Structure Of Neural Circuits Research Articles

Inferring neural circuit structure from datasets of heterogeneous tuning curves.

Tuning curves characterizing the response selectivities of biological neurons can exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or partially random connectivity also give rise to diverse tuning curves. Empirical tuning curve distributions can thus be utilized to make model-based inferences about the statistics of single-cell parameters and network connectivity. However, a general framework for such an inference or fitting procedure is lacking. We address this problem by proposing to view mechanistic network models as implicit generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable. Recent advances in machine learning provide ways for fitting implicit generative models without the need to evaluate the likelihood and its gradient. Generative Adversarial Networks (GANs) provide one such framework which has been successful in traditional machine learning tasks. We apply this approach in two separate experiments, showing how GANs can be used to fit commonly used mechanistic circuit models in theoretical neuroscience to datasets of tuning curves. This fitting procedure avoids the computationally expensive step of inferring latent variables, such as the biophysical parameters of, or synaptic connections between, particular recorded cells. Instead, it directly learns generalizable model parameters characterizing the network’s statistical structure such as the statistics of strength and spatial range of connections between different cell types. Another strength of this approach is that it fits the joint high-dimensional distribution of tuning curves, instead of matching a few summary statistics picked a priori by the user, resulting in a more accurate inference of circuit properties. More generally, this framework opens the door to direct model-based inference of circuit structure from data beyond single-cell tuning curves, such as simultaneous population recordings.

Digital Museum of Retinal Ganglion Cells with Dense Anatomy and Physiology

When 3D electron microscopy and calcium imaging are used to investigate the structure and function of neural circuits, the resulting datasets pose new challenges of visualization and interpretation. Here, we present a new kind of digital resource that encompasses almost 400 ganglion cells from a single patch of mouse retina. An online "museum" provides a 3D interactive view of each cell's anatomy, as well as graphs of its visual responses. The resource reveals two aspects of the retina's inner plexiform layer: an arbor segregation principle governing structure along the light axis and a density conservation principle governing structure in the tangential plane. Structure is related to visual function; ganglion cells with arbors near the layer of ganglion cell somas are more sustained in their visual responses on average. Our methods are potentially applicable to dense maps of neuronal anatomy and physiology in other parts of the nervous system.

18.4 CHARACTERIZING LARGE-SCALE NETWORK DYSCONNECTIVITY IN SCHIZOPHRENIA: MODELING ALTERED E/I BALANCE

BackgroundNeuropsychiatric disorders alter the structure and function of neural circuits and distributed neural networks, which leads to profound behavioral disability such as working memory impairment. Non-invasive neuroimaging tools have matured to reliably detect neural systems-level disturbances in neuropsychiatric disorders. However, mechanistic mapping from neural circuit pathology to abnormal behavior remains out of reach for most psychiatric conditions. We focus on the emerging field of ‘computational psychiatry’ to close these gaps from the perspective of E/I imbalance in schizophrenia with an emphasis on understanding large-Scale network dysconnectivity in schizophrenia.MethodsFirst, we apply computational microcircuit models to understand cognitive and neural system-level disturbances in schizophrenia as a function of altered E/I balance. In turn, the focus is placed on pharmacological neuroimaging as a powerful causal tool to probe neural circuit perturbations. Specific recent neuroimaging studies are discussed that use the NMDA receptor antagonist, ketamine, to probe glutamate synaptic dysfunction associated with schizophrenia. We leverage resting-state neuroimaging advances to inform biomarker development for network alterations in schizophrenia given its ease of data collection and lack of task-based confounds.ResultsPharmacological and clinical results implicate alterations in cortico-striato-thalamic circuits, which might constitute a final common pathway of neural system disturbances in schizophrenia. Results indicate that this functional marker appears in at-risk states, chronic patients, and following pharmacological manipulations following ketamine. Computational simulations implicate altered E/I balance in cortical microcircuits as a parsimonious upstream mechanism.DiscussionThe combined use of biophysical computational modeling, extended to large-scale neural system simulations, has proven particularly powerful to draw inferences about neural circuit alterations that may be ‘driving’ the resting-state dysconnectivity and cognitive deficits in schizophrenia. In summary, present data illustrates a framework for linking experimental studies in humans with computational models and pharmacological probes will advance to effort to bridge cellular, systems, and clinical neuroscience approaches to psychiatric disorders.

Endogenous dopamine and endocannabinoid signaling mediate cocaine-induced reversal of AMPAR synaptic potentiation in the nucleus accumbens shell

Repeated exposure to drugs of abuse alters the structure and function of neural circuits mediating reward, generating maladaptive plasticity in circuits critical for motivated behavior. Within meso-corticolimbic dopamine circuitry, repeated exposure to cocaine induces progressive alterations in AMPAR-mediated glutamatergic synaptic transmission. During a 10–14 day period of abstinence from cocaine, AMPAR signaling is potentiated at synapses on nucleus accumbens (NAc) medium spiny neurons (MSNs), promoting a state of heightened synaptic excitability. Re-exposure to cocaine during abstinence, however, rapidly reverses and depotentiates enhanced AMPAR signaling. To understand how re-exposure to cocaine alters AMPAR synaptic transmission, we investigated the roles of dopamine and endocannabinoid (eCB) signaling in modifying synaptic strength in the NAc shell. Using patch-clamp recordings from NAc slices prepared after 10–14 days of abstinence from repeated cocaine, we found that AMPAR-mediated depotentiation is rapidly induced in the NAc shell within 20 min of cocaine re-exposure ex vivo, and persists for up to five days before synapses return to levels of potentiation observed during abstinence. In cocaine-treated animals, global dopamine receptor activation was both necessary and sufficient for the cocaine-evoked depotentiation of AMPAR synaptic function. Additionally, we identified that CB1 receptors are engaged by endogenous endocannabinoids (eCBs) during re-exposure to cocaine ex vivo. Overall, these results indicate the central role that dopamine and eCB signaling mechanisms play in modulating cocaine-induced AMPAR plasticity in the NAc shell.

Conserved neural circuit structure across Drosophila larval development revealed by comparative connectomics.

During postembryonic development, the nervous system must adapt to a growing body. How changes in neuronal structure and connectivity contribute to the maintenance of appropriate circuit function remains unclear. Previously , we measured the cellular neuroanatomy underlying synaptic connectivity in Drosophila (Schneider-Mizell et al., 2016). Here, we examined how neuronal morphology and connectivity change between first instar and third instar larval stages using serial section electron microscopy. We reconstructed nociceptive circuits in a larva of each stage and found consistent topographically arranged connectivity between identified neurons. Five-fold increases in each size, number of terminal dendritic branches, and total number of synaptic inputs were accompanied by cell type-specific connectivity changes that preserved the fraction of total synaptic input associated with each pre-synaptic partner. We propose that precise patterns of structural growth act to conserve the computational function of a circuit, for example determining the location of a dangerous stimulus.

SHANK proteins: roles at the synapse and in autism spectrum disorder.

Several large-scale genomic studies have supported an association between cases of autism spectrum disorder and mutations in the genes SH3 and multiple ankyrin repeat domains protein 1 (SHANK1), SHANK2 and SHANK3, which encode a family of postsynaptic scaffolding proteins that are present at glutamatergic synapses in the CNS. An evaluation of human genetic data, as well as of in vitro and in vivo animal model data, may allow us to understand how disruption of SHANK scaffolding proteins affects the structure and function of neural circuits and alters behaviour.

The dynamic connectome

Abstract When trying to gain intuitions about the computations implemented in neural circuits, we often use comparisons with electronic circuits. However, one fundamental difference to hard-wired electronic circuits is that the structure of neural circuits undergoes constant remodeling. Here, we discuss recent findings highlighting the dynamic nature of neural circuits and the underlying mechanisms. The dynamics of neural circuits follows rules that explain steady state statistics of synaptic properties observed at a single time point. Interestingly, these rules allow the prediction of future network states and extend the insights gained from serial sectioning electron microscopy of brain samples, which inherently provides information from only a single time point. We argue how the connectome’s dynamic nature can be reconciled with stable functioning and long-termmemory storage and how it may even benefit learning.

The dynamic connectome

When trying to gain intuitions about the computations implemented in neural circuits, we often use comparisons with electronic circuits. However, one fundamental difference to hard-wired electronic circuits is that the structure of neural circuits undergoes constant remodeling. Here, we discuss recent findings highlighting the dynamic nature of neural circuits and the underlying mechanisms. The dynamics of neural circuits follows rules that explain steady state statistics of synaptic properties observed at a single time point. Interestingly, these rules allow the prediction of future network states and extend the insights gained from serial sectioning electron microscopy of brain samples, which inherently provides information from only a single time point. We argue how the connectome’s dynamic nature can be reconciled with stable functioning and long-term memory storage and how it may even benefit learning.

Shaping Neural Circuits by High Order Synaptic Interactions.

Spike timing dependent plasticity (STDP) is believed to play an important role in shaping the structure of neural circuits. Here we show that STDP generates effective interactions between synapses of different neurons, which were neglected in previous theoretical treatments, and can be described as a sum over contributions from structural motifs. These interactions can have a pivotal influence on the connectivity patterns that emerge under the influence of STDP. In particular, we consider two highly ordered forms of structure: wide synfire chains, in which groups of neurons project to each other sequentially, and self connected assemblies. We show that high order synaptic interactions can enable the formation of both structures, depending on the form of the STDP function and the time course of synaptic currents. Furthermore, within a certain regime of biophysical parameters, emergence of the ordered connectivity occurs robustly and autonomously in a stochastic network of spiking neurons, without a need to expose the neural network to structured inputs during learning.

Epigenetic Basis of Mental Illness

Psychiatric disorders are complex multifactorial illnesses involving chronic alterations in neural circuit structure and function as well as likely abnormalities in glial cells. While genetic factors are important in the etiology of most mental disorders, the relatively high rates of discordance among identical twins, particularly for depression and other stress-related syndromes, clearly indicate the importance of additional mechanisms. Environmental factors such as stress are known to play a role in the onset of these illnesses. Exposure to such environmental insults induces stable changes in gene expression, neural circuit function, and ultimately behavior, and these maladaptations appear distinct between developmental versus adult exposures. Increasing evidence indicates that these sustained abnormalities are maintained by epigenetic modifications in specific brain regions. Indeed, transcriptional dysregulation and the aberrant epigenetic regulation that underlies this dysregulation is a unifying theme in psychiatric disorders. Here, we provide a progress report of epigenetic studies of the three major psychiatric syndromes, depression, schizophrenia, and bipolar disorder. We review the literature derived from animal models of these disorders as well as from studies of postmortem brain tissue from human patients. While epigenetic studies of mental illness remain at early stages, understanding how environmental factors recruit the epigenetic machinery within specific brain regions to cause lasting changes in disease susceptibility and pathophysiology is revealing new insight into the etiology and treatment of these conditions.

Persistent Structural Plasticity Optimizes Sensory Information Processing in the Olfactory Bulb

In the mammalian brain, the anatomical structure of neural circuits changes little during adulthood. As a result, adult learning and memory are thought to result from specific changes in synaptic strength. Apossible exception is the olfactory bulb (OB), where activity guides interneuron turnover throughout adulthood. These adult-born granule cell (GC) interneurons form new GABAergic synapses that have little synaptic strength plasticity. In the face of persistent neuronal and synaptic turnover, how does the OB balance flexibility, as is required for adapting to changing sensory environments, with perceptual stability? Here we show that high dendritic spine turnover is a universal feature of GCs, regardless of their developmental origin and age. We find matching dynamics among postsynaptic sites on the principal neurons receiving the new synaptic inputs. We further demonstrate insilico that this coordinated structural plasticity is consistent with stable, yetflexible, decorrelated sensory representations. Together, our study reveals that persistent, coordinated synaptic structural plasticity between interneurons and principal neurons is a major mode of functional plasticity in theOB.

Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.

Statistical modeling implicates neuroanatomical circuit mediating stress relief by 'comfort' food.

A history of eating highly palatable foods reduces physiological and emotional responses to stress. For instance, we have previously shown that limited sucrose intake (4ml of 30% sucrose twice daily for 14days) reduces hypothalamic-pituitary-adrenocortical (HPA) axis responses to stress. However, the neural mechanisms underlying stress relief by such 'comfort' foods are unclear, and could reveal an endogenous brain pathway for stress mitigation. As such, the present work assessed the expression of several proteins related to neuronal activation and/or plasticity in multiple stress- and reward-regulatory brain regions of rats after limited sucrose (vs. water control) intake. These data were then subjected to a series of statistical analyses, including Bayesian modeling, to identify the most likely neurocircuit mediating stress relief by sucrose. The analyses suggest that sucrose reduces HPA activation by dampening an excitatory basolateral amygdala-medial amygdala circuit, while also potentiating an inhibitory bed nucleus of the stria terminalis principle subdivision-mediated circuit, resulting in reduced HPA activation after stress. Collectively, the results support the hypothesis that sucrose limits stress responses via plastic changes to the structure and function of stress-regulatory neural circuits. The work also illustrates that advanced statistical methods are useful approaches to identify potentially novel and important underlying relationships in biological datasets.

Effects of G-gene Deletion and Replacement on Rabies Virus Vector Gene Expression.

The glycoprotein-gene (G gene) -deleted rabies virus (RV) vector is a powerful tool to examine the function and structure of neural circuits. We previously reported that the deletion of the G gene enhances the transgene expression level of the RV vector. However, the mechanism of this enhancement remains to be clarified. We presume that there are two possible factors for this enhancement. The first factor is the glycoprotein of RV, which shows cytotoxicity; thus, may cause a dysfunction in the translation process of infected cells. The second possible factor is the enhanced expression of the L gene, which encodes viral RNA polymerase. In the RV, it is known that the gene expression level is altered depending on the position of the gene. Since G-gene deletion displaces the L gene in the genome, the expression of the L gene and viral transcription may be enhanced. In this study, we compared the transgene expression level and viral transcription of three recombinant RV vectors. The effect of glycoprotein was examined by comparing the viral gene expression of G-gene-intact RV and G-gene-replaced RV. Despite the fact that the L-gene transcription level of these two RV vectors was similar, the G-gene-replaced RV vector showed higher viral transcription and transgene expression level than the G-gene-intact RV vector. To examine the effect of the position of the L gene, we compared the viral gene expression of the G-gene-deleted RV and G-gene-replaced RV. The G-gene-deleted RV vector showed higher L-gene transcription, viral transcription, and transgene expression level than the G-gene-replaced RV vector. These results indicate that G-gene deletion enhances the transgene expression level through at least two factors, the absence of glycoprotein and enhancement of L-gene expression. These findings enable investigators to design a useful viral vector that shows a controlled desirable transgene expression level in applications.

Visible rodent brain-wide networks at single-neuron resolution

There are some unsolvable fundamental questions, such as cell type classification, neural circuit tracing and neurovascular coupling, though great progresses are being made in neuroscience. Because of the structural features of neurons and neural circuits, the solution of these questions needs us to break through the current technology of neuroanatomy for acquiring the exactly fine morphology of neuron and vessels and tracing long-distant circuit at axonal resolution in the whole brain of mammals. Combined with fast-developing labeling techniques, efficient whole-brain optical imaging technology emerging at the right moment presents a huge potential in the structure and function research of specific-function neuron and neural circuit. In this review, we summarize brain-wide optical tomography techniques, review the progress on visible brain neuronal/vascular networks benefit from these novel techniques, and prospect the future technical development.

Chronic Co‐Variation of Neural Network Configuration and Activity in Mature Dissociated Cultures

SUMMARYSpatiotemporal neural patterns depend on the physical structure of neural circuits. Neural plasticity can thus be associated with changes in the circuit structure. For example, newborn neurons migrate toward existing, already matured, neural networks in order to participate in neural computation. In the present study, we have conducted two experiments to investigate how neural migration is associated with the development of neural activity in primary dissociated cultures of neuronal cells. In Experiment 1, using a mature culture, a high‐density CMOS microelectrode array was used to continuously monitor neural migration and activity for more than two weeks. Consequently, we found that even in mature neuronal cultures neurons moved 2.0 ± 1.0 μm a day and that the moving distance was negatively correlated with their firing rate, suggesting that neurons featuring low firing rates tend to migrate actively. In Experiment 2 using a co‐culture of mature and immature neurons, we found that immature neurons moved more actively than matured neurons to achieve functional connections to other neurons. These findings suggest that neurons with low firing rates as well as newborn neurons actively migrate in order to establish their connections and function in a neuronal network.

Anatomical identification of extracellularly recorded cells in large-scale multielectrode recordings.

This study combines for the first time two major approaches to understanding the function and structure of neural circuits: large-scale multielectrode recordings, and confocal imaging of labeled neurons. To achieve this end, we develop a novel approach to the central problem of anatomically identifying recorded cells, based on the electrical image: the spatiotemporal pattern of voltage deflections induced by spikes on a large-scale, high-density multielectrode array. Recordings were performed from identified ganglion cell types in the macaque retina. Anatomical images of cells in the same preparation were obtained using virally transfected fluorescent labeling or by immunolabeling after fixation. The electrical image was then used to locate recorded cell somas, axon initial segments, and axon trajectories, and these signatures were used to identify recorded cells. Comparison of anatomical and physiological measurements permitted visualization and physiological characterization of numerically dominant ganglion cell types with high efficiency in a single preparation.

Anterior cingulate cortex gray matter volume mediates an association between 2D:4D ratio and trait aggression in women but not men

Previous research demonstrates that prenatal testosterone exposure increases aggression, possibly through its effects on the structure and function of neural circuits supporting threat detection and emotion regulation. Here we examined associations between regional gray matter volume, trait aggression, and the ratio of the second and fourth digit of the hand (2D:4D ratio) as a putative index of prenatal testosterone exposure in 464 healthy young adult volunteers. Our analyses revealed a significant positive correlation between 2D:4D ratio and gray matter volume of the dorsal anterior cingulate cortex (dACC), a brain region supporting emotion regulation, conflict monitoring, and behavioral inhibition. Subsequent analyses demonstrated that reduced (i.e., masculinized) gray matter volume in the dACC mediated the relationship between 2D:4D ratio and aggression in women, but not men. Expanding on this gender-specific mediation, additional analyses demonstrated that the shared variance between 2D:4D ratio, dACC gray matter volume, and aggression in women reflected the tendency to engage in cognitive reappraisal of emotionally provocative stimuli. Our results provide novel evidence that 2D:4D ratio is associated with masculinization of dACC gray matter volume, and that this neural phenotype mediates, in part, the expression of trait aggression in women.

From the connectome to brain function: rabies virus tools for elucidating structure and function of neural circuits

From the connectome to brain function: rabies virus tools for elucidating structure and function of neural circuits

Novel advancements in three-dimensional neural tissue engineering and regenerative medicine.

Neurological diseases and injuries present some of the greatest challenges in modern medicine, often causing irreversible and lifelong burdens in the people whom they afflict. These diagnoses have devastating consequences on millions of people each year, and yet there are currently no therapies or interventions that can repair the structure of neural circuits and restore neural tissue function in the brain and spinal cord. Despite the challenges of overcoming these limitations, there are many new approaches under development that hold much promise. Neural tissue engineering aims to restore and influence the function of damaged or diseased neural tissue generally through the use of stem cells and biomaterials. In this paper, several new 3D tissue constructs and designs are described for functional reconstruction of neural architecture. With the use of induced pluripotent stem cells or induced neuronal cells, these 3D constructs could then be studied as regional models of the central nervous system or could one day be implemented as autologous grafts into damaged sites of the nervous system in order to restore neural function, particularly for damaged sites of spinal cord, areas of stroke infarction, tumor resection sites, peripheral nerve injuries, or areas of neurodegeneration.