a hyperpolarizing shift in the voltage-dependent inactivation and slowed the recovery time from the fast inactivation in both cellular subtypes. Since the voltage of half-maximal inactivation was significantly depolarized in PV+ neurons compared to that of pyramidal neurons, isoflurane inhibited the peak Nav currents in pyramidal neurons more potently than those of PV+ neurons (35.95 ± 13.32% vs. 19.24 ± 16.04%, P = 0.036 by the Mann-Whitney test). Isoflurane differentially inhibits Nav currents between pyramidal and PV+ neurons in the prefrontal cortex, which may contribute to the preferential suppression of glutamate release over GABA release, resulting in the net depression of excitatory-inhibitory circuits in the prefrontal cortex.

Maturation Delay of Human GABAergic Neurogenesis in Fragile X Syndrome Pluripotent Stem Cells.

Fragile X Syndrome (FXS), the leading monogenic cause of intellectual disability and autism spectrum disorder, is caused by expansion of a CGG trinucleotide repeat in the 5ʹ-UTR of the Fragile X Mental Retardation-1 (FMR1) gene. Epigenetic silencing of FMR1 results in loss of the Fragile X Mental Retardation Protein (FMRP). Although most studies to date have focused on excitatory neurons, recent evidence suggests that GABAergic inhibitory networks are also affected. To investigate human GABAergic neurogenesis, we established a method to reproducibly derive inhibitory neurons from multiple FXS and control human pluripotent stem cell (hPSC) lines. Electrophysiological analyses suggested that the developing FXS neurons had a delay in the GABA functional switch, a transition in fetal development that converts the GABAA channel’s function from depolarization to hyperpolarization, with profound effects on the developing brain. To investigate the cause of this delay, we analyzed 14 400 single-cell transcriptomes from FXS and control cells at 2 stages of GABAergic neurogenesis. While control and FXS cells were similar at the earlier time point, the later-stage FXS cells retained expression of neuroblast proliferation-associated genes and had lower levels of genes associated with action potential regulation, synapses, and mitochondria compared with controls. Our analysis suggests that loss of FMRP prolongs the proliferative stage of progenitors, which may result in more neurons remaining immature during the later stages of neurogenesis. This could have profound implications for homeostatic excitatory-inhibitory circuit development in FXS, and suggests a novel direction for understanding disease mechanisms that may help to guide therapeutic interventions.

Neonatal inflammation via persistent TGF-β1 downregulation decreases GABAAR expression in basolateral amygdala leading to the imbalance of the local excitation-inhibition circuits and anxiety-like phenotype in adult mice

Neonatal inflammation can increase the risk of anxiety disorder in adulthood. The balance between glutamatergic excitatory and GABAergic inhibitory transmissions in the basolateral amygdala (BLA) plays a vital role in controlling anxiety state. Based on the reports that early-life inflammation had adverse effects on GABAergic system, the aim of this study was to investigate whether and how neonatal inflammation affects excitatory-inhibitory circuits in the BLA resulting in anxiety disorder. Neonatal mice received a daily subcutaneous injection of lipopolysaccharide (LPS, 50 μg/kg) or saline on postnatal days 3–5. LPS-treated mice developed anxiety behaviors accompanied by the hyperactivity of adrenal axis in adulthood. Electrophysiological study revealed the increase of postsynaptic neuronal excitability in the cortical-BLA excitatory synapses of LPS mice which could be recovered by bath-application of GABAAR agonist suggesting the impairment of GABAergic system in LPS mice. Compared with controls, GABAARα2 subunit expression and density of GABA-evoked current in BLA principal neurons were reduced in LPS mice. Additionally, neonatal LPS treatment resulted in the down-regulation of transforming growth factor-beta 1 (TGF-β1) expression and PKC signaling pathway in the adult BLA. The local TGF-β1 overexpression in the BLA improved GABAARα2 expression via up-regulating the activity of PKC signaling, which corrected GABAAR-mediated inhibition leading to the abolishment of anxiety-like change in adrenal axis regulation and behaviors in LPS mice. These data suggest the persistent TGF-β1deficit induces the down-regulation of GABAARα2 expression and subsequent disruption of the excitation-inhibition balance in the BLA circuits, which is the important mechanisms of neonatal inflammation-induced anxiety disorder.

Activity Stabilization in a Population Model of Working Memory by Sinusoidal and Noisy Inputs.

According to mechanistic theories of working memory (WM), information is retained as stimulus-dependent persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains a largely open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e., they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the “common-noise” groups compared to the “independent-noise” groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.

MPTP-Induced Dopamine Depletion in Basolateral Amygdala via Decrease of D2R Activation Suppresses GABAA Receptors Expression and LTD Induction Leading to Anxiety-Like Behaviors.

Anxiety disorders commonly occur in Parkinson’s disease. Using field potential recording and patch-clamp recording, we evaluated influence of MPTP-reduced dopaminergic afferent in basolateral amygdala (BLA), a main region for affective regulation, on excitatory–inhibitory circuits and synaptic plasticity. Field excitatory post-synaptic potential (fEPSP) slopes at external capsule-BLA synapses were increased in MPTP-mice with decreases in paired-pulse facilitation and long-term potentiation amplitude, which were corrected by bath-application of D2R agonist quinpirole or cannabinoid type 1 receptors agonist WIN55,212-2, but not D1R agonist SKF38393. Compared to single waveform fEPSP in control mice, a multi-spike waveform fEPSP was observed in MPTP-mice with prolongation of duration and an increase in paired-pulse inhibition, which were recovered by BLA-injection of quinpirole for 2 days rather than bath-application. Density of GABA-evoked current (IGABA) in BLA principal neurons and GABAAR-α2 subunit expression were reduced in MPTP-mice, which were recovered by administration of quinpirole. Decline of PKC phosphorylation in BLA of MPTP-mice was corrected by bath-application of quinpirole, but not SKF38393. In MPTP-mice, BLA-injection of quinpirole or PKC activator PMA could recover GABAAR expression, which was sensitive to PKC inhibitor GF109203X. The impairment of long-term depression (LTD) in MPTP-mice was rescued by bath-application of GABAAR agonist muscimol or BLA-injection of quinpirole and PMA. Finally, BLA-injection of muscimol, quinpirole or PMA relieved anxiety-like behaviors in MPTP-mice. The results indicate that the MPTP-induced dopamine depletion in BLA principal neurons through reducing D2R-mediated PKC phosphorylation suppresses GABAAR expression and activity, which impairs GABAAR-mediated inhibition and LTD induction leading to anxiety-like behaviors.

Strength of ~20-Hz Rebound and Motor Recovery After Stroke

Background. Stroke is a major cause of disability worldwide, and effective rehabilitation is crucial to regain skills for independent living. Recently, novel therapeutic approaches manipulating the excitatory-inhibitory balance of the motor cortex have been introduced to boost recovery after stroke. However, stroke-induced neurophysiological changes of the motor cortex may vary despite of similar clinical symptoms. Therefore, better understanding of excitability changes after stroke is essential when developing and targeting novel therapeutic approaches. Objective and Methods. We identified recovery-related alterations in motor cortex excitability after stroke using magnetoencephalography. Dynamics (suppression and rebound) of the ~20-Hz motor cortex rhythm were monitored during passive movement of the index finger in 23 stroke patients with upper limb paresis at acute phase, 1 month, and 1 year after stroke. Results. After stroke, the strength of the ~20-Hz rebound to stimulation of both impaired and healthy hand was decreased with respect to the controls in the affected (AH) and unaffected (UH) hemispheres, and increased during recovery. Importantly, the rebound strength was lower than that of the controls in the AH and UH also to healthy-hand stimulation despite of intact afferent input. In the AH, the rebound strength to impaired-hand stimulation correlated with hand motor recovery. Conclusions. Motor cortex excitability is increased bilaterally after stroke and decreases concomitantly with recovery. Motor cortex excitability changes are related to both alterations in local excitatory-inhibitory circuits and changes in afferent input. Fluent sensorimotor integration, which is closely coupled with excitability changes, seems to be a key factor for motor recovery.

The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics.

Probabilistic inference offers a principled framework for understanding both behaviour and cortical computation. However, two basic and ubiquitous properties of cortical responses seem difficult to reconcile with probabilistic inference: neural activity displays prominent oscillations in response to constant input, and large transient changes in response to stimulus onset. Indeed, cortical models of probabilistic inference have typically either concentrated on tuning curve or receptive field properties and remained agnostic as to the underlying circuit dynamics, or had simplistic dynamics that gave neither oscillations nor transients. Here we show that these dynamical behaviours may in fact be understood as hallmarks of the specific representation and algorithm that the cortex employs to perform probabilistic inference. We demonstrate that a particular family of probabilistic inference algorithms, Hamiltonian Monte Carlo (HMC), naturally maps onto the dynamics of excitatory-inhibitory neural networks. Specifically, we constructed a model of an excitatory-inhibitory circuit in primary visual cortex that performed HMC inference, and thus inherently gave rise to oscillations and transients. These oscillations were not mere epiphenomena but served an important functional role: speeding up inference by rapidly spanning a large volume of state space. Inference thus became an order of magnitude more efficient than in a non-oscillatory variant of the model. In addition, the network matched two specific properties of observed neural dynamics that would otherwise be difficult to account for using probabilistic inference. First, the frequency of oscillations as well as the magnitude of transients increased with the contrast of the image stimulus. Second, excitation and inhibition were balanced, and inhibition lagged excitation. These results suggest a new functional role for the separation of cortical populations into excitatory and inhibitory neurons, and for the neural oscillations that emerge in such excitatory-inhibitory networks: enhancing the efficiency of cortical computations.

Genetically Dissecting Cortical Neurons Involved in Epilepsy in Angelman Syndrome

Epilepsy in Angelman Syndrome is thought to originate from an imbalance between local excitatory-inhibitory circuits that results in a generalized hyperexcitability. In this issue of Neuron, Judson etal. (2016) demonstrate that selective maternal deletion of Ube3a in cortical GABAergic neurons causes circuit hyperexcitability, increased seizure severity, and EEG abnormalities.

New concepts on visual cortical plasticity: Multiple critical periods and implications for amblyopia

New concepts on visual cortical plasticity: Multiple critical periods and implications for amblyopia

Searching for Memories, Sudoku, Implicit Check Bits, and the Iterative Use of Not-Always-Correct Rapid Neural Computation

The algorithms that simple feedback neural circuits representing a brain area can rapidly carry out are often adequate to solve easy problems but for more difficult problems can return incorrect answers. A new excitatory-inhibitory circuit model of associative memory displays the common human problem of failing to rapidly find a memory when only a small clue is present. The memory model and a related computational network for solving Sudoku puzzles produce answers that contain implicit check bits in the representation of information across neurons, allowing a rapid evaluation of whether the putative answer is correct or incorrect through a computation related to visual pop-out. This fact may account for our strong psychological feeling of right or wrong when we retrieve a nominal memory from a minimal clue. This information allows more difficult computations or memory retrievals to be done in a serial fashion by using the fast but limited capabilities of a computational module multiple times. The mathematics of the excitatory-inhibitory circuits for associative memory and for Sudoku, both of which are understood in terms of energy or Lyapunov functions, is described in detail.

Searching for Memories, Sudoku, Implicit Check Bits, and the Iterative Use of Not-Always-Correct Rapid Neural Computation

The algorithms that simple feedback neural circuits representing a brain area can rapidly carry out are often adequate to solve only easy problems, and for more difficult problems can return incorrect answers. A new excitatory-inhibitory circuit model of associative memory displays the common human problem of failing to rapidly find a memory when only a small clue is present. The memory model and a related computational network for solving Sudoku puzzles produce answers that contain implicit check-bits in the representation of information across neurons, allowing a rapid evaluation of whether the putative answer is correct or incorrect through a computation related to visual 'pop-out'. This fact may account for our strong psychological feeling of right or wrong when we retrieve a nominal memory from a minimal clue. This information allows more difficult computations or memory retrievals to be done in a serial fashion by using the fast but limited capabilities of a computational module multiple times. The mathematics of the excitatory-inhibitory circuits for associative memory and for Sudoku, both of which are understood in terms of 'energy' or Lyapunov functions, is described in detail.