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The intensity and the features of sensory stimuli are encoded in the activity of neurons in the cortex. In the visual and piriform cortices, the stimulus intensity rescales the activity of the population without changing its selectivity for the stimulus features. The cortical representation of the stimulus is therefore intensity invariant. This emergence of network-invariant representations appears robust to local changes in synaptic strength induced by synaptic plasticity, even though (i) synaptic plasticity can potentiate or depress connections between neurons in a feature-dependent manner, and (ii) in networks with balanced excitation and inhibition, synaptic plasticity determines the nonlinear network behavior. In this study we investigate the consistency of invariant representations with a variety of synaptic states in balanced networks. By using mean-field models and spiking network simulations, we show how the synaptic state controls the emergence of intensity-invariant or intensity-dependent selectivity. In particular, we demonstrate that an effective power-law synaptic transformation at the population level is necessary for invariance. In a range of firing rates, purely depressing short-term synapses fulfills this condition, and in this case, the network is contrast-invariant. Instead, facilitating short-term plasticity generally narrows the network selectivity. We found that facilitating and depressing short-term plasticity can be combined to approximate a power-law that leads to contrast invariance. These results explain how the physiology of individual synapses is linked to the emergence of invariant representations of sensory stimuli at the network level.
The human immune system is determined by the functionality of the human lymph node. With the use of high-throughput techniques in clinical diagnostics, a large number of data is currently collected. The new data on the spatiotemporal organization of cells offers new possibilities to build a mathematical model of the human lymph node - a virtual lymph node. The virtual lymph node can be applied to simulate drug responses and may be used in clinical diagnosis. Here, we review mathematical models of the human lymph node from the viewpoint of cellular processes. Starting with classical methods, such as systems of differential equations, we discuss the values of different levels of abstraction and methods in the range from artificial intelligence techniques formalism.
Highlights
• Brain connectivity states identified by cofluctuation strength.
• CMEP as new method to robustly predict human traits from brain imaging data.
• Network-identifying connectivity ‘events’ are not predictive of cognitive ability.
• Sixteen temporally independent fMRI time frames allow for significant prediction.
• Neuroimaging-based assessment of cognitive ability requires sufficient scan lengths.
Abstract
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Solving the problem of consciousness remains one of the biggest challenges in modern science. One key step towards understanding consciousness is to empirically narrow down neural processes associated with the subjective experience of a particular content. To unravel these neural correlates of consciousness (NCC) a common scientific strategy is to compare perceptual conditions in which consciousness of a particular content is present with those in which it is absent, and to determine differences in measures of brain activity (the so called "contrastive analysis"). However, this comparison appears not to reveal exclusively the NCC, as the NCC proper can be confounded with prerequisites for and consequences of conscious processing of the particular content. This implies that previous results cannot be unequivocally interpreted as reflecting the neural correlates of conscious experience. Here we review evidence supporting this conjecture and suggest experimental strategies to untangle the NCC from the prerequisites and consequences of conscious experience in order to further develop the otherwise valid and valuable contrastive methodology.
From August to November 2017, Madagascar endured an outbreak of plague. A total of 2417 cases of plague were confirmed, causing a death toll of 209. Public health intervention efforts were introduced and successfully stopped the epidemic at the end of November. The plague, however, is endemic in the region and occurs annually, posing the risk of future outbreaks. To understand the plague transmission, we collected real-time data from official reports, described the outbreak's characteristics, and estimated transmission parameters using statistical and mathematical models. The pneumonic plague epidemic curve exhibited multiple peaks, coinciding with sporadic introductions of new bubonic cases. Optimal climate conditions for rat flea to flourish were observed during the epidemic. Estimate of the plague basic reproduction number during the large wave of the epidemic was high, ranging from 5 to 7 depending on model assumptions. The incubation and infection periods for bubonic and pneumonic plague were 4.3 and 3.4 days and 3.8 and 2.9 days, respectively. Parameter estimation suggested that even with a small fraction of the population exposed to infected rat fleas (1/10,000) and a small probability of transition from a bubonic case to a secondary pneumonic case (3%), the high human-to-human transmission rate can still generate a large outbreak. Controlling rodent and fleas can prevent new index cases, but managing human-to-human transmission is key to prevent large-scale outbreaks.
Ebola virus (EBOV) infection causes a high death toll, killing a high proportion of EBOV-infected patients within 7 days. Comprehensive data on EBOV infection are fragmented, hampering efforts in developing therapeutics and vaccines against EBOV. Under this circumstance, mathematical models become valuable resources to explore potential controlling strategies. In this paper, we employed experimental data of EBOV-infected nonhuman primates (NHPs) to construct a mathematical framework for determining windows of opportunity for treatment and vaccination. Considering a prophylactic vaccine based on recombinant vesicular stomatitis virus expressing the EBOV glycoprotein (rVSV-EBOV), vaccination could be protective if a subject is vaccinated during a period from one week to four months before infection. For the case of a therapeutic vaccine based on monoclonal antibodies (mAbs), a single dose might resolve the invasive EBOV replication even if it was administrated as late as four days after infection. Our mathematical models can be used as building blocks for evaluating therapeutic and vaccine modalities as well as for evaluating public health intervention strategies in outbreaks. Future laborator experiments will help to validate and refine the estimates of the windows of opportunity proposed here.
Background: The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language—quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data.
Results: QuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data.
Conclusions: The presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.
For medicine to fulfill its promise of personalized treatments based on a better understanding of disease biology, computational and statistical tools must exist to analyze the increasing amount of patient data that becomes available. A particular challenge is that several types of data are being measured to cope with the complexity of the underlying systems, enhance predictive modeling and enrich molecular understanding.
Here we review a number of recent approaches that specialize in the analysis of multimodal data in the context of predictive biomedicine. We focus on methods that combine different OMIC measurements with image or genome variation data. Our overview shows the diversity of methods that address analysis challenges and reveals new avenues for novel developments.
The development of epilepsy (epileptogenesis) involves a complex interplay of neuronal and immune processes. Here, we present a first-of-its-kind mathematical model to better understand the relationships among these processes. Our model describes the interaction between neuroinflammation, blood-brain barrier disruption, neuronal loss, circuit remodeling, and seizures. Formulated as a system of nonlinear differential equations, the model reproduces the available data from three animal models. The model successfully describes characteristic features of epileptogenesis such as its paradoxically long timescales (up to decades) despite short and transient injuries or the existence of qualitatively different outcomes for varying injury intensity. In line with the concept of degeneracy, our simulations reveal multiple routes toward epilepsy with neuronal loss as a sufficient but non-necessary component. Finally, we show that our model allows for in silico predictions of therapeutic strategies, revealing injury-specific therapeutic targets and optimal time windows for intervention.
Penile squamous cell carcinomas are rare tumor entities throughout Europe. Early lymphonodal spread urges for aggressive therapeutic approaches in advanced tumor stages. Therefore, understanding tumor biology and its microenvironment and correlation with known survival data is of substantial interest in order to establish treatment strategies adapted to the individual patient. Fifty-five therapy naïve squamous cell carcinomas, age range between 41 and 85 years with known clinicopathological data, were investigated with the use of tissue microarrays (TMA) regarding the tumor-associated immune cell infiltrate density (ICID). Slides were stained with antibodies against CD3, CD8 and CD20. An image analysis software was applied for evaluation. Data were correlated with clinicopathological characteristics and overall survival. There was a significant increase of ICID in squamous cell carcinomas of the penis in relation to tumor adjacent physiological tissue. Higher CD3-positive ICID was significantly associated with lower tumor stage in our cohort. The ICID was not associated with overall survival. Our data sharpens the view on tumor-associated immune cell infiltrate in penile squamous cell carcinomas with an unbiased digital and automated cell count. Further investigations on the immune cell infiltrate and its prognostic and possible therapeutic impact are needed.
Classic Hodgkin lymphoma (cHL) is usually characterized by a low tumour cell content, derived from crippled germinal centre B cells. Rare cases have been described in which the tumour cells show clonal T-cell receptor rearrangements. From a clinicopathological perspective, it is unclear if these cases should be classified as cHL or anaplastic large T-cell lymphoma (ALCL). Since we recently observed differences in the motility of ALCL and cHL tumour cells, here, we aimed to obtain a better understanding of T-cell-derived cHL by investigating their global proteomic profiles and their motility. In a proteomics analysis, when only motility-associated proteins were regarded, T-cell-derived cHL cell lines showed the highest similarity to ALK− ALCL cell lines. In contrast, T-cell-derived cHL cell lines presented a very low overall motility, similar to that observed in conventional cHL. Whereas all ALCL cell lines, as well as T-cell-derived cHL, predominantly presented an amoeboid migration pattern with uropod at the rear, conventional cHL never presented with uropods. The migration of ALCL cell lines was strongly impaired upon application of different inhibitors. This effect was less pronounced in cHL cell lines and almost invisible in T-cell-derived cHL. In summary, our cell line-derived data suggest that based on proteomics and migration behaviour, T-cell-derived cHL is a neoplasm that shares features with both cHL and ALCL and is not an ALCL with low tumour cell content. Complementary clinical studies on this lymphoma are warranted.
Sholl analysis has been an important technique in dendritic anatomy for more than 60 years. The Sholl intersection profile is obtained by counting the number of dendritic branches at a given distance from the soma and is a key measure of dendritic complexity; it has applications from evaluating the changes in structure induced by pathologies to estimating the expected number of anatomical synaptic contacts. We find that the Sholl intersection profiles of most neurons can be reproduced from three basic, functional measures: the domain spanned by the dendritic arbor, the total length of the dendrite, and the angular distribution of how far dendritic segments deviate from a direct path to the soma (i.e., the root angle distribution). The first two measures are determined by axon location and hence microcircuit structure; the third arises from optimal wiring and represents a branching statistic estimating the need for conduction speed in a neuron.
The cytoskeleton is crucial for defining neuronal-type-specific dendrite morphologies. To explore how the complex interplay of actin-modulatory proteins (AMPs) can define neuronal types in vivo, we focused on the class III dendritic arborization (c3da) neuron of Drosophila larvae. Using computational modeling, we reveal that the main branches (MBs) of c3da neurons follow general models based on optimal wiring principles, while the actin-enriched short terminal branches (STBs) require an additional growth program. To clarify the cellular mechanisms that define this second step, we thus concentrated on STBs for an in-depth quantitative description of dendrite morphology and dynamics. Applying these methods systematically to mutants of six known and novel AMPs, we revealed the complementary roles of these individual AMPs in defining STB properties. Our data suggest that diverse dendrite arbors result from a combination of optimal-wiring-related growth and individualized growth programs that are neuron-type specific.
In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin–eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, deep learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) can show variable histological growth patterns and present remarkable overlap with T-cell/histiocyte-rich large B-cell lymphoma (THRLBCL). Previous studies suggest that NLPHL histological variants represent progression forms of NLPHL and THRLBCL transformation in aggressive disease. Since molecular studies of both lymphomas are limited due to the low number of tumor cells, the present study aimed to learn if a better understanding of these lymphomas is possible via detailed measurements of nuclear and cell size features in 2D and 3D sections. Whereas no significant differences were visible in 2D analyses, a slightly increased nuclear volume and a significantly enlarged cell size were noted in 3D measurements of the tumor cells of THRLBCL in comparison to typical NLPHL cases. Interestingly, not only was the size of the tumor cells increased in THRLBCL but also the nuclear volume of concomitant T cells in the reactive infiltrate when compared with typical NLPHL. Particularly CD8+ T cells had frequent contacts to tumor cells of THRLBCL. However, the nuclear volume of B cells was comparable in all cases. These results clearly demonstrate that 3D tissue analyses are superior to conventional 2D analyses of histological sections. Furthermore, the results point to a strong activation of T cells in THRLBCL, representing a cytotoxic response against the tumor cells with unclear effectiveness, resulting in enhanced swelling of the tumor cell bodies and limiting proliferative potential. Further molecular studies combining 3D tissue analyses and molecular data will help to gain profound insight into these ill-defined cellular processes.
The impact of GABAergic transmission on neuronal excitability depends on the Cl--gradient across membranes. However, the Cl--fluxes through GABAA receptors alter the intracellular Cl- concentration ([Cl-]i) and in turn attenuate GABAergic responses, a process termed ionic plasticity. Recently it has been shown that coincident glutamatergic inputs significantly affect ionic plasticity. Yet how the [Cl-]i changes depend on the properties of glutamatergic inputs and their spatiotemporal relation to GABAergic stimuli is unknown. To investigate this issue, we used compartmental biophysical models of Cl- dynamics simulating either a simple ball-and-stick topology or a reconstructed CA3 neuron. These computational experiments demonstrated that glutamatergic co-stimulation enhances GABA receptor-mediated Cl- influx at low and attenuates or reverses the Cl- efflux at high initial [Cl-]i. The size of glutamatergic influence on GABAergic Cl--fluxes depends on the conductance, decay kinetics, and localization of glutamatergic inputs. Surprisingly, the glutamatergic shift in GABAergic Cl--fluxes is invariant to latencies between GABAergic and glutamatergic inputs over a substantial interval. In agreement with experimental data, simulations in a reconstructed CA3 pyramidal neuron with physiological patterns of correlated activity revealed that coincident glutamatergic synaptic inputs contribute significantly to the activity-dependent [Cl-]i changes. Whereas the influence of spatial correlation between distributed glutamatergic and GABAergic inputs was negligible, their temporal correlation played a significant role. In summary, our results demonstrate that glutamatergic co-stimulation had a substantial impact on ionic plasticity of GABAergic responses, enhancing the attenuation of GABAergic inhibition in the mature nervous systems, but suppressing GABAergic [Cl-]i changes in the immature brain. Therefore, glutamatergic shift in GABAergic Cl--fluxes should be considered as a relevant factor of short-term plasticity.
Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. Here, we hypothesized that attentional and contextual top-down signals interact in V1, in a manner that primarily benefits the representation of natural visual stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we found that attention enhanced the decodability of stimulus identity from population responses evoked by natural scenes but, critically, not by synthetic stimuli in which higher-order statistical regularities were eliminated. Population analysis revealed that neuronal responses converged to a low dimensional subspace for natural but not for synthetic images. Critically, we determined that the attentional enhancement in stimulus decodability was captured by the dominant low dimensional subspace, suggesting an alignment between the attentional and natural stimulus variance. The alignment was pronounced for late evoked responses but not for early transient responses of V1 neurons, supporting the notion that top-down feedback was required. We argue that attention and perception share top-down pathways, which mediate hierarchical interactions optimized for natural vision.
Sharp wave-ripples (SPW-Rs) are a hippocampal network phenomenon critical for memory consolidation and planning. SPW-Rs have been extensively studied in the adult brain, yet their developmental trajectory is poorly understood. While SPWs have been recorded in rodents shortly after birth, the time point and mechanisms of ripple emergence are still unclear. Here, we combine in vivo electrophysiology with optogenetics and chemogenetics in 4 to 12 days-old mice to address this knowledge gap. We show that ripples are robustly detected and induced by light stimulation of ChR2-transfected CA1 pyramidal neurons only from postnatal day (P) 10 onwards. Leveraging a spiking neural network model, we mechanistically link the maturation of inhibition and ripple emergence. We corroborate these findings by reducing ripple rate upon chemogenetic silencing of CA1 interneurons. Finally, we show that early SPW-Rs elicit a more robust prefrontal cortex response then SPWs lacking ripples. Thus, development of inhibition promotes ripples emergence.
We present a model for the autonomous learning of active binocular vision using a recently developed biome-chanical model of the human oculomotor system. The model is formulated in the Active Efficient Coding (AEC) framework, a recent generalization of classic efficient coding theories to active perception. The model simultaneously learns how to efficiently encode binocular images and how to generate accurate vergence eye movements that facilitate efficient encoding of the visual input. In order to resolve the redundancy problem arising from the actuation of the eyes through antagonistic muscle pairs, we consider the metabolic costs associated with eye movements. We show that the model successfully learns to trade off vergence accuracy against the associated metabolic costs, producing high fidelity vergence eye movements obeying Sherrington’s law of reciprocal innervation.