Number and laminar distribution of neurons in a thalamocortical projection column of rat vibrissal cortex

At the onset of active whisking, the input layer of barrel cortex exhibits a 24 h window of increased excitability that depends on prior experience

The development of motor control over sensory organs is a critical milestone in sensory processing, enabling active exploration and shaping of the sensory environment. However, whether the onset of sensory organ motor control directly influences the development of corresponding sensory cortices remains unknown. Here, we exploit the late onset of whisking behavior in mice to address this question in the somatosensory system. Using ex vivo electrophysiology, we discovered a transient increase in the intrinsic excitability of excitatory neurons in layer IV of the barrel cortex, which processes whisker input, precisely coinciding with the onset of active whisking at postnatal day 14 (P14). This increase in neuronal gain was specific to layer IV, independent of changes in synaptic strength, and required prior sensory experience. Strikingly, the effect was not observed in layer II/III of the barrel cortex or in the visual cortex upon eye opening, suggesting a unique interaction between the development of active sensing and the thalamocortical input layer in the somatosensory system. Predictive modeling indicated that changes in active membrane conductances alone could reliably distinguish P14 neurons in control but not whisker-deprived hemispheres. Our findings demonstrate an experience-dependent, lamina-specific refinement of neuronal excitability tightly linked to the emergence of active whisking. This transient increase in the gain of the thalamic input layer coincides with a critical period for synaptic plasticity in downstream layers, suggesting a role in facilitating cortical maturation and sensory processing. Together, our results provide evidence for a direct interaction between the development of motor control and sensory cortex, offering new insights into the experience-dependent development and refinement of sensory systems. These findings have broad implications for understanding the interplay between motor and sensory development, and how the mechanisms of perception cooperate with behavior.

Nanoscale analysis of functionally diverse glutamatergic synapses in the neocortex reveals input and layer-specific organization

Discovery of synaptic nanostructures suggests a molecular logic for the flexibility of synaptic function. We still have little understanding of how functionally diverse synapses in the brain organize their nanoarchitecture due to challenges associated with super-resolution imaging in complex brain tissue. Here, we characterized single-domain camelid nanobodies for the 3D quantitative multiplex imaging of synaptic nano-organization in 6 µm brain cryosections using STED nanoscopy. We focused on thalamocortical (TC) and corticocortical (CC) synapses along the apical-basal axis of layer 5 pyramidal neurons as models of functionally diverse glutamatergic synapses in the brain. Spines receiving TC input were larger than CC spines in all layers examined. However, TC synapses on apical and basal dendrites conformed to different organizational principles. TC afferents on apical dendrites frequently contacted spines with multiple aligned PSD-95/Bassoon nanomodules, which are larger. TC spines on basal dendrites contained mostly one aligned PSD-95/Bassoon nanocluster. However, PSD-95 nanoclusters were larger and scaled with spine volume. The nano-organization of CC synapses did not change across cortical layers. These results highlight striking nanoscale diversity of functionally distinct glutamatergic synapses, relying on afferent input and sub-cellular localization of individual synaptic connections.

Layer 6b controls brain state via apical dendrites and the higher-order thalamocortical system

The deepest layer of the cortex (layer 6b [L6b]) contains relatively few neurons, but it is the only cortical layer responsive to the potent wake-promoting neuropeptide orexin/hypocretin. Can these few neurons significantly influence brain state? Here, we show that L6b-photoactivation causes a surprisingly robust enhancement of attention-associated high-gamma oscillations and population spiking while abolishing slow waves in sleep-deprived mice. To explain this powerful impact on brain state, we investigated L6b's synaptic output using optogenetics, electrophysiology, and monoCaTChR ex vivo. We found powerful output in the higher-order thalamus and apical dendrites of L5 pyramidal neurons, via L1a and L5a, as well as in superior colliculus and L6 interneurons. L6b subpopulations with distinct morphologies and short- and long-term plasticities project to these diverse targets. The L1a-targeting subpopulation triggered powerful NMDA-receptor-dependent spikes that elicited burst firing in L5. We conclude that orexin/hypocretin-activated cortical neurons form a multifaceted, fine-tuned circuit for the sustained control of the higher-order thalamocortical system.

Shared input and recurrency in neural networks for metabolically efficient information transmission

Shared input to a population of neurons induces noise correlations, which can decrease the information carried by a population activity. Inhibitory feedback in recurrent neural networks can reduce the noise correlations and thus increase the information carried by the population activity. However, the activity of inhibitory neurons is costly. This inhibitory feedback decreases the gain of the population. Thus, depolarization of its neurons requires stronger excitatory synaptic input, which is associated with higher ATP consumption. Given that the goal of neural populations is to transmit as much information as possible at minimal metabolic costs, it is unclear whether the increased information transmission reliability provided by inhibitory feedback compensates for the additional costs. We analyze this problem in a network of leaky integrate-and-fire neurons receiving correlated input. By maximizing mutual information with metabolic cost constraints, we show that there is an optimal strength of recurrent connections in the network, which maximizes the value of mutual information-per-cost. For higher values of input correlation, the mutual information-per-cost is higher for recurrent networks with inhibitory feedback compared to feedforward networks without any inhibitory neurons. Our results, therefore, show that the optimal synaptic strength of a recurrent network can be inferred from metabolically efficient coding arguments and that decorrelation of the input by inhibitory feedback compensates for the associated increased metabolic costs.

Neuronal composition of processing modules in human V1: laminar density for neuronal and non-neuronal populations and a comparison with macaque

The neuronal composition of homologous brain regions in different primates is important for understanding their processing capacities. Primary visual cortex (V1) has been widely studied in different members of the catarrhines. Neuronal density is considered to be central in defining the structure-function relationship. In human, there are large variations in the reported neuronal density from prior studies. We found the neuronal density in human V1 was 79,000 neurons/mm3, which is 35% of the neuronal density previously determined in macaque V1. Laminar density was proportionally similar between human and macaque. In V1, the ocular dominance column (ODC) contains the circuits for the emergence of orientation preference and spatial processing of a point image in many mammalian species. Analysis of the total neurons in an ODC and of the full number of neurons in macular vision (the central 15°) indicates that humans have 1.3× more neurons than macaques even though the density of neurons in macaque is 3× the density in human V1. We propose that the number of neurons in a functional processing unit rather than the number of neurons under a mm2 of cortex is more appropriate for cortical comparisons across species.

Simulation-based inference for efficient identification of generative models in computational connectomics

Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.

Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics

Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.

Targeting operational regimes of interest in recurrent neural networks

Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require tractable spiking network models that can consistently incorporate new information about the network structure and reproduce the recorded neural activity features. However, for spiking networks, it is challenging to predict which connectivity configurations and neural properties can generate fundamental operational states and specific experimentally reported nonlinear cortical computations. Theoretical descriptions for the computational state of cortical spiking circuits are diverse, including the balanced state where excitatory and inhibitory inputs balance almost perfectly or the inhibition stabilized state (ISN) where the excitatory part of the circuit is unstable. It remains an open question whether these states can co-exist with experimentally reported nonlinear computations and whether they can be recovered in biologically realistic implementations of spiking networks. Here, we show how to identify spiking network connectivity patterns underlying diverse nonlinear computations such as XOR, bistability, inhibitory stabilization, supersaturation, and persistent activity. We establish a mapping between the stabilized supralinear network (SSN) and spiking activity which allows us to pinpoint the location in parameter space where these activity regimes occur. Notably, we find that biologically-sized spiking networks can have irregular asynchronous activity that does not require strong excitation-inhibition balance or large feedforward input and we show that the dynamic firing rate trajectories in spiking networks can be precisely targeted without error-driven training algorithms.

Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture

The complexity of the cerebral cortex underlies its function and distinguishes us as humans. Here, we present a principled veridical data science methodology for quantitative histology that shifts focus from image-level investigations towards neuron-level representations of cortical regions, with the neurons in the image as a subject of study, rather than pixel-wise image content. Our methodology relies on the automatic segmentation of neurons across whole histological sections and an extensive set of engineered features, which reflect the neuronal phenotype of individual neurons and the properties of neurons' neighborhoods. The neuron-level representations are used in an interpretable machine learning pipeline for mapping the phenotype to cortical layers. To validate our approach, we created a unique dataset of cortical layers manually annotated by three experts in neuroanatomy and histology. The presented methodology offers high interpretability of the results, providing a deeper understanding of human cortex organization, which may help formulate new scientific hypotheses, as well as to cope with systematic uncertainty in data and model predictions.

Mapping thalamic innervation to individual L2/3 pyramidal neurons and modeling their 'readout' of visual input

The thalamus is the main gateway for sensory information from the periphery to the mammalian cerebral cortex. A major conundrum has been the discrepancy between the thalamus's central role as the primary feedforward projection system into the neocortex and the sparseness of thalamocortical synapses. Here we use new methods, combining genetic tools and scalable tissue expansion microscopy for whole-cell synaptic mapping, revealing the number, density and size of thalamic versus cortical excitatory synapses onto individual layer 2/3 (L2/3) pyramidal cells (PCs) of the mouse primary visual cortex. We find that thalamic inputs are not only sparse, but remarkably heterogeneous in number and density across individual dendrites and neurons. Most surprising, despite their sparseness, thalamic synapses onto L2/3 PCs are smaller than their cortical counterparts. Incorporating these findings into fine-scale, anatomically faithful biophysical models of L2/3 PCs reveals how individual neurons with sparse and weak thalamocortical synapses, embedded in small heterogeneous neuronal ensembles, may reliably 'read out' visually driven thalamic input.

Computational Concepts for Reconstructing and Simulating Brain Tissue

It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.

Sensory Experience as a Regulator of Structural Plasticity in the Developing Whisker-to-Barrel System

Cellular structures provide the physical foundation for the functionality of the nervous system, and their developmental trajectory can be influenced by the characteristics of the external environment that an organism interacts with. Historical and recent works have determined that sensory experiences, particularly during developmental critical periods, are crucial for information processing in the brain, which in turn profoundly influence neuronal and non-neuronal cortical structures that subsequently impact the animals' behavioral and cognitive outputs. In this review, we focus on how altering sensory experience influences normal/healthy development of the central nervous system, particularly focusing on the cerebral cortex using the rodent whisker-to-barrel system as an illustrative model. A better understanding of structural plasticity, encompassing multiple aspects such as neuronal, glial, and extra-cellular domains, provides a more integrative view allowing for a deeper appreciation of how all aspects of the brain work together as a whole.

Cortical Representation of Touch in Silico

With its six layers and ~ 12,000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents'. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here we introduce an open-source, biologically inspired, computationally efficient network model of the somatosensory cortex's granular and supragranular layers after reconstructing the barrel cortex in soma resolution. Comparisons of the network activity to empirical observations showed that the in silico network replicates the known properties of touch representations and whisker deprivation-induced changes in synaptic strength induced in vivo. Simulations show that the history of the membrane potential acts as a spatial filter that determines the presynaptic population of neurons contributing to a post-synaptic action potential; this spatial filtering might be critical for synaptic integration of top-down and bottom-up information.

Effects of arousal and movement on secondary somatosensory and visual thalamus

Neocortical sensory areas have associated primary and secondary thalamic nuclei. While primary nuclei transmit sensory information to cortex, secondary nuclei remain poorly understood. We recorded juxtasomally from secondary somatosensory (POm) and visual (LP) nuclei of awake mice while tracking whisking and pupil size. POm activity correlated with whisking, but not precise whisker kinematics. This coarse movement modulation persisted after facial paralysis and thus was not due to sensory reafference. This phenomenon also continued during optogenetic silencing of somatosensory and motor cortex and after lesion of superior colliculus, ruling out a motor efference copy mechanism. Whisking and pupil dilation were strongly correlated, possibly reflecting arousal. Indeed LP, which is not part of the whisker system, tracked whisking equally well, further indicating that POm activity does not encode whisker movement per se. The semblance of movement-related activity is likely instead a global effect of arousal on both nuclei. We conclude that secondary thalamus monitors behavioral state, rather than movement, and may exist to alter cortical activity accordingly.

Corticothalamic Pathways From Layer 5: Emerging Roles in Computation and Pathology

Large portions of the thalamus receive strong driving input from cortical layer 5 (L5) neurons but the role of this important pathway in cortical and thalamic computations is not well understood. L5-recipient "higher-order" thalamic regions participate in cortico-thalamo-cortical (CTC) circuits that are increasingly recognized to be (1) anatomically and functionally distinct from better-studied "first-order" CTC networks, and (2) integral to cortical activity related to learning and perception. Additionally, studies are beginning to elucidate the clinical relevance of these networks, as dysfunction across these pathways have been implicated in several pathological states. In this review, we highlight recent advances in understanding L5 CTC networks across sensory modalities and brain regions, particularly studies leveraging cell-type-specific tools that allow precise experimental access to L5 CTC circuits. We aim to provide a focused and accessible summary of the anatomical, physiological, and computational properties of L5-originating CTC networks, and outline their underappreciated contribution in pathology. We particularly seek to connect single-neuron and synaptic properties to network (dys)function and emerging theories of cortical computation, and highlight information processing in L5 CTC networks as a promising focus for computational studies.

Cellular connectomes as arbiters of local circuit models in the cerebral cortex

With the availability of cellular-resolution connectivity maps, connectomes, from the mammalian nervous system, it is in question how informative such massive connectomic data can be for the distinction of local circuit models in the mammalian cerebral cortex. Here, we investigated whether cellular-resolution connectomic data can in principle allow model discrimination for local circuit modules in layer 4 of mouse primary somatosensory cortex. We used approximate Bayesian model selection based on a set of simple connectome statistics to compute the posterior probability over proposed models given a to-be-measured connectome. We find that the distinction of the investigated local cortical models is faithfully possible based on purely structural connectomic data with an accuracy of more than 90%, and that such distinction is stable against substantial errors in the connectome measurement. Furthermore, mapping a fraction of only 10% of the local connectome is sufficient for connectome-based model distinction under realistic experimental constraints. Together, these results show for a concrete local circuit example that connectomic data allows model selection in the cerebral cortex and define the experimental strategy for obtaining such connectomic data.

The emerging role of chromatin remodelers in neurodevelopmental disorders: a developmental perspective

Neurodevelopmental disorders (NDDs), including intellectual disability (ID) and autism spectrum disorders (ASD), are a large group of disorders in which early insults during brain development result in a wide and heterogeneous spectrum of clinical diagnoses. Mutations in genes coding for chromatin remodelers are overrepresented in NDD cohorts, pointing towards epigenetics as a convergent pathogenic pathway between these disorders. In this review we detail the role of NDD-associated chromatin remodelers during the developmental continuum of progenitor expansion, differentiation, cell-type specification, migration and maturation. We discuss how defects in chromatin remodelling during these early developmental time points compound over time and result in impaired brain circuit establishment. In particular, we focus on their role in the three largest cell populations: glutamatergic neurons, GABAergic neurons, and glia cells. An in-depth understanding of the spatiotemporal role of chromatin remodelers during neurodevelopment can contribute to the identification of molecular targets for treatment strategies.

A network model of the barrel cortex combined with a differentiator detector reproduces features of the behavioral response to single-neuron stimulation

The stimulation of a single neuron in the rat somatosensory cortex can elicit a behavioral response. The probability of a behavioral response does not depend appreciably on the duration or intensity of a constant stimulation, whereas the response probability increases significantly upon injection of an irregular current. Biological mechanisms that can potentially suppress a constant input signal are present in the dynamics of both neurons and synapses and seem ideal candidates to explain these experimental findings. Here, we study a large network of integrate-and-fire neurons with several salient features of neuronal populations in the rat barrel cortex. The model includes cellular spike-frequency adaptation, experimentally constrained numbers and types of chemical synapses endowed with short-term plasticity, and gap junctions. Numerical simulations of this model indicate that cellular and synaptic adaptation mechanisms alone may not suffice to account for the experimental results if the local network activity is read out by an integrator. However, a circuit that approximates a differentiator can detect the single-cell stimulation with a reliability that barely depends on the length or intensity of the stimulus, but that increases when an irregular signal is used. This finding is in accordance with the experimental results obtained for the stimulation of a regularly-spiking excitatory cell.

It is widely assumed that only a large group of neurons can encode a stimulus or control behavior. This tenet of neuroscience has been challenged by experiments in which stimulating a single cortical neuron has had a measurable effect on an animal’s behavior. Recently, theoretical studies have explored how a single-neuron stimulation could be detected in a large recurrent network. However, these studies missed essential biological mechanisms of cortical networks and are unable to explain more recent experiments in the barrel cortex. Here, to describe the stimulated brain area, we propose and study a network model endowed with many important biological features of the barrel cortex. Importantly, we also investigate different readout mechanisms, i.e. ways in which the stimulation effects can propagate to other brain areas. We show that a readout network which tracks rapid variations in the local network activity is in agreement with the experiments. Our model demonstrates a possible mechanism for how the stimulation of a single neuron translates into a signal at the population level, which is taken as a proxy of the animal’s response. Our results illustrate the power of spiking neural networks to properly describe the effects of a single neuron’s activity.

Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception

The array of whiskers on the snout provides rodents with tactile sensory information relating to the size, shape and texture of objects in their immediate environment. Rodents can use their whiskers to detect stimuli, distinguish textures, locate objects and navigate. Important aspects of whisker sensation are thought to result from neuronal computations in the whisker somatosensory cortex (wS1). Each whisker is individually represented in the somatotopic map of wS1 by an anatomical unit named a 'barrel' (hence also called barrel cortex). This allows precise investigation of sensory processing in the context of a well-defined map. Here, we first review the signaling pathways from the whiskers to wS1, and then discuss current understanding of the various types of excitatory and inhibitory neurons present within wS1. Different classes of cells can be defined according to anatomical, electrophysiological and molecular features. The synaptic connectivity of neurons within local wS1 microcircuits, as well as their long-range interactions and the impact of neuromodulators, are beginning to be understood. Recent technological progress has allowed cell-type-specific connectivity to be related to cell-type-specific activity during whisker-related behaviors. An important goal for future research is to obtain a causal and mechanistic understanding of how selected aspects of tactile sensory information are processed by specific types of neurons in the synaptically connected neuronal networks of wS1 and signaled to downstream brain areas, thus contributing to sensory-guided decision-making.

Effects of Cortical Cooling on Activity Across Layers of the Rat Barrel Cortex

Moderate cortical cooling is known to suppress slow oscillations and to evoke persistent cortical activity. However, the cooling-induced changes in electrical activity across cortical layers remain largely unknown. Here, we performed multi-channel local field potential (LFP) and multi-unit activity (MUA) recordings with linear silicone probes through the layers of single cortical barrel columns in urethane-anesthetized rats under normothermia (38°C) and during local cortical surface cooling (30°C). During cortically generated slow oscillations, moderate cortical cooling decreased delta wave amplitude, delta-wave occurrence, the duration of silent states, and delta wave-locked MUA synchronization. Moderate cortical cooling increased total time spent in the active state and decreased total time spent in the silent state. Cooling-evoked changes in the MUA firing rate in cortical layer 5 (L5) varied from increase to decrease across animals, and the polarity of changes in L5 MUA correlated with changes in total time spent in the active state. The decrease in temperature reduced MUA firing rates in all other cortical layers. Sensory-evoked MUA responses also decreased during cooling through all cortical layers. The cooling-dependent slowdown was detected at the fast time-scale with a decreased frequency of sensory-evoked high-frequency oscillations (HFO). Thus, moderate cortical cooling suppresses slow oscillations and desynchronizes neuronal activity through all cortical layers, and is associated with reduced firing across all cortical layers except L5, where cooling induces variable and non-consistent changes in neuronal firing, which are common features of the transition from slow-wave synchronization to desynchronized activity in the barrel cortex.

Excitation and emission dual-wavelength confocal metalens designed directly in the biological tissue environment for two-photon micro-endoscopy

With the advantages of completely controlling the phase, amplitude, and polarization in subwavelength range, metalenses have drawn intensive attentions in high resolution two-photon micro-endoscopic fluorescence imaging system. However, chromatic dispersion and severe scattering of biological tissue significantly reduce excitation-collection efficiency in the traditional two-photon imaging system based on traditional metalenses designed in the air background. Here, an excitation and emission dual-wavelength confocal and polarization-insensitive metalens designed in the biological tissue environment was proposed by adopting the composite embedding structure and spatial multiplexing approach. The metalens with numerical aperture (NA) of 0.895 can focus the excitation (915 nm) and emission (510 nm) beams to the same focal spot in the mouse cortex. According to the theoretical simulation of two-photon fluorescence imaging, the lateral resolution of the collected fluorescent spots via the proposed metalens can be up to 0.42 µm. Compared to the metalens designed in the air environment, the collection efficiency of fluorescent spot is improved from 5.92% to 14.60%. Our investigation has opened a new window of high resolution and minimally invasive imaging in deep regions of biological tissues.

Human in vitro systems for examining synaptic function and plasticity in the brain

The human brain shows remarkable complexity in its cellular makeup and function, which are distinct from nonhuman species, signifying the need for human-based research platforms for the study of human cellular neurophysiology and neuropathology. However, the use of adult human brain tissue for research purposes is hampered by technical, methodological, and accessibility challenges. One of the major problems is the limited number of in vitro systems that, in contrast, are readily available from rodent brain tissue. With recent advances in the optimization of protocols for adult human brain preparations, there is a significant opportunity for neuroscientists to validate their findings in human-based systems. This review addresses the methodological aspects, advantages, and disadvantages of human neuron in vitro systems, focusing on the unique properties of human neurons and synapses in neocortical microcircuits. These in vitro models provide the incomparable advantage of being a direct representation of the neurons that have formed part of the human brain until the point of recording, which cannot be replicated by animal models nor human stem-cell systems. Important distinct cellular mechanisms are observed in human neurons that may underlie the higher order cognitive abilities of the human brain. The use of human brain tissue in neuroscience research also raises important ethical, diversity, and control tissue limitations that need to be considered. Undoubtedly however, these human neuron systems provide critical information to increase the potential of translation of treatments from the laboratory to the clinic in a way animal models are failing to provide.

Quantitative 3D Ultrastructure of Thalamocortical Synapses from the "Lemniscal" Ventral Posteromedial Nucleus in Mouse Barrel Cortex

Thalamocortical synapses from “lemniscal” neurons of the dorsomedial portion of the rodent ventral posteromedial nucleus (VPMdm) are able to induce with remarkable efficacy, despite their relative low numbers, the firing of primary somatosensory cortex (S1) layer 4 (L4) neurons. To which extent this high efficacy depends on structural synaptic features remains unclear. Using both serial transmission (TEM) and focused ion beam milling scanning electron microscopy (FIB/SEM), we 3D-reconstructed and quantitatively analyzed anterogradely labeled VPMdm axons in L4 of adult mouse S1. All VPMdm synapses are asymmetric. Virtually all are established by axonal boutons, 53% of which contact multiple (2–4) elements (overall synapse/bouton ratio = 1.6). Most boutons are large (mean 0.47 μm³), and contain 1–3 mitochondria. Vesicle pools and postsynaptic density (PSD) surface areas are large compared to others in rodent cortex. Most PSDs are complex. Most synapses (83%) are established on dendritic spine heads. Furthermore, 15% of the postsynaptic spines receive a second, symmetric synapse. In addition, 13% of the spine heads have a large protrusion inserted into a membrane pouch of the VPMdm bouton. The unusual combination of structural features in VPMdm synapses is likely to contribute significantly to the high efficacy, strength, and plasticity of these thalamocortical synapses.

Electrophysiological and Molecular Characterization of the Parasubiculum

The parahippocampal region is thought to be critical for memory and spatial navigation. Within this region lies the parasubiculum, a small structure that exhibits strong theta modulation, contains functionally specialized cells, and projects to layer II of the medial entorhinal cortex (MEC). Thus, it is uniquely positioned to influence firing of spatially modulated cells in the MEC and play a key role in the internal representation of the external environment. However, the basic neuronal composition of the parasubiculum remains largely unknown, and its border with the MEC is often ambiguous. We combine electrophysiology and immunohistochemistry in adult mice (both sexes) to define first, the boundaries of the parasubiculum, and second, the major cell types found in this region. We find distinct differences in the colabeling of molecular markers between the parasubiculum and the MEC, allowing us to clearly separate the two structures. Moreover, we find distinct distribution patterns of different molecular markers within the parasubiculum, across both superficial-deep and DV axes. Using unsupervised cluster analysis, we find that neurons in the parasubiculum can be broadly separated into three clusters based on their electrophysiological properties, and that each cluster corresponds to a different molecular marker. We demonstrate that, while the parasubiculum aligns structurally to some to general cortical principals, it also shows divergent features in particular in contrast to the MEC. This work will form an important basis for future studies working to disentangle the circuitry underlying memory and spatial navigation functions of the parasubiculum.SIGNIFICANCE STATEMENT We identify the major neuron types in the parasubiculum using immunohistochemistry and electrophysiology, and determine their distribution throughout the parasubiculum. We find that the neuronal composition of the parasubiculum differs considerably compared with the neighboring medial entorhinal cortex. Both regions are involved in spatial navigation. Thus, our findings are of importance for unraveling the underlying circuitry of this process and for determining the role of the parasubiculum within this network.

A Non-canonical Feedback Circuit for Rapid Interactions between Somatosensory Cortices

Sensory perception depends on interactions among cortical areas. These interactions are mediated by canonical patterns of connectivity in which higher areas send feedback projections to lower areas via neurons in superficial and deep layers. Here, we probed the circuit basis of interactions among two areas critical for touch perception in mice, whisker primary (wS1) and secondary (wS2) somatosensory cortices. Neurons in layer 4 of wS2 (S2_L4) formed a major feedback pathway to wS1. Feedback from wS2 to wS1 was organized somatotopically. Spikes evoked by whisker deflections occurred nearly as rapidly in wS2 as in wS1, including among putative S2_L4 → S1 feedback neurons. Axons from S2_L4 → S1 neurons sent stimulus orientation-specific activity to wS1. Optogenetic excitation of S2_L4 neurons modulated activity across both wS2 and wS1, while inhibition of S2_L4 reduced orientation tuning among wS1 neurons. Thus, a non-canonical feedback circuit, originating in layer 4 of S2, rapidly modulates early tactile processing.

Cell numbers, distribution, shape, and regional variation throughout the murine hippocampal formation from the adult brain Allen Reference Atlas

Quantifying the distribution of cells in every brain region is fundamental to attaining a comprehensive census of distinct neuronal and glial types. Until recently, estimating neuron numbers involved time-consuming procedures that were practically limited to stereological sampling. Progress in open-source image recognition software, growth in computing power, and unprecedented neuroinformatics developments now offer the potentially paradigm-shifting alternative of comprehensive cell-by-cell analysis in an entire brain region. The Allen Brain Atlas provides free digital access to complete series of raw Nissl-stained histological section images along with regional delineations. Automated cell segmentation of these data enables reliable and reproducible high-throughput quantification of regional variations in cell count, density, size, and shape at whole-system scale. While this strategy is directly applicable to any regions of the mouse brain, we first deploy it here on the closed-loop circuit of the hippocampal formation: the medial and lateral entorhinal cortices; dentate gyrus (DG); areas Cornu Ammonis 3 (CA3), CA2, and CA1; and dorsal and ventral subiculum. Using two independent image processing pipelines and the adult mouse reference atlas, we report the first cellular-level soma segmentation in every sub-region and non-principal layer of the left hippocampal formation through the full rostral-caudal extent. It is important to note that our techniques excluded the layers with the largest number of cells, DG granular and CA pyramidal, due to dense packing. The numerical estimates for the remaining layers are corroborated by traditional stereological sampling on a data subset and well match sparse published reports.

Dense connectomic reconstruction in layer 4 of the somatosensory cortex

The dense circuit structure of mammalian cerebral cortex is still unknown. With developments in three-dimensional electron microscopy, the imaging of sizable volumes of neuropil has become possible, but dense reconstruction of connectomes is the limiting step. We reconstructed a volume of ~500,000 cubic micrometers from layer 4 of mouse barrel cortex, ~300 times larger than previous dense reconstructions from the mammalian cerebral cortex. The connectomic data allowed the extraction of inhibitory and excitatory neuron subtypes that were not predictable from geometric information. We quantified connectomic imprints consistent with Hebbian synaptic weight adaptation, which yielded upper bounds for the fraction of the circuit consistent with saturated long-term potentiation. These data establish an approach for the locally dense connectomic phenotyping of neuronal circuitry in the mammalian cortex.

Anatomical Correlates of Local, Translaminar, and Transcolumnar Inhibition by Layer 6 GABAergic Interneurons in Somatosensory Cortex

In the vibrissal area of rodent somatosensory cortex, information on whisker stimulation is processed by neuronal networks in a corresponding cortical column. To understand how sensory stimuli are represented in a column, it is essential to identify cell types constituting these networks. Layer 6 (L6) comprises 25% of all neurons in a column. In rats, 430 of these are inhibitory interneurons (INs). Little is known about the axon projection of L6 INs with reference to columnar and laminar organization. We quantified axonal projections of L6 INs (n = 68) with reference to columns and layers in somatosensory cortex of rats. We found distinct projection types differentially targeting layers of a cortical column. The majority of L6 INs did not show a column-specific innervation, densely projecting to neighboring columns as well as the home column. However, a small fraction targeted granular and supragranular layers, where axon projections were confined to the home column. We also quantified putative innervation of pyramidal cells as a functional correlate of axonal distribution. Electrophysiological properties were not correlated to axon projection. The quantitative data on axonal projections and electrophysiological properties of L6 INs can guide future studies investigating cortical processing of sensory information at the single cell level.

Correlation Transfer by Layer 5 Cortical Neurons Under Recreated Synaptic Inputs In Vitro

Correlated electrical activity in neurons is a prominent characteristic of cortical microcircuits. Despite a growing amount of evidence concerning both spike-count and subthreshold membrane potential pairwise correlations, little is known about how different types of cortical neurons convert correlated inputs into correlated outputs. We studied pyramidal neurons and two classes of GABAergic interneurons of layer 5 in neocortical brain slices obtained from rats of both sexes, and we stimulated them with biophysically realistic correlated inputs, generated using dynamic clamp. We found that the physiological differences between cell types manifested unique features in their capacity to transfer correlated inputs. We used linear response theory and computational modeling to gain clear insights into how cellular properties determine both the gain and timescale of correlation transfer, thus tying single-cell features with network interactions. Our results provide further ground for the functionally distinct roles played by various types of neuronal cells in the cortical microcircuit.SIGNIFICANCE STATEMENT No matter how we probe the brain, we find correlated neuronal activity over a variety of spatial and temporal scales. For the cerebral cortex, significant evidence has accumulated on trial-to-trial covariability in synaptic inputs activation, subthreshold membrane potential fluctuations, and output spike trains. Although we do not yet fully understand their origin and whether they are detrimental or beneficial for information processing, we believe that clarifying how correlations emerge is pivotal for understanding large-scale neuronal network dynamics and computation. Here, we report quantitative differences between excitatory and inhibitory cells, as they relay input correlations into output correlations. We explain this heterogeneity by simple biophysical models and provide the most experimentally validated test of a theory for the emergence of correlations.

Stationary-State Statistics of a Binary Neural Network Model with Quenched Disorder

In this paper, we study the statistical properties of the stationary firing-rate states of a neural network model with quenched disorder. The model has arbitrary size, discrete-time evolution equations and binary firing rates, while the topology and the strength of the synaptic connections are randomly generated from known, generally arbitrary, probability distributions. We derived semi-analytical expressions of the occurrence probability of the stationary states and the mean multistability diagram of the model, in terms of the distribution of the synaptic connections and of the external stimuli to the network. Our calculations rely on the probability distribution of the bifurcation points of the stationary states with respect to the external stimuli, calculated in terms of the permanent of special matrices using extreme value theory. While our semi-analytical expressions are exact for any size of the network and for any distribution of the synaptic connections, we focus our study on networks made of several populations, that we term "statistically homogeneous" to indicate that the probability distribution of their connections depends only on the pre- and post-synaptic population indexes, and not on the individual synaptic pair indexes. In this specific case, we calculated analytically the permanent, obtaining a compact formula that outperforms of several orders of magnitude the Balasubramanian-Bax-Franklin-Glynn algorithm. To conclude, by applying the Fisher-Tippett-Gnedenko theorem, we derived asymptotic expressions of the stationary-state statistics of multi-population networks in the large-network-size limit, in terms of the Gumbel (double exponential) distribution. We also provide a Python implementation of our formulas and some examples of the results generated by the code.

Detecting single-cell stimulation in a large network of integrate-and-fire neurons

Several experiments have shown that the stimulation of a single neuron in the cortex can influence the local network activity and even the behavior of an animal. From the theoretical point of view, it is not clear how stimulating a single cell in a cortical network can evoke a statistically significant change in the activity of a large population. Our previous study considered a random network of integrate-and-fire neurons and proposed a way of detecting the stimulation of a single neuron in the activity of a local network: a threshold detector biased toward a specific subset of neurons. Here, we revisit this model and extend it by introducing a second network acting as a readout. In the simplest scenario, the readout consists of a collection of integrate-and-fire neurons with no recurrent connections. In this case, the ability to detect the stimulus does not improve. However, a readout network with both feed-forward and local recurrent inhibition permits detection with a very small bias, if compared to the readout scheme introduced previously. The crucial role of inhibition is to reduce global input cross correlations, the main factor limiting detectability. Finally, we show that this result is robust if recurrent excitatory connections are included or if a different kind of readout bias (in the synaptic amplitudes instead of connection probability) is used.

Major Feedforward Thalamic Input Into Layer 4C of Primary Visual Cortex in Primate

One of the underlying principles of how mammalian circuits are constructed is the relative influence of feedforward to recurrent synaptic drive. It has been dogma in sensory systems that the thalamic feedforward input is relatively weak and that there is a large amplification of the input signal by recurrent feedback. Here we show that in trichromatic primates there is a major feedforward input to layer 4C of primary visual cortex. Using a combination of 3D-electron-microscopy and 3D-confocal imaging of thalamic boutons we found that the average feedforward contribution was about 20% of the total excitatory input in the parvocellular (P) pathway, about 3 times the currently accepted values for primates. In the magnocellular (M) pathway it was around 15%, nearly twice the currently accepted values. New methods showed the total synaptic and cell densities were as much as 150% of currently accepted values. The new estimates of contributions of feedforward synaptic inputs into visual cortex call for a major revision of the design of the canonical cortical circuit.

Cell Densities in the Mouse Brain: A Systematic Review

The mouse brain is the most extensively studied brain of all species. We performed an exhaustive review of the literature to establish our current state of knowledge on cell numbers in mouse brain regions, arguably the most fundamental property to measure when attempting to understand a brain. The synthesized information, collected in one place, can be used by both theorists and experimentalists. Although for commonly-studied regions cell densities could be obtained for principal cell types, overall we know very little about how many cells are present in most brain regions and even less about cell-type specific densities. There is also substantial variation in cell density values obtained from different sources. This suggests that we need a new approach to obtain cell density datasets for the mouse brain.

Neocortical Microdissection at Columnar and Laminar Resolution for Molecular Interrogation

The heterogeneous organization of the mammalian neocortex poses a challenge for elucidating the molecular mechanisms underlying its physiological processes. Although high-throughput molecular methods are increasingly deployed in neuroscience, their anatomical specificity is often lacking. In this unit, we introduce a targeted microdissection technique that enables extraction of high-quality RNA and proteins at high anatomical resolution from acutely prepared brain slices. We exemplify its utility by isolating single cortical columns and laminae from the mouse primary somatosensory (barrel) cortex. Tissues can be isolated from living slices in minutes, and the extracted RNA and protein are of sufficient quantity and quality to be used for RNA sequencing and mass spectrometry. This technique will help to increase the anatomical specificity of molecular studies of the neocortex, and the brain in general, as it is applicable to any brain structure that can be identified using optical landmarks in living slices. © 2018 by John Wiley & Sons, Inc.

Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models

During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits. While computational models of cortical circuits have included different combinations of excitability, balance, and stability, they have done so without a systematic quantitative comparison with experimental data. Our study provides quantitative criteria for this purpose, by analyzing in-vitro and in-vivo neuronal activity and characterizing the dynamics on the neuronal and population levels. The criteria are defined with a tolerance that allows for differences between experiments, yet are sufficient to capture commonalities between persistently depolarized cortical network states and to help validate computational models of cortex. As test cases for the derived set of criteria, we analyze three widely used models of cortical circuits and find that each model possesses some of the experimentally observed features, but none satisfies all criteria simultaneously, showing that the criteria are able to identify weak spots in computational models. The criteria described here form a starting point for the systematic validation of cortical neuronal network models, which will help improve the reliability of future models, and render them better building blocks for larger models of the brain.

Pharmacodynamics of the Glutamate Receptor Antagonists in the Rat Barrel Cortex

Epipial application is one of the approaches for drug delivery into the cortex. However, passive diffusion of epipially applied drugs through the cortical depth may be slow, and different drug concentrations may be achieved at different rates across the cortical depth. Here, we explored the pharmacodynamics of the inhibitory effects of epipially applied ionotropic glutamate receptor antagonists CNQX and dAPV on sensory-evoked and spontaneous activity across layers of the cortical barrel column in urethane-anesthetized rats. The inhibitory effects of CNQX and dAPV were observed at concentrations that were an order higher than in slices in vitro, and they slowly developed from the cortical surface to depth after epipial application. The level of the inhibitory effects also followed the surface-to-depth gradient, with full inhibition of sensory evoked potentials (SEPs) in the supragranular layers and L4 and only partial inhibition in L5 and L6. During epipial CNQX and dAPV application, spontaneous activity and the late component of multiple unit activity (MUA) during sensory-evoked responses were suppressed faster than the short-latency MUA component. Despite complete suppression of SEPs in L4, sensory-evoked short-latency multiunit responses in L4 persisted, and they were suppressed by further addition of lidocaine suggesting that spikes in thalamocortical axons contribute ∼20% to early multiunit responses. Epipial CNQX and dAPV also completely suppressed sensory-evoked very fast (∼500 Hz) oscillations and spontaneous slow wave activity in L2/3 and L4. However, delta oscillations persisted in L5/6. Thus, CNQX and dAPV exert inhibitory actions on cortical activity during epipial application at much higher concentrations than in vitro, and the pharmacodynamics of their inhibitory effects is characterized by the surface-to-depth gradients in the rate of development and the level of inhibition of sensory-evoked and spontaneous cortical activity.

Comprehensive Morpho-Electrotonic Analysis Shows 2 Distinct Classes of L2 and L3 Pyramidal Neurons in Human Temporal Cortex

There have been few quantitative characterizations of the morphological, biophysical, and cable properties of neurons in the human neocortex. We employed feature-based statistical methods on a rare data set of 60 3D reconstructed pyramidal neurons from L2 and L3 in the human temporal cortex (HL2/L3 PCs) removed after brain surgery. Of these cells, 25 neurons were also characterized physiologically. Thirty-two morphological features were analyzed (e.g., dendritic surface area, 36 333 ± 18 157 μm²; number of basal trees, 5.55 ± 1.47; dendritic diameter, 0.76 ± 0.28 μm). Eighteen features showed a significant gradual increase with depth from the pia (e.g., dendritic length and soma radius). The other features showed weak or no correlation with depth (e.g., dendritic diameter). The basal dendritic terminals in HL2/L3 PCs are particularly elongated, enabling multiple nonlinear processing units in these dendrites. Unlike the morphological features, the active biophysical features (e.g., spike shapes and rates) and passive/cable features (e.g., somatic input resistance, 47.68 ± 15.26 MΩ, membrane time constant, 12.03 ± 1.79 ms, average dendritic cable length, 0.99 ± 0.24) were depth-independent. A novel descriptor for apical dendritic topology yielded 2 distinct classes, termed hereby as “slim-tufted” and “profuse-tufted” HL2/L3 PCs; the latter class tends to fire at higher rates. Thus, our morpho-electrotonic analysis shows 2 distinct classes of HL2/L3 PCs.

Quantifying the three-dimensional facial morphology of the laboratory rat with a focus on the vibrissae

The morphology of an animal's face will have large effects on the sensory information it can acquire. Here we quantify the arrangement of cranial sensory structures of the rat, with special emphasis on the mystacial vibrissae (whiskers). Nearly all mammals have vibrissae, which are generally arranged in rows and columns across the face. The vibrissae serve a wide variety of important behavioral functions, including navigation, climbing, wake following, anemotaxis, and social interactions. To date, however, there are few studies that compare the morphology of vibrissal arrays across species, or that describe the arrangement of the vibrissae relative to other facial sensory structures. The few studies that do exist have exploited the whiskers' grid-like arrangement to quantify array morphology in terms of row and column identity. However, relying on whisker identity poses a challenge for comparative research because different species have different numbers and arrangements of whiskers. The present work introduces an approach to quantify vibrissal array morphology regardless of the number of rows and columns, and to quantify the array's location relative to other sensory structures. We use the three-dimensional locations of the whisker basepoints as fundamental parameters to generate equations describing the length, curvature, and orientation of each whisker. Results show that in the rat, whisker length varies exponentially across the array, and that a hard limit on intrinsic curvature constrains the whisker height-to-length ratio. Whiskers are oriented to "fan out" approximately equally in dorsal-ventral and rostral-caudal directions. Quantifying positions of the other sensory structures relative to the whisker basepoints shows remarkable alignment to the somatosensory cortical homunculus, an alignment that would not occur for other choices of coordinate systems (e.g., centered on the midpoint of the eyes). We anticipate that the quantification of facial sensory structures, including the vibrissae, will ultimately enable cross-species comparisons of multi-modal sensing volumes.

Neural crest stem cells protect spinal cord neurons from excitotoxic damage and inhibit glial activation by secretion of brain-derived neurotrophic factor

The acute phase of spinal cord injury is characterized by excitotoxic and inflammatory events that mediate extensive neuronal loss in the gray matter. Neural crest stem cells (NCSCs) can exert neuroprotective and anti-inflammatory effects that may be mediated by soluble factors. We therefore hypothesize that transplantation of NCSCs to acutely injured spinal cord slice cultures (SCSCs) can prevent neuronal loss after excitotoxic injury. NCSCs were applied onto SCSCs previously subjected to N-methyl-d-aspartate (NMDA)-induced injury. Immunohistochemistry and TUNEL staining were used to quantitatively study cell populations and apoptosis. Concentrations of neurotrophic factors were measured by ELISA. Migration and differentiation properties of NCSCs on SCSCs, laminin, or hyaluronic acid hydrogel were separately studied. NCSCs counteracted the loss of NeuN-positive neurons that was otherwise observed after NMDA-induced excitotoxicity, partly by inhibiting neuronal apoptosis. They also reduced activation of both microglial cells and astrocytes. The concentration of brain-derived neurotrophic factor (BDNF) was increased in supernatants from SCSCs cultured with NCSCs compared to SCSCs alone and BDNF alone mimicked the effects of NCSC application on SCSCs. NCSCs migrated superficially across the surface of SCSCs and showed no signs of neuronal or glial differentiation but preserved their expression of SOX2 and Krox20. In conclusion, NCSCs exert neuroprotective, anti-apoptotic and glia-inhibitory effects on excitotoxically injured spinal cord tissue, some of these effects mediated by secretion of BDNF. However, the investigated NCSCs seem not to undergo neuronal or glial differentiation in the short term since markers indicative of an undifferentiated state were expressed during the entire observation period.

Studies of cortical connectivity using optical circuit mapping methods

An important consideration when probing the function of any neuron is to uncover the source of synaptic input onto the cell, its intrinsic physiology and efferent targets. Over the years, electrophysiological approaches have generated considerable insight into these properties in a variety of cortical neuronal subtypes and circuits. However, as researchers explore neuronal function in greater detail, they are increasingly turning to optical techniques to bridge the gap between local network interactions and behaviour. The application of optical methods has increased dramatically over the past decade, spurred on by the optogenetic revolution. In this review, we provide an account of recent innovations, providing researchers with a primer detailing circuit mapping strategies in the cerebral cortex. We will focus on technical aspects of performing neurotransmitter uncaging and channelrhodopsin-assisted circuit mapping, with the aim of identifying common pitfalls that can negatively influence the collection of reliable data.

Interneuron Cooperativity in Cortical Circuits

Neocortical neurons tend to be coactive in groups called ensembles. However, sometimes, individual neurons also spike alone, independent of the ensemble. What processes regulate the transition between individual and cooperative action? Inspired by classical work in biochemistry, we apply the concept of neuronal cooperativity to explore this question. With a focus on neocortical inhibitory interneurons, we offer a working definition of neuronal cooperativity, review its recorded incidences and proposed mechanisms, and describe experimental approaches that will demonstrate and further describe this action. We suggest that cooperativity of "neuron teams" is manifested in vivo through their coactivity, as well as via the action of individual "soloist neurons" in the low end of the sigmoidal cooperativity curve. Finally, we explore the evidence for and implications of individual and team action of neurons.

Preferential Initiation and Spread of Anoxic Depolarization in Layer 4 of Rat Barrel Cortex

Anoxic depolarization (AD) is a hallmark of ischemic brain damage. AD is associated with a spreading wave of neuronal depolarization and an increase in light transmittance. However, initiation and spread of AD across the layers of the somatosensory cortex, which is one of the most frequently affected brain regions in ischemic stroke, remains largely unknown. Here, we explored the initiation and propagation of AD in slices of the rat barrel cortex using extracellular local field potential (LFP) recordings and optical intrinsic signal (OIS) recordings. We found that ischemia-like conditions induced by oxygen-glucose deprivation (OGD) evoked AD, which manifested as a large negative LFP shift and an increase in light transmittance. AD typically initiated in one or more barrels and further spread across the entire slice with a preferential propagation through L4. Elevated extracellular potassium concentration accelerated the AD onset without affecting proneness of L4 to AD. In live slices, barrels were most heavily labeled by the metabolic level marker 2,3,5-triphenyltetrazolium chloride, suggesting that the highest metabolic demand is in L4 when compared to the other layers. Thus, L4 is the layer of the barrel cortex most prone to AD, which may be due to the highest metabolic demand and cell density in this layer.

Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency-specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings.

Section

Analysis methods.

Classifications

Source localization: inverse problem; Source localization: other.

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Laminar inferences can be made with MEG using both local and global fit metrics.

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Source inversion algorithms with sparsity constraints performed best.

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Classification is affected by patch size estimates, anatomy, and lead field strength.

Cell Type-Specific Structural Organization of the Six Layers in Rat Barrel Cortex

The cytoarchitectonic subdivision of the neocortex into six layers is often used to describe the organization of the cortical circuitry, sensory-evoked signal flow or cortical functions. However, each layer comprises neuronal cell types that have different genetic, functional and/or structural properties. Here, we reanalyze structural data from some of our recent work in the posterior-medial barrel-subfield of the vibrissal part of rat primary somatosensory cortex (vS1). We quantify the degree to which somata, dendrites and axons of the 10 major excitatory cell types of the cortex are distributed with respect to the cytoarchitectonic organization of vS1. We show that within each layer, somata of multiple cell types intermingle, but that each cell type displays dendrite and axon distributions that are aligned to specific cytoarchitectonic landmarks. The resultant quantification of the structural composition of each layer in terms of the cell type-specific number of somata, dendritic and axonal path lengths will aid future studies to bridge between layer- and cell type-specific analyses.

Multiple Transmitter Receptors in Regions and Layers of the Human Cerebral Cortex

We measured the densities (fmol/mg protein) of 15 different receptors of various transmitter systems in the supragranular, granular and infragranular strata of 44 areas of visual, somatosensory, auditory and multimodal association systems of the human cerebral cortex. Receptor densities were obtained after labeling of the receptors using quantitative in vitro receptor autoradiography in human postmortem brains. The mean density of each receptor type over all cortical layers and of each of the three major strata varies between cortical regions. In a single cortical area, the multi-receptor fingerprints of its strata (i.e., polar plots, each visualizing the densities of multiple different receptor types in supragranular, granular or infragranular layers of the same cortical area) differ in shape and size indicating regional and laminar specific balances between the receptors. Furthermore, the three strata are clearly segregated into well definable clusters by their receptor fingerprints. Fingerprints of different cortical areas systematically vary between functional networks, and with the hierarchical levels within sensory systems. Primary sensory areas are clearly separated from all other cortical areas particularly by their very high muscarinic M₂ and nicotinic α₄β₂ receptor densities, and to a lesser degree also by noradrenergic α₂ and serotonergic 5-HT₂ receptors. Early visual areas of the dorsal and ventral streams are segregated by their multi-receptor fingerprints. The results are discussed on the background of functional segregation, cortical hierarchies, microstructural types, and the horizontal (layers) and vertical (columns) organization in the cerebral cortex. We conclude that a cortical column is composed of segments, which can be assigned to the cortical strata. The segments differ by their patterns of multi-receptor balances, indicating different layer-specific signal processing mechanisms. Additionally, the differences between the strata-and area-specific fingerprints of the 44 areas reflect the segregation of the cerebral cortex into functionally and topographically definable groups of cortical areas (visual, auditory, somatosensory, limbic, motor), and reveals their hierarchical position (primary and unimodal (early) sensory to higher sensory and finally to multimodal association areas).

Highlights

Densities of transmitter receptors vary between areas of human cerebral cortex.

Multi-receptor fingerprints segregate cortical layers.

The densities of all examined receptor types together reach highest values in the supragranular stratum of all areas.

The lowest values are found in the infragranular stratum.

Multi-receptor fingerprints of entire areas and their layers segregate functional systems

Cortical types (primary sensory, motor, multimodal association) differ in their receptor fingerprints.

Long-Term Deficits in Cortical Circuit Function after Asphyxial Cardiac Arrest and Resuscitation in Developing Rats

Cardiac arrest is a common cause of global hypoxic-ischemic brain injury. Poor neurologic outcome among cardiac arrest survivors results not only from direct cellular injury but also from subsequent long-term dysfunction of neuronal circuits. Here, we investigated the long-term impact of cardiac arrest during development on the function of cortical layer IV (L4) barrel circuits in the rat primary somatosensory cortex. We used multielectrode single-neuron recordings to examine responses of presumed excitatory L4 barrel neurons to controlled whisker stimuli in adult (8 ± 2-mo-old) rats that had undergone 9 min of asphyxial cardiac arrest and resuscitation during the third postnatal week. Results indicate that responses to deflections of the topographically appropriate principal whisker (PW) are smaller in magnitude in cardiac arrest survivors than in control rats. Responses to adjacent whisker (AW) deflections are similar in magnitude between the two groups. Because of a disproportionate decrease in PW-evoked responses, receptive fields of L4 barrel neurons are less spatially focused in cardiac arrest survivors than in control rats. In addition, spiking activity among L4 barrel neurons is more correlated in cardiac arrest survivors than in controls. Computational modeling demonstrates that experimentally observed disruptions in barrel circuit function after cardiac arrest can emerge from a balanced increase in background excitatory and inhibitory conductances in L4 neurons. Experimental and modeling data together suggest that after a hypoxic-ischemic insult, cortical sensory circuits are less responsive and less spatially tuned. Modulation of these deficits may represent a therapeutic approach to improving neurologic outcome after cardiac arrest.

Focal Local Field Potential Signature of the Single-Axon Monosynaptic Thalamocortical Connection

A resurgence has taken place in recent years in the use of the extracellularly recorded local field potential (LFP) to investigate neural network activity. To probe monosynaptic thalamic activation of cortical postsynaptic target cells, so called spike-trigger-averaged LFP (stLFP) signatures have been measured. In these experiments, the cortical LFP is measured by multielectrodes covering several cortical lamina and averaged on spontaneous spikes of thalamocortical (TC) cells. Using a well established forward-modeling scheme, we investigated the biophysical origin of this stLFP signature with simultaneous synaptic activation of cortical layer-4 neurons, mimicking the effect of a single afferent spike from a single TC neuron. Constrained by previously measured intracellular responses of the main postsynaptic target cell types and with biologically plausible assumptions regarding the spatial distribution of thalamic synaptic inputs into layer 4, the model predicted characteristic contributions to monosynaptic stLFP signatures both for the regular-spiking (RS) excitatory neurons and the fast-spiking (FS) inhibitory interneurons. In particular, the FS cells generated stLFP signatures of shorter temporal duration than the RS cells. Added together, a sum of the stLFP signatures of these two principal synaptic targets of TC cells were observed to resemble experimentally measured stLFP signatures. Outside the volume targeted by TC afferents, the resulting postsynaptic LFP signals were found to be sharply attenuated. This implies that such stLFP signatures provide a very local measure of TC synaptic activation, and that newly developed inverse current-source density (CSD)-estimation methods are needed for precise assessment of the underlying spatiotemporal CSD profiles.SIGNIFICANCE STATEMENT Despite its long history and prevalent use, the proper interpretation of the extracellularly recorded local field potential (LFP) is still not fully established. Here we investigate by biophysical modeling the origin of the focal LFP signature of the single-axon monosynaptic thalamocortical connection as measured by spike-trigger-averaging of cortical LFPs on spontaneous spikes of thalamocortical neurons. We find that this LFP signature is well accounted for by a model assuming thalamic projections to two cortical layer-4 cell populations: one excitatory (putatively regular-spiking cells) and one inhibitory (putatively fast-spiking cells). The LFP signature is observed to decay sharply outside the cortical region receiving the thalamocortical projection, implying that it indeed provides a very local measure of thalamocortical synaptic activation.

DNA Methylation program in normal and alcohol-induced thinning cortex

While cerebral underdevelopment is a hallmark of fetal alcohol spectrum disorders (FASD), the mechanism(s) guiding the broad cortical neurodevelopmental deficits are not clear. DNA methylation is known to regulate early development and tissue specification through gene regulation. Here, we examined DNA methylation in the onset of alcohol-induced cortical thinning in a mouse model of FASD. C57BL/6 (B6) mice were administered a 4% alcohol (v/v) liquid diet from embryonic (E) days 7-16, and their embryos were harvested at E17, along with isocaloric liquid diet and lab chow controls. Cortical neuroanatomy, neural phenotypes, and epigenetic markers of methylation were assessed using immunohistochemistry, Western blot, and methyl-DNA assays. We report that cortical thickness, neuroepithelial proliferation, and neuronal migration and maturity were found to be deterred by alcohol at E17. Simultaneously, DNA methylation, including 5-methylcytosine (5mC) and 5-hydroxcylmethylcytosine (5hmC), which progresses as an intrinsic program guiding normal embryonic cortical development, was severely affected by in utero alcohol exposure. The intricate relationship between cortical thinning and this DNA methylation program disruption is detailed and illustrated. DNA methylation, dynamic across the multiple cortical layers during the late embryonic stage, is highly disrupted by fetal alcohol exposure; this disruption occurs in tandem with characteristic developmental abnormalities, ranging from structural to molecular. Finally, our findings point to a significant question for future exploration: whether epigenetics guides neurodevelopment or whether developmental conditions dictate epigenetic dynamics in the context of alcohol-induced cortical teratogenesis.