Beyond excitation/inhibition imbalance in multidimensional models of neural circuit changes in brain disorders

High neural noise in autism: A hypothesis currently at the nexus of explanatory power

Autism is a neurodevelopmental difference associated with specific autistic experiences and characteristics. Early models such as Weak Central Coherence and Enhanced Perceptual Functioning have tried to capture complex autistic behaviours in a single framework, however, these models lacked a neurobiological explanation. Conversely, current neurobiological theories of autism at the cellular and network levels suggest excitation/inhibition imbalances lead to high neural noise (or, a 'noisy brain') but lack a thorough explanation of how autistic behaviours occur. Critically, around 15 years ago, it was proposed that high neural noise in autism produced a stochastic resonance (SR) effect, a phenomenon where optimal amounts of noise improve signal quality. High neural noise can thus capture both the enhanced (through SR) and reduced performance observed in autistic individuals during certain tasks. Here, we provide a review and perspective that positions the "high neural noise" hypothesis in autism as best placed to provide research direction and impetus. Emphasis is placed on evidence for SR in autism, as this promising prediction has not yet been reviewed in the literature. Using this updated approach towards autism, we can explain a spectrum of autistic experiences all through a neurobiological lens. This approach can further aid in developing specific support or services for autism.

The function of Mef2c toward the development of excitatory and inhibitory cortical neurons

Neurodevelopmental disorders (NDDs) are caused by abnormal brain development, leading to altered brain function and affecting cognition, learning, self-control, memory, and emotion. NDDs are often demarcated as discrete entities for diagnosis, but empirical evidence indicates that NDDs share a great deal of overlap, including genetics, core symptoms, and biomarkers. Many NDDs also share a primary sensitive period for disease, specifically the last trimester of pregnancy in humans, which corresponds to the neonatal period in mice. This period is notable for cortical circuit assembly, suggesting that deficits in the establishment of brain connectivity are likely a leading cause of brain dysfunction across different NDDs. Regulators of gene programs that underlie neurodevelopment represent a point of convergence for NDDs. Here, we review how the transcription factor MEF2C, a risk factor for various NDDs, impacts cortical development. Cortical activity requires a precise balance of various types of excitatory and inhibitory neuron types. We use MEF2C loss-of-function as a study case to illustrate how brain dysfunction and altered behavior may derive from the dysfunction of specific cortical circuits at specific developmental times.

C. elegans: a prominent platform for modeling and drug screening in neurological disorders

INTRODUCTION

Human neurodevelopmental and neurodegenerative diseases (NDevDs and NDegDs, respectively) encompass a broad spectrum of disorders affecting the nervous system with an increasing incidence. In this context, the nematode C. elegans, has emerged as a benchmark model for biological research, especially in the field of neuroscience.

AREAS COVERED

The authors highlight the numerous advantages of this tiny worm as a model for exploring nervous system pathologies and as a platform for drug discovery. There is a particular focus given to describing the existing models of C. elegans for the study of NDevDs and NDegDs. Specifically, the authors underscore their strong applicability in preclinical drug development. Furthermore, they place particular emphasis on detailing the common techniques employed to explore the nervous system in both healthy and diseased states.

EXPERT OPINION

Drug discovery constitutes a long and expensive process. The incorporation of invertebrate models, such as C. elegans, stands as an exemplary strategy for mitigating costs and expediting timelines. The utilization of C. elegans as a platform to replicate nervous system pathologies and conduct high-throughput automated assays in the initial phases of drug discovery is pivotal for rendering therapeutic options more attainable and cost-effective.

Impaired synaptic incorporation of AMPA receptors in a mouse model of fragile X syndrome

Fragile X syndrome (FXS) is the most common monogenetic cause of inherited intellectual disability and autism in humans. One of the well-characterized molecular phenotypes of Fmr1 KO mice, a model of FXS, is increased translation of synaptic proteins. Although this upregulation stabilizes in adulthood, abnormalities during the critical period of plasticity have long-term effects on circuit formation and synaptic properties. Using high-resolution quantitative proteomics of synaptoneurosomes isolated from the adult, developed brains of Fmr1 KO mice, we show a differential abundance of proteins regulating the postsynaptic receptor activity of glutamatergic synapses. We investigated the AMPA receptor composition and shuttling in adult Fmr1 KO and WT mice using a variety of complementary experimental strategies such as surface protein crosslinking, immunostaining of surface receptors, and electrophysiology. We discovered that the activity-dependent synaptic delivery of AMPARs is impaired in adult Fmr1 KO mice. Furthermore, we show that Fmr1 KO synaptic AMPARs contain more GluA2 subunits that can be interpreted as a switch in the synaptic AMPAR subtype toward an increased number of Ca2+-impermeable receptors in adult Fmr1 KO synapses.

Local and long-range GABAergic circuits in hippocampal area CA1 and their link to Alzheimer's disease

GABAergic inhibitory neurons are the principal source of inhibition in the brain. Traditionally, their role in maintaining the balance of excitation-inhibition has been emphasized. Beyond homeostatic functions, recent circuit mapping and functional manipulation studies have revealed a wide range of specific roles that GABAergic circuits play in dynamically tilting excitation-inhibition coupling across spatio-temporal scales. These span from gating of compartment- and input-specific signaling, gain modulation, shaping input-output functions and synaptic plasticity, to generating signal-to-noise contrast, defining temporal windows for integration and rate codes, as well as organizing neural assemblies, and coordinating inter-regional synchrony. GABAergic circuits are thus instrumental in controlling single-neuron computations and behaviorally-linked network activity. The activity dependent modulation of sensory and mnemonic information processing by GABAergic circuits is pivotal for the formation and maintenance of episodic memories in the hippocampus. Here, we present an overview of the local and long-range GABAergic circuits that modulate the dynamics of excitation-inhibition and disinhibition in the main output area of the hippocampus CA1, which is crucial for episodic memory. Specifically, we link recent findings pertaining to GABAergic neuron molecular markers, electrophysiological properties, and synaptic wiring with their function at the circuit level. Lastly, given that area CA1 is particularly impaired during early stages of Alzheimer's disease, we emphasize how these GABAergic circuits may contribute to and be involved in the pathophysiology.

Circuit-level theories for sensory dysfunction in autism: convergence across mouse models

Individuals with autism spectrum disorder (ASD) exhibit a diverse range of behavioral features and genetic backgrounds, but whether different genetic forms of autism involve convergent pathophysiology of brain function is unknown. Here, we analyze evidence for convergent deficits in neural circuit function across multiple transgenic mouse models of ASD. We focus on sensory areas of neocortex, where circuit differences may underlie atypical sensory processing, a central feature of autism. Many distinct circuit-level theories for ASD have been proposed, including increased excitation-inhibition (E-I) ratio and hyperexcitability, hypofunction of parvalbumin (PV) interneuron circuits, impaired homeostatic plasticity, degraded sensory coding, and others. We review these theories and assess the degree of convergence across ASD mouse models for each. Behaviorally, our analysis reveals that innate sensory detection behavior is heightened and sensory discrimination behavior is impaired across many ASD models. Neurophysiologically, PV hypofunction and increased E-I ratio are prevalent but only rarely generate hyperexcitability and excess spiking. Instead, sensory tuning and other aspects of neural coding are commonly degraded and may explain impaired discrimination behavior. Two distinct phenotypic clusters with opposing neural circuit signatures are evident across mouse models. Such clustering could suggest physiological subtypes of autism, which may facilitate the development of tailored therapeutic approaches.

LPS induces microglial activation and GABAergic synaptic deficits in the hippocampus accompanied by prolonged cognitive impairment

Neuroinflammation impacts the brain and cognitive behavior through microglial activation. In this study, we determined the temporal sequence from microglial activation to synaptic dysfunction and cognitive behavior induced by neuroinflammation in mice. We found that LPS injection activated microglia within a short period, followed by impairments in GABAergic synapses, and that these events led to long-term cognitive impairment. We demonstrated that, 3 days after LPS injection, microglia in the hippocampus were significantly activated due to the LPS-induced inflammation in association with alterations in cellular morphology, microglial density, and expression of phagocytic markers. GABAergic synaptic impairments were detected at 4-6 days after LPS treatment, a time when microglia activity had returned to normal. Consequently, memory impairment persisted for 6 days after injection of LPS. Our results suggest that neuroinflammation induces microglia activation, GABAergic synaptic deficits and prolonged memory impairment over a defined temporal sequence. Our observations provide insight into the temporal sequence of neuroinflammation-associated brain pathologies. Moreover, the specific loss of inhibitory synapses accompanying the impaired inhibitory synaptic transmission provides mechanistic insight that may explain the prolonged cognitive deficit observed in patients with neuroinflammation. Thus, this study provides essential clues regarding early intervention strategies against brain pathologies accompanying neuroinflammation.

A working taxonomy for describing the sensory differences of autism

BACKGROUND

Individuals on the autism spectrum have been long described to process sensory information differently than neurotypical individuals. While much effort has been leveraged towards characterizing and investigating the neurobiology underlying the sensory differences of autism, there has been a notable lack of consistency in the terms being used to describe the nature of those differences.

MAIN BODY

We argue that inconsistent and interchangeable terminology-use when describing the sensory differences of autism has become problematic beyond mere pedantry and inconvenience. We begin by highlighting popular terms that are currently being used to describe the sensory differences of autism (e.g. "sensitivity", "reactivity" and "responsivity") and discuss why poor nomenclature may hamper efforts towards understanding the aetiology of sensory differences in autism. We then provide a solution to poor terminology-use by proposing a hierarchical taxonomy for describing and referring to various sensory features.

CONCLUSION

Inconsistent terminology-use when describing the sensory features of autism has stifled discussion and scientific understanding of the sensory differences of autism. The hierarchical taxonomy proposed was developed to help resolve lack of clarity when discussing the sensory differences of autism and to place future research targets at appropriate levels of analysis.

Psychosis spectrum illnesses as disorders of prefrontal critical period plasticity

Emerging research on neuroplasticity processes in psychosis spectrum illnesses-from the synaptic to the macrocircuit levels-fill key gaps in our models of pathophysiology and open up important treatment considerations. In this selective narrative review, we focus on three themes, emphasizing alterations in spike-timing dependent and Hebbian plasticity that occur during adolescence, the critical period for prefrontal system development: (1) Experience-dependent dysplasticity in psychosis emerges from activity decorrelation within neuronal ensembles. (2) Plasticity processes operate bidirectionally: deleterious environmental and experiential inputs shape microcircuits. (3) Dysregulated plasticity processes interact across levels of scale and time and include compensatory mechanisms that have pathogenic importance. We present evidence that-given the centrality of progressive dysplastic changes, especially in prefrontal cortex-pharmacologic or neuromodulatory interventions will need to be supplemented by corrective learning experiences for the brain if we are to help people living with these illnesses to fully thrive.

Chronic sodium bromide treatment relieves autistic-like behavioral deficits in three mouse models of autism

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders whose diagnosis relies on deficient social interaction and communication together with repetitive behavior. To date, no pharmacological treatment has been approved that ameliorates social behavior in patients with ASD. Based on the excitation/inhibition imbalance theory of autism, we hypothesized that bromide ions, long used as an antiepileptic medication, could relieve core symptoms of ASD. We evaluated the effects of chronic sodium bromide (NaBr) administration on autistic-like symptoms in three genetic mouse models of autism: Oprm1^-/-, Fmr1^-/- and Shank3^Δex13-16-/- mice. We showed that chronic NaBr treatment relieved autistic-like behaviors in these three models. In Oprm1^-/- mice, these beneficial effects were superior to those of chronic bumetanide administration. At transcriptional level, chronic NaBr in Oprm1 null mice was associated with increased expression of genes coding for chloride ions transporters, GABA_A receptor subunits, oxytocin and mGlu4 receptor. Lastly, we uncovered synergistic alleviating effects of chronic NaBr and a positive allosteric modulator (PAM) of mGlu4 receptor on autistic-like behavior in Oprm1^-/- mice. We evidenced in heterologous cells that bromide ions behave as PAMs of mGlu4, providing a molecular mechanism for such synergy. Our data reveal the therapeutic potential of bromide ions, alone or in combination with a PAM of mGlu4 receptor, for the treatment of ASDs.

Mechanisms Driving the Emergence of Neuronal Hyperexcitability in Fragile X Syndrome

Hyperexcitability is a shared neurophysiological phenotype across various genetic neurodevelopmental disorders, including Fragile X syndrome (FXS). Several patient symptoms are associated with hyperexcitability, but a puzzling feature is that their onset is often delayed until their second and third year of life. It remains unclear how and why hyperexcitability emerges in neurodevelopmental disorders. FXS is caused by the loss of FMRP, an RNA-binding protein which has many critical roles including protein synthesis-dependent and independent regulation of ion channels and receptors, as well as global regulation of protein synthesis. Here, we discussed recent literature uncovering novel mechanisms that may drive the progressive onset of hyperexcitability in the FXS brain. We discussed in detail how recent publications have highlighted defects in homeostatic plasticity, providing new insight on the FXS brain and suggest pharmacotherapeutic strategies in FXS and other neurodevelopmental disorders.

Can biophysical models of dendritic spines be used to explore synaptic changes associated with addiction?

Effective treatments that prevent or reduce drug relapse vulnerability should be developed to relieve the high burden of drug addiction on society. This will only be possible by enhancing the understanding of the molecular mechanisms underlying the neurobiology of addiction. Recent experimental data have shown that dendritic spines, small protrusions from the dendrites that receive excitatory input, of spiny neurons in the nucleus accumbens exhibit morphological changes during drug exposure and withdrawal. Moreover, these changes relate to the characteristic drug-seeking behavior of addiction. However, due to the complexity of the dendritic spines, we do not yet fully understand the processes underlying their structural changes in response to different inputs. We propose that biophysical models can enhance the current understanding of these processes by incorporating different, and sometimes, discrepant experimental data to identify the shared underlying mechanisms and generate experimentally testable hypotheses. This review aims to give an up-to-date report on biophysical models of dendritic spines, focusing on those models that describe their shape changes, which are well-known to relate to learning and memory. Moreover, it examines how these models can enhance our understanding of the effect of the drugs and the synaptic changes during withdrawal, as well as during neurodegenerative disease progression such as Alzheimer's disease.

Hyperexcitability and Homeostasis in Fragile X Syndrome

Fragile X Syndrome (FXS) is a leading inherited cause of autism and intellectual disability, resulting from a mutation in the FMR1 gene and subsequent loss of its protein product FMRP. Despite this simple genetic origin, FXS is a phenotypically complex disorder with a range of physical and neurocognitive disruptions. While numerous molecular and cellular pathways are affected by FMRP loss, there is growing evidence that circuit hyperexcitability may be a common convergence point that can account for many of the wide-ranging phenotypes seen in FXS. The mechanisms for hyperexcitability in FXS include alterations to excitatory synaptic function and connectivity, reduced inhibitory neuron activity, as well as changes to ion channel expression and conductance. However, understanding the impact of FMR1 mutation on circuit function is complicated by the inherent plasticity in neural circuits, which display an array of homeostatic mechanisms to maintain activity near set levels. FMRP is also an important regulator of activity-dependent plasticity in the brain, meaning that dysregulated plasticity can be both a cause and consequence of hyperexcitable networks in FXS. This makes it difficult to separate the direct effects of FMR1 mutation from the myriad and pleiotropic compensatory changes associated with it, both of which are likely to contribute to FXS pathophysiology. Here we will: (1) review evidence for hyperexcitability and homeostatic plasticity phenotypes in FXS models, focusing on similarities/differences across brain regions, cell-types, and developmental time points; (2) examine how excitability and plasticity disruptions interact with each other to ultimately contribute to circuit dysfunction in FXS; and (3) discuss how these synaptic and circuit deficits contribute to disease-relevant behavioral phenotypes like epilepsy and sensory hypersensitivity. Through this discussion of where the current field stands, we aim to introduce perspectives moving forward in FXS research.

Cortical interneurons in autism

The mechanistic underpinnings of autism remain a subject of debate and controversy. Why do individuals with autism share an overlapping set of atypical behaviors and symptoms, despite having different genetic and environmental risk factors? A major challenge in developing new therapies for autism has been the inability to identify convergent neural phenotypes that could explain the common set of symptoms that result in the diagnosis. Although no striking macroscopic neuropathological changes have been identified in autism, there is growing evidence that inhibitory interneurons (INs) play an important role in its neural basis. In this Review, we evaluate and interpret this evidence, focusing on recent findings showing reduced density and activity of the parvalbumin class of INs. We discuss the need for additional studies that investigate how genes and the environment interact to change the developmental trajectory of INs, permanently altering their numbers, connectivity and circuit engagement.

Prenatal interleukin 6 elevation increases glutamatergic synapse density and disrupts hippocampal connectivity in offspring

Early prenatal inflammatory conditions are thought to be a risk factor for different neurodevelopmental disorders. Maternal interleukin-6 (IL-6) elevation during pregnancy causes abnormal behavior in offspring, but whether these defects result from altered synaptic developmental trajectories remains unclear. Here we showed that transient IL-6 elevation via injection into pregnant mice or developing embryos enhanced glutamatergic synapses and led to overall brain hyperconnectivity in offspring into adulthood. IL-6 activated synaptogenesis gene programs in glutamatergic neurons and required the transcription factor STAT3 and expression of the RGS4 gene. The STAT3-RGS4 pathway was also activated in neonatal brains during poly(I:C)-induced maternal immune activation, which mimics viral infection during pregnancy. These findings indicate that IL-6 elevation at early developmental stages is sufficient to exert a long-lasting effect on glutamatergic synaptogenesis and brain connectivity, providing a mechanistic framework for the association between prenatal inflammatory events and brain neurodevelopmental disorders.

A Selective Review of the Excitatory-Inhibitory Imbalance in Schizophrenia: Underlying Biology, Genetics, Microcircuits, and Symptoms

Schizophrenia is a chronic disorder characterized by specific positive and negative primary symptoms, social behavior disturbances and cognitive deficits (e.g., impairment in working memory and cognitive flexibility). Mounting evidence suggests that altered excitability and inhibition at the molecular, cellular, circuit and network level might be the basis for the pathophysiology of neurodevelopmental and neuropsychiatric disorders such as schizophrenia. In the past decades, human and animal studies have identified that glutamate and gamma-aminobutyric acid (GABA) neurotransmissions are critically involved in several cognitive progresses, including learning and memory. The purpose of this review is, by analyzing emerging findings relating to the balance of excitatory and inhibitory, ranging from animal models of schizophrenia to clinical studies in patients with early onset, first-episode or chronic schizophrenia, to discuss how the excitatory-inhibitory imbalance may relate to the pathophysiology of disease phenotypes such as cognitive deficits and negative symptoms, and highlight directions for appropriate therapeutic strategies.

Neural circuit function redundancy in brain disorders

Redundancy is a ubiquitous property of the nervous system. This means that vastly different configurations of cellular and synaptic components can enable the same neural circuit functions. However, until recently, very little brain disorder research has considered the implications of this characteristic when designing experiments or interpreting data. Here, we first summarise the evidence for redundancy in healthy brains, explaining redundancy and three related sub-concepts: sloppiness, dependencies and multiple solutions. We then lay out key implications for brain disorder research, covering recent examples of redundancy effects in experimental studies on psychiatric disorders. Finally, we give predictions for future experiments based on these concepts.

How does hemispheric specialization contribute to human-defining cognition?

Uniquely human cognitive faculties arise from flexible interplay between specific local neural modules, with hemispheric asymmetries in functional specialization. Here, we discuss how these computational design principles provide a scaffold that enables some of the most advanced cognitive operations, such as semantic understanding of world structure, logical reasoning, and communication via language. We draw parallels to dual-processing theories of cognition by placing a focus on Kahneman's System 1 and System 2. We propose integration of these ideas with the global workspace theory to explain dynamic relay of information products between both systems. Deepening the current understanding of how neurocognitive asymmetry makes humans special can ignite the next wave of neuroscience-inspired artificial intelligence.

Spatial organization and transitions of spontaneous neuronal activities in the developing sensory cortex

The sensory cortex underlies our ability to perceive and interact with the external world. Sensory perceptions are controlled by specialized neuronal circuits established through fine-tuning, which relies largely on neuronal activity during the development. Spontaneous neuronal activity is an essential driving force of neuronal circuit refinement. At early developmental stages, sensory cortices display spontaneous activities originating from the periphery and characterized by correlated firing arranged spatially according to the modality. The firing patterns are reorganized over time and become sparse, which is typical for the mature brain. This review focuses mainly on rodent sensory cortices. First, the features of the spontaneous activities during early postnatal stages are described. Then, the developmental changes in the spatial organization of the spontaneous activities and the transition mechanisms involved are discussed. The identification of the principles controlling the spatial organization of spontaneous activities in the developing sensory cortex is essential to understand the self-organization process of neuronal circuits.

The Emergence of Network Activity Patterns in the Somatosensory Cortex - An Early Window to Autism Spectrum Disorders

Across mammalian species, patterned activity in neural populations is a prominent feature of developing sensory cortices. Numerous studies have long appreciated the diversity of these patterns, characterizing their differences in spatial and temporal dynamics. In the murine somatosensory cortex, neuronal co-activation is thought to guide the formation of sensory maps and prepare the cortex for sensory processing after birth. While pioneering studies deftly utilized slice electrophysiology and unit recordings to characterize correlated activity, a detailed understanding of the underlying circuits remains poorly understood. More recently, advances in in vivo calcium imaging in awake mouse pups and increasing genetic tractability of neuronal types have allowed unprecedented manipulation of circuit components at select developmental timepoints. These novel approaches have proven fundamental in uncovering the identity of neurons engaged in correlated activity during development. In particular, recent studies have highlighted interneurons as key in refining the spatial extent and temporal progression of patterned activity. Here, we discuss how emergent synchronous activity across the first postnatal weeks is shaped by underlying gamma aminobutyric acid (GABA)ergic contributors in the somatosensory cortex. Further, the importance of participation in specific activity patterns per se for neuronal maturation and perdurance will be of particular highlight in this survey of recent literature. Finally, we underscore how aberrant neuronal synchrony and disrupted inhibitory interneuron activity underlie sensory perturbations in neurodevelopmental disorders, particularly Autism Spectrum Disorders (ASDs), emphasizing the importance of future investigative approaches that incorporate the spatiotemporal features of patterned activity alongside the cellular components to probe disordered circuit assembly.

Inhibitory neurons exhibit high controlling ability in the cortical microconnectome

The brain is a network system in which excitatory and inhibitory neurons keep activity balanced in the highly non-random connectivity pattern of the microconnectome. It is well known that the relative percentage of inhibitory neurons is much smaller than excitatory neurons in the cortex. So, in general, how inhibitory neurons can keep the balance with the surrounding excitatory neurons is an important question. There is much accumulated knowledge about this fundamental question. This study quantitatively evaluated the relatively higher functional contribution of inhibitory neurons in terms of not only properties of individual neurons, such as firing rate, but also in terms of topological mechanisms and controlling ability on other excitatory neurons. We combined simultaneous electrical recording (~2.5 hours) of ~1000 neurons in vitro, and quantitative evaluation of neuronal interactions including excitatory-inhibitory categorization. This study accurately defined recording brain anatomical targets, such as brain regions and cortical layers, by inter-referring MRI and immunostaining recordings. The interaction networks enabled us to quantify topological influence of individual neurons, in terms of controlling ability to other neurons. Especially, the result indicated that highly influential inhibitory neurons show higher controlling ability of other neurons than excitatory neurons, and are relatively often distributed in deeper layers of the cortex. Furthermore, the neurons having high controlling ability are more effectively limited in number than central nodes of k-cores, and these neurons also participate in more clustered motifs. In summary, this study suggested that the high controlling ability of inhibitory neurons is a key mechanism to keep balance with a large number of other excitatory neurons beyond simple higher firing rate. Application of the selection method of limited important neurons would be also applicable for the ability to effectively and selectively stimulate E/I imbalanced disease states.

How small numbers of inhibitory neurons functionally keep balance with large numbers of excitatory neurons in the brain by controlling each other is a fundamental question. Especially, this study quantitatively evaluated a topological mechanism of interaction networks in terms of controlling abilities of individual cortical neurons to other neurons. Combination of simultaneous electrical recording of ~1000 neurons and a quantitative evaluation method of neuronal interactions including excitatory-inhibitory categories, enabled us to evaluate the influence of individual neurons not only about firing rate but also about their relative positions in the networks and controllable ability of other neurons. Especially, the result showed that inhibitory neurons have more controlling ability than excitatory neurons, and such neurons were more often observed in deep layers. Because the limited number of neurons in terms controlling ability were much smaller than neurons based on centrality measure and, of course, more directly selected neurons based on their ability to control other neurons, the selection method of important neurons will help not only to produce realistic computational models but also will help to stimulate brain to effectively treat imbalanced disease states.

Optimal Organization of Functional Connectivity Networks for Segregation and Integration With Large-Scale Critical Dynamics in Human Brains

The optimal organization for functional segregation and integration in brain is made evident by the “small-world” feature of functional connectivity (FC) networks and is further supported by the loss of this feature that has been described in many types of brain disease. However, it remains unknown how such optimally organized FC networks arise from the brain's structural constrains. On the other hand, an emerging literature suggests that brain function may be supported by critical neural dynamics, which is believed to facilitate information processing in brain. Though previous investigations have shown that the critical dynamics plays an important role in understanding the relation between whole brain structural connectivity and functional connectivity, it is not clear if the critical dynamics could be responsible for the optimal FC network configuration in human brains. Here, we show that the long-range temporal correlations (LRTCs) in the resting state fMRI blood-oxygen-level-dependent (BOLD) signals are significantly correlated with the topological matrices of the FC brain network. Using structure-dynamics-function modeling approach that incorporates diffusion tensor imaging (DTI) data and simple cellular automata dynamics, we showed that the critical dynamics could optimize the whole brain FC network organization by, e.g., maximizing the clustering coefficient while minimizing the characteristic path length. We also demonstrated with a more detailed excitation-inhibition neuronal network model that loss of local excitation-inhibition (E/I) balance causes failure of critical dynamics, therefore disrupting the optimal FC network organization. The results highlighted the crucial role of the critical dynamics in forming an optimal organization of FC networks in the brain and have potential application to the understanding and modeling of abnormal FC configurations in neuropsychiatric disorders.

A proof of concept 'phase zero' study of neurodevelopment using brain organoid models with Vis/near-infrared spectroscopy and electrophysiology

Homeostatic control of neuronal excitability by modulation of synaptic inhibition (I) and excitation (E) of the principal neurons is important during brain maturation. The fundamental features of in-utero brain development, including local synaptic E-I ratio and bioenergetics, can be modeled by cerebral organoids (CO) that have exhibited highly regular nested oscillatory network events. Therefore, we evaluated a 'Phase Zero' clinical study platform combining broadband Vis/near-infrared(NIR) spectroscopy and electrophysiology with studying E-I ratio based on the spectral exponent of local field potentials and bioenergetics based on the activity of mitochondrial Cytochrome-C Oxidase (CCO). We found a significant effect of the age of the healthy controls iPSC CO from 23 days to 3 months on the CCO activity (chi-square (2, N = 10) = 20, p = 4.5400e-05), and spectral exponent between 30-50 Hz (chi-square (2, N = 16) = 13.88, p = 0.001). Also, a significant effect of drugs, choline (CHO), idebenone (IDB), R-alpha-lipoic acid plus acetyl-L-carnitine (LCLA), was found on the CCO activity (chi-square (3, N = 10) = 25.44, p = 1.2492e-05), spectral exponent between 1 and 20 Hz (chi-square (3, N = 16) = 43.5, p = 1.9273e-09) and 30-50 Hz (chi-square (3, N = 16) = 23.47, p = 3.2148e-05) in 34 days old CO from schizophrenia (SCZ) patients iPSC. We present the feasibility of a multimodal approach, combining electrophysiology and broadband Vis-NIR spectroscopy, to monitor neurodevelopment in brain organoid models that can complement traditional drug design approaches to test clinically meaningful hypotheses.

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.

Functional characterization of rare NRXN1 variants identified in autism spectrum disorders and schizophrenia

Background

Rare genetic variants contribute to the etiology of both autism spectrum disorder (ASD) and schizophrenia (SCZ). Most genetic studies limit their focus to likely gene-disrupting mutations because they are relatively easier to interpret their effects on the gene product. Interpretation of missense variants is also informative to some pathophysiological mechanisms of these neurodevelopmental disorders; however, their contribution has not been elucidated because of relatively small effects. Therefore, we characterized missense variants detected in NRXN1, a well-known neurodevelopmental disease-causing gene, from individuals with ASD and SCZ.

Methods

To discover rare variants with large effect size and to evaluate their role in the shared etiopathophysiology of ASD and SCZ, we sequenced NRXN1 coding exons with a sample comprising 562 Japanese ASD and SCZ patients, followed by a genetic association analysis in 4273 unrelated individuals. Impact of each missense variant detected here on cell surface expression, interaction with NLGN1, and synaptogenic activity was analyzed using an in vitro functional assay and in silico three-dimensional (3D) structural modeling.

Results

Through mutation screening, we regarded three ultra-rare missense variants (T737M, D772G, and R856W), all of which affected the LNS4 domain of NRXN1α isoform, as disease-associated variants. Diagnosis of individuals with T737M, D772G, and R856W was 1ASD and 1SCZ, 1ASD, and 1SCZ, respectively. We observed the following phenotypic and functional burden caused by each variant. (i) D772G and R856W carriers had more serious social disabilities than T737M carriers. (ii) In vitro assay showed reduced cell surface expression of NRXN1α by D772G and R856W mutations. In vitro functional analysis showed decreased NRXN1α-NLGN1 interaction of T737M and D772G mutants. (iii) In silico 3D structural modeling indicated that T737M and D772G mutations could destabilize the rod-shaped structure of LNS2-LNS5 domains, and D772G and R856W could disturb N-glycan conformations for the transport signal.

Conclusions

The combined data suggest that missense variants in NRXN1 could be associated with phenotypes of neurodevelopmental disorders beyond the diagnosis of ASD and/or SCZ.

Response Dissociation in Hierarchical Cortical Circuits: a Unique Feature of Autism Spectrum Disorder

A prominent hypothesis regarding the pathophysiology of autism is that an increase in the balance between neural excitation and inhibition results in an increase in neural responses. However, previous reports of population-level response magnitude in individuals with autism have been inconsistent. Critically, network interactions have not been considered in previous neuroimaging studies of excitation and inhibition imbalance in autism. In particular, a defining characteristic of cortical organization is its hierarchical and interactive structure; sensory and cognitive systems are comprised of networks where later stages inherit and build upon the processing of earlier input stages, and also influence and shape earlier stages by top-down modulation. Here we used the well established connections of the human visual system to examine response magnitudes in a higher-order motion processing region [middle temporal area (MT+)] and its primary input region (V1). Simple visual stimuli were presented to adult individuals with autism spectrum disorders (ASD; n = 24, mean age 23 years, 8 females) and neurotypical controls (n = 24, mean age 22, 8 females) during fMRI scanning. We discovered a strong dissociation of fMRI response magnitude between region MT+ and V1 in individuals with ASD: individuals with high MT+ responses had attenuated V1 responses. The magnitude of MT+ amplification and of V1 attenuation was associated with autism severity, appeared to result from amplified suppressive feedback from MT+ to V1, and was not present in neurotypical controls. Our results reveal the potential role of altered hierarchical network interactions in the pathophysiology of ASD.SIGNIFICANCE STATEMENT An imbalance between neural excitation and inhibition, resulting in increased neural responses, has been suggested as a pathophysiological pathway to autism, but direct evidence from humans is lacking. In the current study we consider the role of interactions between stages of sensory processing when testing increased neural responses in individuals with autism. We used the well known hierarchical structure of the visual motion pathway to demonstrate dissociation in the fMRI response magnitude between adjacent stages of processing in autism: responses are attenuated in a primary visual area but amplified in a subsequent higher-order area. This response dissociation appears to rely on enhanced suppressive feedback between regions and reveals a previously unknown cortical network alteration in autism.

Imaging epilepsy in larval zebrafish

Our understanding of the genetic aetiology of paediatric epilepsies has grown substantially over the last decade. However, in order to translate improved diagnostics to personalised treatments, there is an urgent need to link molecular pathophysiology in epilepsy to whole-brain dynamics in seizures. Zebrafish have emerged as a promising new animal model for epileptic seizure disorders, with particular relevance for genetic and developmental epilepsies. As a novel model organism for epilepsy research they combine key advantages: the small size of larval zebrafish allows high throughput in vivo experiments; the availability of advanced genetic tools allows targeted modification to model specific human genetic disorders (including genetic epilepsies) in a vertebrate system; and optical access to the entire central nervous system has provided the basis for advanced microscopy technologies to image structure and function in the intact larval zebrafish brain. There is a growing body of literature describing and characterising features of epileptic seizures and epilepsy in larval zebrafish. Recently genetically encoded calcium indicators have been used to investigate the neurobiological basis of these seizures with light microscopy. This approach offers a unique window into the multiscale dynamics of epileptic seizures, capturing both whole-brain dynamics and single-cell behaviour concurrently. At the same time, linking observations made using calcium imaging in the larval zebrafish brain back to an understanding of epileptic seizures largely derived from cortical electrophysiological recordings in human patients and mammalian animal models is non-trivial. In this review we briefly illustrate the state of the art of epilepsy research in zebrafish with particular focus on calcium imaging of epileptic seizures in the larval zebrafish. We illustrate the utility of a dynamic systems perspective on the epileptic brain for providing a principled approach to linking observations across species and identifying those features of brain dynamics that are most relevant to epilepsy. In the following section we survey the literature for imaging features associated with epilepsy and epileptic seizures and link these to observations made from humans and other more traditional animal models. We conclude by identifying the key challenges still facing epilepsy research in the larval zebrafish and indicate strategies for future research to address these and integrate more directly with the themes and questions that emerge from investigating epilepsy in other model systems and human patients.

Cellular and synaptic phenotypes lead to disrupted information processing in Fmr1-KO mouse layer 4 barrel cortex

Sensory hypersensitivity is a common and debilitating feature of neurodevelopmental disorders such as Fragile X Syndrome (FXS). How developmental changes in neuronal function culminate in network dysfunction that underlies sensory hypersensitivities is unknown. By systematically studying cellular and synaptic properties of layer 4 neurons combined with cellular and network simulations, we explored how the array of phenotypes in Fmr1-knockout (KO) mice produce circuit pathology during development. We show that many of the cellular and synaptic pathologies in Fmr1-KO mice are antagonistic, mitigating circuit dysfunction, and hence may be compensatory to the primary pathology. Overall, the layer 4 network in the Fmr1-KO exhibits significant alterations in spike output in response to thalamocortical input and distorted sensory encoding. This developmental loss of layer 4 sensory encoding precision would contribute to subsequent developmental alterations in layer 4-to-layer 2/3 connectivity and plasticity observed in Fmr1-KO mice, and circuit dysfunction underlying sensory hypersensitivity.

Parameter inference in dynamical systems with co-dimension 1 bifurcations

Dynamical systems with intricate behaviour are all-pervasive in biology. Many of the most interesting biological processes indicate the presence of bifurcations, i.e. phenomena where a small change in a system parameter causes qualitatively different behaviour. Bifurcation theory has become a rich field of research in its own right and evaluating the bifurcation behaviour of a given dynamical system can be challenging. An even greater challenge, however, is to learn the bifurcation structure of dynamical systems from data, where the precise model structure is not known. Here, we study one aspects of this problem: the practical implications that the presence of bifurcations has on our ability to infer model parameters and initial conditions from empirical data; we focus on the canonical co-dimension 1 bifurcations and provide a comprehensive analysis of how dynamics, and our ability to infer kinetic parameters are linked. The picture thus emerging is surprisingly nuanced and suggests that identification of the qualitative dynamics-the bifurcation diagram-should precede any attempt at inferring kinetic parameters.

Multifaceted Changes in Synaptic Composition and Astrocytic Involvement in a Mouse Model of Fragile X Syndrome

Fragile X Syndrome (FXS), a common inheritable form of intellectual disability, is known to alter neocortical circuits. However, its impact on the diverse synapse types comprising these circuits, or on the involvement of astrocytes, is not well known. We used immunofluorescent array tomography to quantify different synaptic populations and their association with astrocytes in layers 1 through 4 of the adult somatosensory cortex of a FXS mouse model, the FMR1 knockout mouse. The collected multi-channel data contained approximately 1.6 million synapses which were analyzed using a probabilistic synapse detector. Our study reveals complex, synapse-type and layer specific changes in the neocortical circuitry of FMR1 knockout mice. We report an increase of small glutamatergic VGluT1 synapses in layer 4 accompanied by a decrease in large VGluT1 synapses in layers 1 and 4. VGluT2 synapses show a rather consistent decrease in density in layers 1 and 2/3. In all layers, we observe the loss of large inhibitory synapses. Lastly, astrocytic association of excitatory synapses decreases. The ability to dissect the circuit deficits by synapse type and astrocytic involvement will be crucial for understanding how these changes affect circuit function, and ultimately defining targets for therapeutic intervention.

Estrus-Cycle Regulation of Cortical Inhibition

Female mammals experience cyclical changes in sexual receptivity known as the estrus cycle. Little is known about how estrus affects the cortex, although alterations in sensation, cognition and the cyclical occurrence of epilepsy suggest brain-wide processing changes. We performed in vivo juxtacellular and whole-cell recordings in somatosensory cortex of female rats and found that the estrus cycle potently altered cortical inhibition. Fast-spiking interneurons were strongly activated with social facial touch and varied their ongoing activity with the estrus cycle and estradiol in ovariectomized females, while regular-spiking excitatory neurons did not change. In situ hybridization for estrogen receptor β (Esr2) showed co-localization with parvalbumin-positive (PV⁺) interneurons in deep cortical layers, mirroring the laminar distribution of our physiological findings. The fraction of neurons positive for estrogen receptor β (Esr2) and PV co-localization (Esr2⁺PV⁺) in cortical layer V was increased in proestrus. In vivo and in vitro experiments confirmed that estrogen acts locally to increase fast-spiking interneuron excitability through an estrogen-receptor-β-dependent mechanism.

Transcriptomic immaturity inducible by neural hyperexcitation is shared by multiple neuropsychiatric disorders

Biomarkers are needed to improve the diagnosis of neuropsychiatric disorders, which are often associated to excitatory/inhibitory imbalances in neural transmission and abnormal maturation. Here, we characterized different disease conditions by mapping changes in the expression patterns of maturation-related genes whose expression was altered by experimental neural hyperexcitation in published studies. This analysis revealed two gene expression patterns: decreases in maturity markers and increases in immaturity markers. These two groups of genes were characterized by the over-representation of genes related to synaptic function and chromosomal modification, respectively. Using these two groups in a transdiagnostic analysis of 87 disease datasets for eight neuropsychiatric disorders and 12 datasets from corresponding animal models, we found that transcriptomic pseudoimmaturity inducible by neural hyperexcitation is shared by multiple neuropsychiatric disorders, such as schizophrenia, Alzheimer disorders, and amyotrophic lateral sclerosis. Our results indicate that this endophenotype serves as a basis for the transdiagnostic characterization of these disorders.

Tomoyuki Murano et al. showed that neural hyperexcitation increases the expression of immaturity related genes. These changes in gene expression are shared among different neuropsychiatric and neurological conditions, hinting at their potential role as biomarkers.

Increased Excitation-Inhibition Ratio Stabilizes Synapse and Circuit Excitability in Four Autism Mouse Models

Distinct genetic forms of autism are hypothesized to share a common increase in excitation-inhibition (E-I) ratio in cerebral cortex, causing hyperexcitability and excess spiking. We provide a systematic test of this hypothesis across 4 mouse models (Fmr1^-/y, Cntnap2^-/-, 16p11.2^del/+, Tsc2^+/-), focusing on somatosensory cortex. All autism mutants showed reduced feedforward inhibition in layer 2/3 coupled with more modest, variable reduction in feedforward excitation, driving a common increase in E-I conductance ratio. Despite this, feedforward spiking, synaptic depolarization, and spontaneous spiking were largely normal. Modeling revealed that E and I conductance changes in each mutant were quantitatively matched to yield stable, not increased, synaptic depolarization for cells near spike threshold. Correspondingly, whisker-evoked spiking was not increased in vivo despite detectably reduced inhibition. Thus, elevated E-I ratio is a common circuit phenotype but appears to reflect homeostatic stabilization of synaptic drive rather than driving network hyperexcitability in autism.

A Proposed Mechanism for Spontaneous Transitions between Interictal and Ictal Activity

Epileptic networks are characterized by two outputs: brief interictal spikes and rarer, more prolonged seizures. Although either output state is readily modeled in silico and induced experimentally, the transition mechanisms are unknown, in part because no models exhibit both output states spontaneously. In silico small-world neural networks were built using single-compartment neurons whose physiological parameters were derived from dual whole-cell recordings of pyramidal cells in organotypic hippocampal slice cultures that were generating spontaneous seizure-like activity. In silico, neurons were connected by abundant local synapses and rare long-distance synapses. Activity-dependent synaptic depression and gradual recovery delimited synchronous activity. Full synaptic recovery engendered interictal population spikes that spread via long-distance synapses. When synaptic recovery was incomplete, postsynaptic neurons required coincident activation of multiple presynaptic terminals to reach firing threshold. Only local connections were sufficiently dense to spread activity under these conditions. This coalesced network activity into traveling waves whose velocity varied with synaptic recovery. Seizures were comprised of sustained traveling waves that were similar to those recorded during experimental and human neocortical seizures. Sustained traveling waves occurred only when wave velocity, network dimensions, and the rate of synaptic recovery enabled wave reentry into previously depressed areas at precisely ictogenic levels of synaptic recovery. Wide-field, cellular-resolution GCamP7b calcium imaging demonstrated similar initial patterns of activation in the hippocampus, although the anatomical distribution of traveling waves of synaptic activation was altered by the pattern of synaptic connectivity in the organotypic hippocampal cultures.SIGNIFICANCE STATEMENT When computerized distributed neural network models are required to generate both features of epileptic networks (i.e., spontaneous interictal population spikes and seizures), the network structure is substantially constrained. These constraints provide important new hypotheses regarding the nature of epileptic networks and mechanisms of seizure onset.

Using computational models to predict in vivo synaptic inputs to interneuron specific 3 (IS3) cells of CA1 hippocampus that also allow their recruitment during rhythmic states

Brain coding strategies are enabled by the balance of synaptic inputs that individual neurons receive as determined by the networks in which they reside. Inhibitory cell types contribute to brain function in distinct ways but recording from specific, inhibitory cell types during behaviour to determine their contributions is highly challenging. In particular, the in vivo activities of vasoactive intestinal peptide-expressing interneuron specific 3 (IS3) cells in the hippocampus that only target other inhibitory cells are unknown at present. We perform a massive, computational exploration of possible synaptic inputs to IS3 cells using multi-compartment models and optimized synaptic parameters. We find that asynchronous, in vivo-like states that are sensitive to additional theta-timed inputs (8 Hz) exist when excitatory and inhibitory synaptic conductances are approximately equally balanced and with low numbers of activated synapses receiving correlated inputs. Specifically, under these balanced conditions, the input resistance is larger with higher mean spike firing rates relative to other activated synaptic conditions investigated. Incoming theta-timed inputs result in strongly increased spectral power relative to baseline. Thus, using a generally applicable computational approach we predict the existence and features of background, balanced states in hippocampal circuits.

The multidimensional brain

Brain activity takes place in three spatial-plus time dimensions. This rather obvious claim has been recently questioned by papers that, taking into account the big data outburst and novel available computational tools, are starting to unveil a more intricate state of affairs. Indeed, various brain activities and their correlated mental functions can be assessed in terms of trajectories embedded in phase spaces of dimensions higher than the canonical ones. In this review, I show how further dimensions may not just represent a convenient methodological tool that allows a better mathematical treatment of otherwise elusive cortical activities, but may also reflect genuine functional or anatomical relationships among real nervous functions. I then describe how to extract hidden multidimensional information from real or artificial neurodata series, and make clear how our mind dilutes, rather than concentrates as currently believed, inputs coming from the environment. Finally, I argue that the principle "the higher the dimension, the greater the information" may explain the occurrence of mental activities and elucidate the mechanisms of human diseases associated with dimensionality reduction.

Genetic mutations in Ca2+ signaling alter dendrite morphology and social approach in juvenile mice

Dendritic morphology is a critical determinant of neuronal connectivity, and calcium signaling plays a predominant role in shaping dendrites. Altered dendritic morphology and genetic mutations in calcium signaling are both associated with neurodevelopmental disorders (NDDs). In this study we tested the hypothesis that dendritic arborization and NDD-relevant behavioral phenotypes are altered by human mutations that modulate calcium-dependent signaling pathways implicated in NDDs. The dendritic morphology of pyramidal neurons in CA1 hippocampus and somatosensory cortex was quantified in Golgi-stained brain sections from juvenile mice of both sexes expressing either a human gain-of-function mutation in ryanodine receptor 1 (T4826I-RYR1), a human CGG repeat expansion (170-200 CGG repeats) in the fragile X mental retardation gene 1 (FMR1 premutation), both mutations (double mutation; DM), or wildtype mice. In hippocampal neurons, increased dendritic arborization was observed in male T4826I-RYR1 and, to a lesser extent, male FMR1 premutation neurons. Dendritic morphology of cortical neurons was altered in both sexes of FMR1 premutation and DM animals with the most pronounced differences seen in DM females. Genotype also impaired behavior, as assessed using the three-chambered social approach test. The most striking lack of sociability was observed in DM male and female mice. In conclusion, mutations that alter the fidelity of calcium signaling enhance dendritic arborization in a brain region- and sex-specific manner and impair social behavior in juvenile mice. The phenotypic outcomes of these mutations likely provide a susceptible biological substrate for additional environmental stressors that converge on calcium signaling to determine individual NDD risk.

A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

Cortical activity has distinct features across scales, from the spiking statistics of individual cells to global resting-state networks. We here describe the first full-density multi-area spiking network model of cortex, using macaque visual cortex as a test system. The model represents each area by a microcircuit with area-specific architecture and features layer- and population-resolved connectivity between areas. Simulations reveal a structured asynchronous irregular ground state. In a metastable regime, the network reproduces spiking statistics from electrophysiological recordings and cortico-cortical interaction patterns in fMRI functional connectivity under resting-state conditions. Stable inter-area propagation is supported by cortico-cortical synapses that are moderately strong onto excitatory neurons and stronger onto inhibitory neurons. Causal interactions depend on both cortical structure and the dynamical state of populations. Activity propagates mainly in the feedback direction, similar to experimental results associated with visual imagery and sleep. The model unifies local and large-scale accounts of cortex, and clarifies how the detailed connectivity of cortex shapes its dynamics on multiple scales. Based on our simulations, we hypothesize that in the spontaneous condition the brain operates in a metastable regime where cortico-cortical projections target excitatory and inhibitory populations in a balanced manner that produces substantial inter-area interactions while maintaining global stability.

The mammalian cortex fulfills its complex tasks by operating on multiple temporal and spatial scales from single cells to entire areas comprising millions of cells. These multi-scale dynamics are supported by specific network structures at all levels of organization. Since models of cortex hitherto tend to concentrate on a single scale, little is known about how cortical structure shapes the multi-scale dynamics of the network. We here present dynamical simulations of a multi-area network model at neuronal and synaptic resolution with population-specific connectivity based on extensive experimental data which accounts for a wide range of dynamical phenomena. Our model elucidates relationships between local and global scales in cortex and provides a platform for future studies of cortical function.

Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience

Computational models in neuroscience typically contain many parameters that are poorly constrained by experimental data. Uncertainty quantification and sensitivity analysis provide rigorous procedures to quantify how the model output depends on this parameter uncertainty. Unfortunately, the application of such methods is not yet standard within the field of neuroscience. Here we present Uncertainpy, an open-source Python toolbox, tailored to perform uncertainty quantification and sensitivity analysis of neuroscience models. Uncertainpy aims to make it quick and easy to get started with uncertainty analysis, without any need for detailed prior knowledge. The toolbox allows uncertainty quantification and sensitivity analysis to be performed on already existing models without needing to modify the model equations or model implementation. Uncertainpy bases its analysis on polynomial chaos expansions, which are more efficient than the more standard Monte-Carlo based approaches. Uncertainpy is tailored for neuroscience applications by its built-in capability for calculating characteristic features in the model output. The toolbox does not merely perform a point-to-point comparison of the "raw" model output (e.g., membrane voltage traces), but can also calculate the uncertainty and sensitivity of salient model response features such as spike timing, action potential width, average interspike interval, and other features relevant for various neural and neural network models. Uncertainpy comes with several common models and features built in, and including custom models and new features is easy. The aim of the current paper is to present Uncertainpy to the neuroscience community in a user-oriented manner. To demonstrate its broad applicability, we perform an uncertainty quantification and sensitivity analysis of three case studies relevant for neuroscience: the original Hodgkin-Huxley point-neuron model for action potential generation, a multi-compartmental model of a thalamic interneuron implemented in the NEURON simulator, and a sparsely connected recurrent network model implemented in the NEST simulator.

Irrelevance by inhibition: Learning, computation, and implications for schizophrenia

Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked to disruptions in cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which sensory inputs are relevant versus irrelevant. Here, we develop a neural network model that demonstrates how the cortex may learn to ignore irrelevant inputs through plasticity processes affecting inhibition. The model is based on the proposal that the amount of excitatory output from a cortical circuit encodes the expected magnitude of reward or punishment ("relevance"), which can be trained using a temporal difference learning mechanism acting on feedforward inputs to inhibitory interneurons. In the model, irrelevant and blocked stimuli drive lower levels of excitatory activity compared with novel and relevant stimuli, and this difference in activity levels is lost following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer with a threshold, the relevance code can be shown to "gate" both learning and behavioral responses to irrelevant stimuli. Accordingly, the combined network is capable of recapitulating published experimental data linking inhibition in frontal cortex with fear learning and expression. Finally, the model demonstrates how relevance learning can take place in parallel with other types of learning, through plasticity rules involving inhibitory and excitatory components, respectively. Altogether, this work offers a theory of how the cortex learns to selectively inhibit inputs, providing insight into how relevance-assignment problems may emerge in schizophrenia.

Common Defects of Spine Dynamics and Circuit Function in Neurodevelopmental Disorders: A Systematic Review of Findings From in Vivo Optical Imaging of Mouse Models

In vivo optical imaging is a powerful tool for revealing brain structure and function at both the circuit and cellular levels. Here, we provide a systematic review of findings obtained from in vivo imaging studies of mouse models of neurodevelopmental disorders, including the monogenic disorders fragile X syndrome, Rett syndrome, and Angelman syndrome, which are caused by genetic abnormalities of FMR1, MECP2, and UBE3A, as well as disorders caused by copy number variations (15q11-13 duplication and 22q11.2 deletion) and BTBR mice as an inbred strain model of autism spectrum disorder (ASD). Most studies visualize the structural and functional responsiveness of cerebral cortical neurons to sensory stimuli and the developmental and experience-dependent changes in these responses as a model of brain functions affected by these disorders. The optical imaging techniques include two-photon microscopy of fluorescently labeled dendritic spines or neurons loaded with fluorescent calcium indicators and macroscopic imaging of cortical activity using calcium indicators, voltage-sensitive dyes or intrinsic optical signals. Studies have revealed alterations in the density, stability, and turnover of dendritic spines, aberrant cortical sensory responses, impaired inhibitory function, and concomitant failure of circuit maturation as common causes for neurological deficits. Mechanistic hypotheses derived from in vivo imaging also provide new directions for therapeutic interventions. For instance, it was recently demonstrated that early postnatal administration of a selective serotonin reuptake inhibitor (SSRI) restores impaired cortical inhibitory function and ameliorates the aberrant social behaviors in a mouse model of ASD. We discuss the potential use of SSRIs for treating ASDs in light of these findings.

A Calcium-Dependent Chloride Current Increases Repetitive Firing in Mouse Sympathetic Neurons

Ca2+-activated ion channels shape membrane excitability in response to elevations in intracellular Ca2+. The most extensively studied Ca2+-sensitive ion channels are Ca2+-activated K+ channels, whereas the physiological importance of Ca2+-activated Cl- channels has been poorly studied. Here we show that a Ca2+-activated Cl- currents (CaCCs) modulate repetitive firing in mouse sympathetic ganglion cells. Electrophysiological recording of mouse sympathetic neurons in an in vitro preparation of the superior cervical ganglion (SCG) identifies neurons with two different firing patterns in response to long depolarizing current pulses (1 s). Neurons classified as phasic (Ph) made up 67% of the cell population whilst the remainders were tonic (T). When a high frequency train of spikes was induced by intracellular current injection, SCG sympathetic neurons reached an afterpotential mainly dependent on the ratio of activation of two Ca2+-dependent currents: the K+ [IK(Ca)] and CaCC. When the IK(Ca) was larger, an afterhyperpolarization was the predominant afterpotential but when the CaCC was larger, an afterdepolarization (ADP) was predominant. These afterpotentials can be observed after a single action potential (AP). Ph and T neurons had similar ADPs and hence, the CaCC does not seem to determine the firing pattern (Ph or T) of these neurons. However, inhibition of Ca2+-activated Cl- channels with anthracene-9'-carboxylic acid (9AC) selectively inhibits the ADP, reducing the firing frequency and the instantaneous frequency without affecting the characteristics of single- or first-spike firing of both Ph and T neurons. Furthermore, we found that the CaCC underlying the ADP was significantly larger in SCG neurons from males than from females. Furthermore, the CaCC ANO1/TMEM16A was more strongly expressed in male than in female SCGs. Blocking ADPs with 9AC did not modify synaptic transmission in either Ph or T neurons. We conclude that the CaCC responsible for ADPs increases repetitive firing in both Ph and T neurons, and it is more relevant in male mouse sympathetic ganglion neurons.

Neuronal Glutamate Transporters Control Dopaminergic Signaling and Compulsive Behaviors

There is an ongoing debate on the contribution of the neuronal glutamate transporter EAAC1 to the onset of compulsive behaviors. Here, we used behavioral, electrophysiological, molecular, and viral approaches in male and female mice to identify the molecular and cellular mechanisms by which EAAC1 controls the execution of repeated motor behaviors. Our findings show that, in the striatum, a brain region implicated with movement execution, EAAC1 limits group I metabotropic glutamate receptor (mGluRI) activation, facilitates D1 dopamine receptor (D1R) expression, and ensures long-term synaptic plasticity. Blocking mGluRI in slices from mice lacking EAAC1 restores D1R expression and synaptic plasticity. Conversely, activation of intracellular signaling pathways coupled to mGluRI in D1R-containing striatal neurons of mice expressing EAAC1 leads to reduced D1R protein level and increased stereotyped movement execution. These findings identify new molecular mechanisms by which EAAC1 can shape glutamatergic and dopaminergic signals and control repeated movement execution.SIGNIFICANCE STATEMENT Genetic studies implicate Slc1a1, a gene encoding the neuronal glutamate transporter EAAC1, with obsessive-compulsive disorder (OCD). EAAC1 is abundantly expressed in the striatum, a brain region that is hyperactive in OCD. What remains unknown is how EAAC1 shapes synaptic function in the striatum. Our findings show that EAAC1 limits activation of metabotropic glutamate receptors (mGluRIs) in the striatum and, by doing so, promotes D1 dopamine receptor (D1R) expression. Targeted activation of signaling cascades coupled to mGluRIs in mice expressing EAAC1 reduces D1R expression and triggers repeated motor behaviors. These findings provide new information on the molecular basis of OCD and suggest new avenues for its treatment.