Cortical cell assemblies and their underlying connectivity: An in silico study.

Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb's postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs.

A parcellation scheme of mouse isocortex based on reversals in connectivity gradients.

The brain is composed of several anatomically clearly separated structures. This parcellation is often extended into the isocortex, based on anatomical, physiological, or functional differences. Here, we derive a parcellation scheme based purely on the spatial structure of long-range synaptic connections within the cortex. To that end, we analyzed a publicly available dataset of average mouse brain connectivity, and split the isocortex into disjunct regions. Instead of clustering connectivity based on modularity, our scheme is inspired by methods that split sensory cortices into subregions where gradients of neuronal response properties, such as the location of the receptive field, reverse. We calculated comparable gradients from voxelized brain connectivity data and automatically detected reversals in them. This approach better respects the known presence of functional gradients within brain regions than clustering-based approaches. Placing borders at the reversals resulted in a parcellation into 41 subregions that differs significantly from an established scheme in nonrandom ways, but is comparable in terms of the modularity of connectivity between regions. It reveals unexpected trends of connectivity, such as a tripartite split of somatomotor regions along an anterior to posterior gradient. The method can be readily adapted to other organisms and data sources, such as human functional connectivity.

Immunometabolic capacities of nutritional fatty acids in regulation of inflammatory bone cell interaction and systemic impact of periodontal infection.

Introduction: Novel preventive strategies in periodontal disease target the bacterial-induced inflammatory host response to reduce associated tissue destruction. Strategies focus on the modulation of tissue-destroying inflammatory host response, particularly the reduction of inflammation and promotion of resolution. Thereby, nutrition is a potent immunometabolic non-pharmacological intervention. Human studies have demonstrated the benefit of olive oil-containing Mediterranean-style diets (MDs), the main component of which being mono-unsaturated fatty acid (FA) oleic acid (OA (C18:1)). Hence, nutritional OA strengthened the microarchitecture of alveolar trabecular bone and increased circulating pro-resolving lipid mediators following bacterial inoculation with periodontal pathogen Porphyromonas gingivalis, contrary to saturated FA palmitic acid (PA (C16:0)), which is abundant in Western-style diets. Additionally, the generalized distribution of inflammatory pathway mediators can occur in response to bacterial infection and compromise systemic tissue metabolism and bone homeostasis distant from the side of infection. Whether specific FA-enriched nutrition and periodontal inoculation are factors in systemic pathology that can be immune-modulatory targeted through dietary substitution is unknown and of clinical relevance. Methods: Normal-weight C57BL/6-mice received OA-or PA-enriched diets (PA-ED, OA-ED, PA/OA-ED) or a normal-standard diet (n=12/group) for 16 weeks and were orally infected with P. gingivalis/placebo to induce periodontal disease. Using histomorphometry and LC-MS/MS, systemic bone morphology, incorporated immunometabolic FA-species, serological markers of bone metabolism, and stress response were determined in addition to bone cell inflammation and interaction in vitro. Results: In contrast to OA-ED, PA-ED reduced systemic bone microarchitecture paralleled by increased lipotoxic PA-containing metabolite accumulation in bone. Substitution with OA reversed the bone-destructive impact of PA, which was accompanied by reduced diacylglycerols (DAG) and saturated ceramide levels. Further, PA-associated reduction in mineralization activity and concomitant pro-inflammatory activation of primary osteoblasts were diminished in cultures where PA was replaced with OA, which impacted cellular interaction with osteoclasts. Additionally, PA-ED increased osteoclast numbers in femurs in response to oral P. gingivalis infection, whereas OA-ED reduced osteoclast occurrence, which was paralleled by serologically increased levels of the stress-reducing lipokine PI(18:1/18:1). Conclusion: OA substitution reverses the bone-destructive and pro-inflammatory effects of PA and eliminates incorporated lipotoxic PA metabolites. This supports Mediterranean-style OA-based diets as a preventive intervention to target the accumulation of PA-associated lipotoxic metabolites and thereby supports systemic bone tissue resilience after oral bacterial P. gingivalis infection.

Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep.

Thalamoreticular circuitry plays a key role in arousal, attention, cognition, and sleep spindles, and is linked to several brain disorders. A detailed computational model of mouse somatosensory thalamus and thalamic reticular nucleus has been developed to capture the properties of over 14,000 neurons connected by 6 million synapses. The model recreates the biological connectivity of these neurons, and simulations of the model reproduce multiple experimental findings in different brain states. The model shows that inhibitory rebound produces frequency-selective enhancement of thalamic responses during wakefulness. We find that thalamic interactions are responsible for the characteristic waxing and waning of spindle oscillations. In addition, we find that changes in thalamic excitability control spindle frequency and their incidence. The model is made openly available to provide a new tool for studying the function and dysfunction of the thalamoreticular circuitry in various brain states.

Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons.

Knowledge of the cell-type-specific composition of the brain is useful in order to understand the role of each cell type as part of the network. Here, we estimated the composition of the whole cortex in terms of well characterized morphological and electrophysiological inhibitory neuron types (me-types). We derived probabilistic me-type densities from an existing atlas of molecularly defined cell-type densities in the mouse cortex. We used a well-established me-type classification from rat somatosensory cortex to populate the cortex. These me-types were well characterized morphologically and electrophysiologically but they lacked molecular marker identity labels. To extrapolate this missing information, we employed an additional dataset from the Allen Institute for Brain Science containing molecular identity as well as morphological and electrophysiological data for mouse cortical neurons. We first built a latent space based on a number of comparable morphological and electrical features common to both data sources. We then identified 19 morpho-electrical clusters that merged neurons from both datasets while being molecularly homogeneous. The resulting clusters best mirror the molecular identity classification solely using available morpho-electrical features. Finally, we stochastically assigned a molecular identity to a me-type neuron based on the latent space cluster it was assigned to. The resulting mapping was used to derive inhibitory me-types densities in the cortex.

A method to estimate the cellular composition of the mouse brain from heterogeneous datasets.

The mouse brain contains a rich diversity of inhibitory neuron types that have been characterized by their patterns of gene expression. However, it is still unclear how these cell types are distributed across the mouse brain. We developed a computational method to estimate the densities of different inhibitory neuron types across the mouse brain. Our method allows the unbiased integration of diverse and disparate datasets into one framework to predict inhibitory neuron densities for uncharted brain regions. We constrained our estimates based on previously computed brain-wide neuron densities, gene expression data from in situ hybridization image stacks together with a wide range of values reported in the literature. Using constrained optimization, we derived coherent estimates of cell densities for the different inhibitory neuron types. We estimate that 20.3% of all neurons in the mouse brain are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. We find that our density estimations improve as more literature values are integrated. Our pipeline is extensible, allowing new cell types or data to be integrated as they become available. The data, algorithms, software, and results of our pipeline are publicly available and update the Blue Brain Cell Atlas. This work therefore leverages the research community to collectively converge on the numbers of each cell type in each brain region.

Palmitate-Triggered COX2/PGE2-Related Hyperinflammation in Dual-Stressed PdL Fibroblasts Is Mediated by Repressive H3K27 Trimethylation.

The interrelationships between periodontal disease, obesity-related hyperlipidemia and mechanical forces and their modulating effects on the epigenetic profile of periodontal ligament (PdL) cells are assumed to be remarkably complex. The PdL serves as a connective tissue between teeth and alveolar bone and is involved in pathogen defense and the inflammatory responses to mechanical stimuli occurring during tooth movement. Altered inflammatory signaling could promote root resorption and tooth loss. Hyperinflammatory COX2/PGE2 signaling was reported for human PdL fibroblasts (HPdLFs) concomitantly stressed with Porphyromonas gingivalis lipopolysaccharides and compressive force after exposure to palmitic acid (PA). The aim of this study was to investigate the extent to which this was modulated by global and gene-specific changes in histone modifications. The expression of key epigenetic players and global H3Kac and H3K27me3 levels were quantitatively evaluated in dual-stressed HPdLFs exposed to PA, revealing a minor force-related reduction in repressive H3K27me3. UNC1999-induced H3K27me3 inhibition reversed the hyperinflammatory responses of dual-stressed PA cultures characterized by increased COX2 expression, PGE2 secretion and THP1 adhesion. The reduced expression of the gene encoding the anti-inflammatory cytokine IL-10 and the increased presence of H3K27me3 at its promoter-associated sites were reversed by inhibitor treatment. Thus, the data highlight an important epigenetic interplay between the different stimuli to which the PdL is exposed.

Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies.

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

Perioperative Outcomes of Transurethral Resection, Open Prostatectomy, and Laser Therapy in the Surgical Treatment of Benign Prostatic Obstruction: A "Real-World" Data Analysis from the URO-Cert Prostate Centers.

INTRODUCTION: The aim of the study is to compare length of hospital stay, transfusion rates, and re-intervention rates during hospitalization for transurethral resection of the prostate (TUR-P), open prostatectomy (OP), and laser therapy (LT) for surgical treatment of benign prostatic obstruction (BPO). METHODS: URO-Cert is an organization, in which clinical data of prostatic diseases from 2 university, 19 public, and 3 private hospitals and 270 office-based urologists are collected in order to document treatment quality. Data on diagnostics, therapy, and course of disease are recorded web based. The analysis includes datasets from 2005 to 2017. RESULTS: Of 10,420 patients, 8,389 were treated with TUR-P, 1,334 with OP, and 697 with LT. Median length of hospital stay was 6 days (IQR: 4-7) for TUR-P, 9 days (IQR: 7-11) for OP, and 5 days (IQR: 4-6) for LT (p < 0.001). Risk for a hospital stay ≥7 days was higher for OP versus TUR-P (OR: 7.25; 95% CI = 6.27-8.36; p < 0.001) and LT (OR: 17.89; 95% CI = 14.12-22.65; p < 0.001) and higher for TUR-P versus LT (OR: 2.47; 95% CI = 2.03-3.01; p < 0.001). OP had a significantly higher risk for transfusions than TUR-P (OR: 2.44; 95% CI = 1.74-3.41; p < 0.001) and LT (OR: 3.32; 95% CI = 1.56-7.01; p < 0.001). Transfusion rates were not significantly different between TUR-P and LT (OR: 1.36; 95% CI = 0.66-2.79; p = 0.51). Risk of re-intervention was not different between all 3 approaches. CONCLUSION: OP was associated with higher transfusion rates and longer hospital stay than TUR-P and LT. Risk of transfusion was not different between TUR-P and LT, but TUR-P was inferior to LT concerning length of hospital stay. Re-intervention rates during hospitalization did not differ between the groups.

In silico voltage-sensitive dye imaging reveals the emergent dynamics of cortical populations.

Voltage-sensitive dye imaging (VSDI) is a powerful technique for interrogating membrane potential dynamics in assemblies of cortical neurons, but with effective resolution limits that confound interpretation. To address this limitation, we developed an in silico model of VSDI in a biologically faithful digital reconstruction of rodent neocortical microcircuitry. Using this model, we extend previous experimental observations regarding the cellular origins of VSDI, finding that the signal is driven primarily by neurons in layers 2/3 and 5, and that VSDI measurements do not capture individual spikes. Furthermore, we test the capacity of VSD image sequences to discriminate between afferent thalamic inputs at various spatial locations to estimate a lower bound on the functional resolution of VSDI. Our approach underscores the power of a bottom-up computational approach for relating scales of cortical processing.

Data-driven integration of hippocampal CA1 synaptic physiology in silico.

The anatomy and physiology of monosynaptic connections in rodent hippocampal CA1 have been extensively studied in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data-driven approach to integrate the current state-of-the-art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo-dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short-term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource toward a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.

Impact of higher order network structure on emergent cortical activity.

Synaptic connectivity between neocortical neurons is highly structured. The network structure of synaptic connectivity includes first-order properties that can be described by pairwise statistics, such as strengths of connections between different neuron types and distance-dependent connectivity, and higher order properties, such as an abundance of cliques of all-to-all connected neurons. The relative impact of first- and higher order structure on emergent cortical network activity is unknown. Here, we compare network structure and emergent activity in two neocortical microcircuit models with different synaptic connectivity. Both models have a similar first-order structure, but only one model includes higher order structure arising from morphological diversity within neuronal types. We find that such morphological diversity leads to more heterogeneous degree distributions, increases the number of cliques, and contributes to a small-world topology. The increase in higher order network structure is accompanied by more nuanced changes in neuronal firing patterns, such as an increased dependence of pairwise correlations on the positions of neurons in cliques. Our study shows that circuit models with very similar first-order structure of synaptic connectivity can have a drastically different higher order network structure, and suggests that the higher order structure imposed by morphological diversity within neuronal types has an impact on emergent cortical activity.

Cortical reliability amid noise and chaos.

Typical responses of cortical neurons to identical sensory stimuli appear highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potential role of spike-time coding is directly limited by the internally generated variability of cortical circuits, which remains largely unexplored. Here, we quantify this internally generated variability using a biophysical model of rat neocortical microcircuitry with biologically realistic noise sources. We find that stochastic neurotransmitter release is a critical component of internally generated variability, causing rapidly diverging, chaotic recurrent network dynamics. Surprisingly, the same nonlinear recurrent network dynamics can transiently overcome the chaos in response to weak feed-forward thalamocortical inputs, and support reliable spike times with millisecond precision. Our model shows that the noisy and chaotic network dynamics of recurrent cortical microcircuitry are compatible with stimulus-evoked, millisecond spike-time reliability, resolving a long-standing debate.

A null model of the mouse whole-neocortex micro-connectome.

In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-scale connectomics, studying connectivity between individual neurons. We combine these two complementary views of connectomics to build a first draft statistical model of the micro-connectome of a whole mouse neocortex based on available data on region-to-region connectivity and individual whole-brain axon reconstructions. This process reveals a targeting principle that allows us to predict the innervation logic of individual axons from meso-scale data. The resulting connectome recreates biological trends of targeting on all scales and predicts that an established principle of scale invariant topological organization of connectivity can be extended down to the level of individual neurons. It can serve as a powerful null model and as a substrate for whole-brain simulations.

Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity.

Synaptic connectivity between neurons is naturally constrained by the anatomical overlap of neuronal arbors, the space on the axon available for synapses, and by physiological mechanisms that form synapses at a subset of potential synapse locations. What is not known is how these constraints impact emergent connectivity in a circuit with diverse morphologies. We investigated the role of morphological diversity within and across neuronal types on emergent connectivity in a model of neocortical microcircuitry. We found that the average overlap between the dendritic and axonal arbors of different types of neurons determines neuron-type specific patterns of distance-dependent connectivity, severely constraining the space of possible connectomes. However, higher order connectivity motifs depend on the diverse branching patterns of individual arbors of neurons belonging to the same type. Morphological diversity across neuronal types, therefore, imposes a specific structure on first order connectivity, and morphological diversity within neuronal types imposes a higher order structure of connectivity. We estimate that the morphological constraints resulting from diversity within and across neuron types together lead to a 10-fold reduction of the entropy of possible connectivity configurations, revealing an upper bound on the space explored by structural plasticity.

Rich cell-type-specific network topology in neocortical microcircuitry.

Uncovering structural regularities and architectural topologies of cortical circuitry is vital for understanding neural computations. Recently, an experimentally constrained algorithm generated a dense network reconstruction of a ∼0.3-mm3 volume from juvenile rat somatosensory neocortex, comprising ∼31,000 cells and ∼36 million synapses. Using this reconstruction, we found a small-world topology with an average of 2.5 synapses separating any two cells and multiple cell-type-specific wiring features. Amounts of excitatory and inhibitory innervations varied across cells, yet pyramidal neurons maintained relatively constant excitation/inhibition ratios. The circuit contained highly connected hub neurons belonging to a small subset of cell types and forming an interconnected cell-type-specific rich club. Certain three-neuron motifs were overrepresented, matching recent experimental results. Cell-type-specific network properties were even more striking when synaptic strength and sign were considered in generating a functional topology. Our systematic approach enables interpretation of microconnectomics 'big data' and provides several experimentally testable predictions.

Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function.

The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission into account, constructing graphs of a network that reflect the direction of information flow, and analyzing these directed graphs using algebraic topology. Applying this approach to a local network of neurons in the neocortex revealed a remarkably intricate and previously unseen topology of synaptic connectivity. The synaptic network contains an abundance of cliques of neurons bound into cavities that guide the emergence of correlated activity. In response to stimuli, correlated activity binds synaptically connected neurons into functional cliques and cavities that evolve in a stereotypical sequence toward peak complexity. We propose that the brain processes stimuli by forming increasingly complex functional cliques and cavities.

An algorithm to predict the connectome of neural microcircuits.

Experimentally mapping synaptic connections, in terms of the numbers and locations of their synapses and estimating connection probabilities, is still not a tractable task, even for small volumes of tissue. In fact, the six layers of the neocortex contain thousands of unique types of synaptic connections between the many different types of neurons, of which only a handful have been characterized experimentally. Here we present a theoretical framework and a data-driven algorithmic strategy to digitally reconstruct the complete synaptic connectivity between the different types of neurons in a small well-defined volume of tissue-the micro-scale connectome of a neural microcircuit. By enforcing a set of established principles of synaptic connectivity, and leveraging interdependencies between fundamental properties of neural microcircuits to constrain the reconstructed connectivity, the algorithm yields three parameters per connection type that predict the anatomy of all types of biologically viable synaptic connections. The predictions reproduce a spectrum of experimental data on synaptic connectivity not used by the algorithm. We conclude that an algorithmic approach to the connectome can serve as a tool to accelerate experimental mapping, indicating the minimal dataset required to make useful predictions, identifying the datasets required to improve their accuracy, testing the feasibility of experimental measurements, and making it possible to test hypotheses of synaptic connectivity.

Reconstruction and Simulation of Neocortical Microcircuitry.

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ~31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies. PAPERCLIP: VIDEO ABSTRACT.