Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy
Ontology highlight
ABSTRACT:
SUBMITTER: Jesse Pfammatter
PROVIDER: S-BSST353 | bioimages |
REPOSITORIES: bioimages
Similar Datasets
Project description:In temporal lobe epilepsy, the ability of the dentate gyrus to limit excitatory cortical input to the hippocampus breaks down, leading to seizures. The dentate gyrus is also thought to help discriminate between similar memories by performing pattern separation, but whether epilepsy leads to a breakdown in this neural computation, and thus to mnemonic discrimination impairments, remains unknown. Here we show that temporal lobe epilepsy is characterized by behavioral deficits in mnemonic discrimination tasks, in both humans (females and males) and mice (C57Bl6 males, systemic low-dose kainate model). Using a recently developed assay in brain slices of the same epileptic mice, we reveal a decreased ability of the dentate gyrus to perform certain forms of pattern separation. This is because of a subset of granule cells with abnormal bursting that can develop independently of early EEG abnormalities. Overall, our results linking physiology, computation, and cognition in the same mice advance our understanding of episodic memory mechanisms and their dysfunction in epilepsy.SIGNIFICANCE STATEMENT People with temporal lobe epilepsy (TLE) often have learning and memory impairments, sometimes occurring earlier than the first seizure, but those symptoms and their biological underpinnings are poorly understood. We focused on the dentate gyrus, a brain region that is critical to avoid confusion between similar memories and is anatomically disorganized in TLE. We show that both humans and mice with TLE experience confusion between similar situations. This impairment coincides with a failure of the dentate gyrus to disambiguate similar input signals because of pathologic bursting in a subset of neurons. Our work bridges seizure-oriented and memory-oriented views of the dentate gyrus function, suggests a mechanism for cognitive symptoms in TLE, and supports a long-standing hypothesis of episodic memory theories.
| S-EPMC8612476 | biostudies-literature
Project description:The insulin-IGF-1/DAF-2 pathway has a central role in the determination of aging and longevity in Caenorhabditis elegans and other organisms. In this paper, we measured neuronal insulin secretion (using INS-22::Venus) during C. elegans lifespan and monitored how this secretion is modified by redox homeostasis. We showed that INS-22::Venus secretion fluctuates during the organism lifetime reaching maximum levels in the active reproductive stage. We also demonstrate that long-lived daf-2 insulin receptor mutants show remarkable low levels of INS-22::Venus secretion. In contrast, we found that short-lived mutant worms that lack the oxidation repair enzyme MSRA-1 show increased levels of INS-22::Venus secretion, specifically during the reproductive stage. MSRA-1 is a target of the insulin-IGF-1/DAF-2 pathway, and the expression of this antioxidant enzyme exclusively in the nervous system rescues the mutant insulin release phenotype and longevity. The msra-1 mutant phenotype can also be reverted by antioxidant treatment during the active reproductive stage. We showed for the first time that there is a pattern of neuronal insulin release with a noticeable increment during the peak of reproduction. Our results suggest that redox homeostasis can modulate longevity through the regulation of insulin secretion, and that the insulin-IGF-1/DAF-2 pathway could be regulated, at least in part, by a feedback loop. These findings highlight the importance of timing for therapeutic interventions aimed at improving health span.
| S-EPMC6351846 | biostudies-other
Project description:Ongoing cortical activity consists of sequences of synchronized bursts, named neuronal avalanches, whose size and duration are power law distributed. These features have been observed in a variety of systems and conditions, at all spatial scales, supporting scale invariance, universality and therefore criticality. However, the mechanisms leading to burst triggering, as well as the relationship between bursts and quiescence, are still unclear. The analysis of temporal correlations constitutes a major step towards a deeper understanding of burst dynamics. Here, we investigate the relation between avalanche sizes and quiet times, as well as between sizes of consecutive avalanches recorded in cortex slice cultures. We show that quiet times depend on the size of preceding avalanches and, at the same time, influence the size of the following one. Moreover we evidence that sizes of consecutive avalanches are correlated. In particular, we show that an avalanche tends to be larger or smaller than the following one for short or long time separation, respectively. Our analysis represents the first attempt to provide a quantitative estimate of correlations between activity and quiescence in the framework of neuronal avalanches and will help to enlighten the mechanisms underlying spontaneous activity.
| S-EPMC4837393 | biostudies-literature
Project description:Perception of segregated sources is essential in navigating cluttered acoustic environments. A basic mechanism to implement this process is the temporal coherence principle. It postulates that a signal is perceived as emitted from a single source only when all of its features are temporally modulated coherently, causing them to bind perceptually. Here we report on neural correlates of this process as rapidly reshaped interactions in primary auditory cortex, measured in three different ways: as changes in response rates, as adaptations of spectrotemporal receptive fields following stimulation by temporally coherent and incoherent tone sequences, and as changes in spiking correlations during the tone sequences. Responses, sensitivity and presumed connectivity were rapidly enhanced by synchronous stimuli, and suppressed by alternating (asynchronous) sounds, but only when the animals engaged in task performance and were attentive to the stimuli. Temporal coherence and attention are therefore both important factors in auditory scene analysis.
| S-EPMC5228385 | biostudies-literature
Project description:BackgroundSeveral new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model.MethodsIn this paper, we propose a novel programming paradigm, called temporal constrained objects, which facilitates a principled approach to modelling complex dynamical systems. Temporal constrained objects are an extension of constrained objects with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation.ResultsWe identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of temporal constrained objects. Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that temporal constrained objects provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://github.com/compneuro/TCOB_Neuron.DiscussionTemporal constrained objects provide powerful capabilities for modelling the structural and dynamic aspects of neural systems. Capabilities of the constraint programming paradigm, such as declarative specification, the ability to express partial information and non-directionality, and capabilities of the object-oriented paradigm especially aggregation and inheritance, make this paradigm the right candidate for complex systems and computational modelling studies. With the advent of multi-core parallel computer architectures and techniques or parallel constraint-solving, the paradigm of temporal constrained objects lends itself to highly efficient execution which is necessary for modelling and simulation of large brain circuits.
| S-EPMC7924700 | biostudies-literature
Project description:Spontaneous activity of cortex in vitro and in vivo has been shown to organize as neuronal avalanches. Avalanches are cascades of neuronal activity that exhibit a power law in their size and duration distribution, typical features of balanced systems in a critical state. Recently it has been shown that the distribution of quiet times between consecutive avalanches in rat cortex slice cultures displays a non-monotonic behavior with a power law decay at short time scales. This behavior has been attributed to the slow alternation between up and down-states. Here we further characterize the avalanche process and investigate how the functional behavior of the quiet time distribution depends on the fine structure of avalanche sequences. By systematically removing smaller avalanches from the experimental time series we show that size and quiet times are correlated and highlight that avalanche occurrence exhibits the characteristic periodicity of θ and β/γ oscillations, which jointly emerge in most of the analyzed samples. Furthermore, our analysis indicates that smaller avalanches tend to be associated with faster β/γ oscillations, whereas larger ones are associated with slower θ and 1-2 Hz oscillations. In particular, large avalanches corresponding to θ cycles trigger cascades of smaller ones, which occur at β/γ frequency. This temporal structure follows closely the one of nested θ - β/γ oscillations. Finally we demonstrate that, because of the multiple time scales characterizing avalanche dynamics, the distributions of quiet times between avalanches larger than a certain size do not collapse onto a unique function when rescaled by the average occurrence rate. However, when considered separately in the up-state and in the down-state, these distributions are solely controlled by the respective average rate and two different unique function can be identified.
| S-EPMC4211381 | biostudies-literature
Project description:BackgroundCutaneous leishmaniasis (CL) is a vector-borne disease classified by the World Health Organization as one of the most neglected tropical diseases. Brazil has the highest incidence of CL in America and is one of the ten countries in the world with the highest number of cases. Understanding the spatiotemporal dynamics of CL is essential to provide guidelines for public health policies in Brazil. In the present study we used a spatial and temporal statistical approach to evaluate the dynamics of CL in Brazil.MethodsWe used data of cutaneous leishmaniasis cases provided by the Ministry of Health of Brazil from 2001 to 2017. We calculated incidence rates and used the Mann-Kendall trend test to evaluate the temporal trend of CL in each municipality. In addition, we used Kuldorff scan method to identify spatiotemporal clusters and emerging hotspots test to evaluate hotspot areas and their temporal trends.ResultsWe found a general decrease in the number of CL cases in Brazil (from 15.3 to 8.4 cases per 100 000 habitants), although 3.2% of municipalities still have an increasing tendency of CL incidence and 72.5% showed no tendency at all. The scan analysis identified a primary cluster in northern and central regions and 21 secondary clusters located mainly in south and southeast regions. The emerging hotspots analysis detected a high spatial and temporal variability of hotspots inside the main cluster area, diminishing hotspots in eastern Amazon and permanent, emerging, and new hotspots in the states of Amapá and parts of Pará, Roraima, Acre and Mato Grosso. The central coast the state of Bahia is one of the most critical areas due to the detection of a cluster of the highest rank in a secondary cluster, and because it is the only area identified as an intensifying hotspot.ConclusionsUsing a combination of statistical methods we were able to detect areas of higher incidence of CL and understand how it changed over time. We suggest that these areas, especially those identified as permanent, new, emerging and intensifying hotspots, should be targeted for future research, surveillance, and implementation of vector control measures.
| S-EPMC8207768 | biostudies-literature
Project description:Visual information processing in animals with large image forming eyes is carried out in highly structured retinotopically ordered neuropils. Visual neuropils in Drosophila form the optic lobe, which consists of four serially arranged major subdivisions; the lamina, medulla, lobula and lobula plate; the latter three of these are further subdivided into multiple layers. The visual neuropils are formed by more than 100 different cell types, distributed and interconnected in an invariant highly regular pattern. This pattern relies on a protracted sequence of developmental steps, whereby different cell types are born at specific time points and nerve connections are formed in a tightly controlled sequence that has to be coordinated among the different visual neuropils. The developing fly visual system has become a highly regarded and widely studied paradigm to investigate the genetic mechanisms that control the formation of neural circuits. However, these studies are often made difficult by the complex and shifting patterns in which different types of neurons and their connections are distributed throughout development. In the present paper we have reconstructed the three-dimensional architecture of the Drosophila optic lobe from the early larva to the adult. Based on specific markers, we were able to distinguish the populations of progenitors of the four optic neuropils and map the neurons and their connections. Our paper presents sets of annotated confocal z-projections and animated 3D digital models of these structures for representative stages. The data reveal the temporally coordinated growth of the optic neuropils, and clarify how the position and orientation of the neuropils and interconnecting tracts (inner and outer optic chiasm) changes over time. Finally, we have analyzed the emergence of the discrete layers of the medulla and lobula complex using the same markers (DN-cadherin, Brp) employed to systematically explore the structure and development of the central brain neuropil. Our work will facilitate experimental studies of the molecular mechanisms regulating neuronal fate and connectivity in the fly visual system, which bears many fundamental similarities with the retina of vertebrates.
| S-EPMC5825191 | biostudies-literature
Project description:In learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.
| S-EPMC3127803 | biostudies-literature
Project description:Neurons form complex networks that evolve over multiple time scales. In order to thoroughly characterize these networks, time dependencies must be explicitly modeled. Here, we present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. Our model combines the class of Stochastic Block Models for community formation with Gaussian processes to model changes in the community structure as a smooth function of time. We validate our model on synthetic data and demonstrate its utility on three different studies using in vitro cultures of dissociated neurons.
| S-EPMC7274455 | biostudies-literature