Effects of increasing CREB-dependent transcription on the storage and recall processes in a hippocampal CA1 microcircuit

A realistic computational model for the formation of a Place Cell

Hippocampal Place Cells (PCs) are pyramidal neurons showing spatially localized firing when an animal gets into a specific area within an environment. Because of their obvious and clear relation with specific cognitive functions, Place Cells operations and modulations are intensely studied experimentally. However, although a lot of data have been gathered since their discovery, the cellular processes that interplay to turn a hippocampal pyramidal neuron into a Place Cell are still not completely understood. Here, we used a morphologically and biophysically detailed computational model of a CA1 pyramidal neuron to show how, and under which conditions, it can turn into a neuron coding for a specific cue location, through the self-organization of its synaptic inputs in response to external signals targeting different dendritic layers. Our results show that the model is consistent with experimental findings demonstrating PCs stability within the same spatial context over different trajectories, environment rotations, and place field remapping to adapt to changes in the environment. To date, this is the only biophysically and morphologically accurate cellular model of PCs formation, which can be directly used in physiologically accurate microcircuits and large-scale model networks to study cognitive functions and dysfunctions at cellular level.

Altered synaptic plasticity at hippocampal CA1-CA3 synapses in Alzheimer's disease: integration of amyloid precursor protein intracellular domain and amyloid beta effects into computational models

Alzheimer's disease (AD) is a progressive memory loss and cognitive dysfunction brain disorder brought on by the dysfunctional amyloid precursor protein (APP) processing and clearance of APP peptides. Increased APP levels lead to the production of AD-related peptides including the amyloid APP intracellular domain (AICD) and amyloid beta (Aβ), and consequently modify the intrinsic excitability of the hippocampal CA1 pyramidal neurons, synaptic protein activity, and impair synaptic plasticity at hippocampal CA1-CA3 synapses. The goal of the present study is to build computational models that incorporate the effect of AD-related peptides on CA1 pyramidal neuron and hippocampal synaptic plasticity under the AD conditions and investigate the potential pharmacological treatments that could normalize hippocampal synaptic plasticity and learning in AD. We employ a phenomenological N-methyl-D-aspartate (NMDA) receptor-based voltage-dependent synaptic plasticity model that includes the separate receptor contributions on long-term potentiation (LTP) and long-term depression (LTD) and embed it into the a detailed compartmental model of CA1 pyramidal neuron. Modeling results show that partial blockade of Glu2NB-NMDAR-gated channel restores intrinsic excitability of a CA1 pyramidal neuron and rescues LTP in AICD and Aβ conditions. The model provides insight into the complex interactions in AD pathophysiology and suggests the conditions under which the synchronous activation of a cluster of synaptic inputs targeting the dendritic tree of CA1 pyramidal neuron leads to restored synaptic plasticity.

SLAMR, a synaptically targeted lncRNA, facilitates the consolidation of contextual fear memory

LncRNAs are involved in critical processes for cell homeostasis and function. However, it remains largely unknown whether and how the transcriptional regulation of long noncoding RNAs results in activity-dependent changes at the synapse and facilitate formation of long-term memories. Here, we report the identification of a novel lncRNA, SLAMR, that becomes enriched in CA1- but not in CA3-hippocampal neurons upon contextual fear conditioning. SLAMR is transported to dendrites via the molecular motor KIF5C and recruited to the synapse in response to stimulation. Loss of function of SLAMR reduced dendritic complexity and impaired activity dependent changes in spine structural plasticity. Interestingly, gain of function of SLAMR enhanced dendritic complexity, and spine density through enhanced translation. Analyses of the SLAMR interactome revealed its association with CaMKIIα protein through a 220-nucleotide element and its modulation of CaMKIIα activity. Furthermore, loss-of-function of SLAMR in CA1 selectively impairs consolidation but neither acquisition, recall, nor extinction of fear memory and spatial memory. Together, these results establish a new mechanism for activity dependent changes at the synapse and consolidation of contextual fear.

Membrane electrical properties of mouse hippocampal CA1 pyramidal neurons during strong inputs

In this work, we highlight an electrophysiological feature often observed in recordings from mouse CA1 pyramidal cells that has so far been ignored by experimentalists and modelers. It consists of a large and dynamic increase in the depolarization baseline (i.e., the minimum value of the membrane potential between successive action potentials during a sustained input) in response to strong somatic current injections. Such an increase can directly affect neurotransmitter release properties and, more generally, the efficacy of synaptic transmission. However, it cannot be explained by any currently available conductance-based computational model. Here we present a model addressing this issue, demonstrating that experimental recordings can be reproduced by assuming that an input current modifies, in a time-dependent manner, the electrical and permeability properties of the neuron membrane by shifting the ionic reversal potentials and channel kinetics. For this reason, we propose that any detailed model of ion channel kinetics for neurons exhibiting this characteristic should be adapted to correctly represent the response and the synaptic integration process during strong and sustained inputs.

Mathematical and computational modeling of the role of homocysteine in the oxidative process and its influence in neuronal degenerative diseases

Degenerative neurological diseases, although common in the population, are difficult to diagnose. However, it is known that most of them are directly related to the so-called oxidative stress. Understanding and evaluating how this process takes place is of great interest and, in this sense, this work extends the existing mathematical and computational models for the oxidative stress process (REIS, 2005; REIS et al., 2006; VIANNA, 2005; VIANNA; REIS; CARVALHO, 2012), incorporating an aspect not previously evaluated, the influence of homocysteine indices on the oxidants present in this process. The results indicate that hyperhomocysteinemia can in fact cause oxidative stress and consequent neuronal death, leading to the appearance of neurodegenerative diseases.

Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders

The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

Dysregulation of excitatory neural firing replicates physiological and functional changes in aging visual cortex

The mammalian visual system has been the focus of countless experimental and theoretical studies designed to elucidate principles of neural computation and sensory coding. Most theoretical work has focused on networks intended to reflect developing or mature neural circuitry, in both health and disease. Few computational studies have attempted to model changes that occur in neural circuitry as an organism ages non-pathologically. In this work we contribute to closing this gap, studying how physiological changes correlated with advanced age impact the computational performance of a spiking network model of primary visual cortex (V1). Our results demonstrate that deterioration of homeostatic regulation of excitatory firing, coupled with long-term synaptic plasticity, is a sufficient mechanism to reproduce features of observed physiological and functional changes in neural activity data, specifically declines in inhibition and in selectivity to oriented stimuli. This suggests a potential causality between dysregulation of neuron firing and age-induced changes in brain physiology and functional performance. While this does not rule out deeper underlying causes or other mechanisms that could give rise to these changes, our approach opens new avenues for exploring these underlying mechanisms in greater depth and making predictions for future experiments.

The Amyloid Precursor Protein C-Terminal Domain Alters CA1 Neuron Firing, Modifying Hippocampus Oscillations and Impairing Spatial Memory Encoding

There is a growing consensus that Alzheimer's disease (AD) involves failure of the homeostatic machinery, which underlies the firing stability of neural circuits. What are the culprits leading to neuron firing instability? The amyloid precursor protein (APP) is central to AD pathogenesis, and we recently showed that its intracellular domain (AICD) could modify synaptic signal integration. We now hypothesize that AICD modifies neuron firing activity, thus contributing to the disruption of memory processes. Using cellular, electrophysiological, and behavioral techniques, we show that pathological AICD levels weaken CA1 neuron firing activity through a gene-transcription-dependent mechanism. Furthermore, increased AICD production in hippocampal neurons modifies oscillatory activity, specifically in the γ-frequency range, and disrupts spatial memory task. Collectively, our data suggest that AICD pathological levels, observed in AD mouse models and in human patients, might contribute to progressive neuron homeostatic failure, driving the shift from normal aging to AD.

The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging

Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks-e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called "seven pillars of aging" combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.

A kinetic model for Brain-Derived Neurotrophic Factor mediated spike timing-dependent LTP

Across the mammalian nervous system, neurotrophins control synaptic plasticity, neuromodulation, and neuronal growth. The neurotrophin Brain-Derived Neurotrophic Factor (BDNF) is known to promote structural and functional synaptic plasticity in the hippocampus, the cerebral cortex, and many other brain areas. In recent years, a wealth of data has been accumulated revealing the paramount importance of BDNF for neuronal function. BDNF signaling gives rise to multiple complex signaling pathways that mediate neuronal survival and differentiation during development, and formation of new memories. These different roles of BDNF for neuronal function have essential consequences if BDNF signaling in the brain is reduced. Thus, BDNF knock-out mice or mice that are deficient in BDNF receptor signaling via TrkB and p75 receptors show deficits in neuronal development, synaptic plasticity, and memory formation. Accordingly, BDNF signaling dysfunctions are associated with many neurological and neurodegenerative conditions including Alzheimer’s and Huntington’s disease. However, despite the widespread implications of BDNF-dependent signaling in synaptic plasticity in healthy and pathological conditions, the interplay of the involved different biochemical pathways at the synaptic level remained mostly unknown. In this paper, we investigated the role of BDNF/TrkB signaling in spike-timing dependent plasticity (STDP) in rodent hippocampus CA1 pyramidal cells, by implementing the first subcellular model of BDNF regulated, spike timing-dependent long-term potentiation (t-LTP). The model is based on previously published experimental findings on STDP and accounts for the observed magnitude, time course, stimulation pattern and BDNF-dependence of t-LTP. It allows interpreting the main experimental findings concerning specific biomolecular processes, and it can be expanded to take into account more detailed biochemical reactions. The results point out a few predictions on how to enhance LTP induction in such a way to rescue or improve cognitive functions under pathological conditions.

Storing memory traces in the brain is essential for learning and memory formation, and it occurs through synaptic plasticity processes. Timing-dependent Long-Term Potentiation (t-LTP) is a physiologically relevant type of synaptic plasticity that results from the repeated sequential firing of action potentials (APs) in pre- and postsynaptic neurons. T-LTP is observed during learning in vivo and is a cellular correlate of memory formation. T-LTP can be elicited by different patterns of combined pre- and postsynaptic activity that recruit distinct synaptic growth processes underlying t-LTP. The protein Brain-Derived Neurotrophic Factor (BDNF) is released at synapses and mediates synaptic plasticity in response to specific patterns of t-LTP stimulation in the theta frequency band, while other patterns mediate BDNF-independent t-LTP. Here, we developed a realistic computational model that accounts for our previously published experimental results of BDNF-independent 1:1 t-LTP (70 repeats of pairing 1 presynaptic with 1 postsynaptic AP) and BDNF-dependent 1:4 t-LTP (25 repeats of pairing 1 presynaptic with 4 postsynaptic APs). The model explains the magnitude and time course of both t-LTP forms and allows predicting t-LTP properties that result from altered BDNF turnover. Since BDNF levels are decreased in demented patients, understanding the function of BDNF in memory processes is important to counteract neurodegenerative diseases.

Computational modeling and biomarker studies of pharmacological treatment of Alzheimer's disease (Review)

Alzheimer's disease (AD) is a complex and multifactorial disease. In order to understand the genetic influence in the progression of AD, and to identify novel pharmaceutical agents and their associated targets, the present study discusses computational modeling and biomarker evaluation approaches. Based on mechanistic signaling pathway approaches, various computational models, including biochemical and morphological models, are discussed to explore the strategies that may be used to target AD treatment. Different biomarkers are interpreted on the basis of morphological and functional features of amyloid β plaques and unstable microtubule‑associated tau protein, which is involved in neurodegeneration. Furthermore, imaging and cerebrospinal fluids are also considered to be key methods in the identification of novel markers for AD. In conclusion, the present study reviews various biochemical and morphological computational models and biomarkers to interpret novel targets and agonists for the treatment of AD. This review also highlights several therapeutic targets and their associated signaling pathways in AD, which may have potential to be used in the development of novel pharmacological agents for the treatment of patients with AD. Computational modeling approaches may aid the quest for the development of AD treatments with enhanced therapeutic efficacy and reduced toxicity.

Surface expression of hippocampal NMDA GluN2B receptors regulated by fear conditioning determines its contribution to memory consolidation in adult rats

The number and subtype composition of N-methyl-d-aspartate receptor (NMDAR) at synapses determines their functional properties and role in learning and memory. Genetically increased or decreased amount of GluN2B affects hippocampus-dependent memory in the adult brain. But in some experimental conditions (e.g., memory elicited by a single conditioning trial (1 CS-US)), GluN2B is not a necessary factor, which indicates that the precise role of GluN2B in memory formation requires further exploration. Here, we examined the role of GluN2B in the consolidation of fear memory using two training paradigms. We found that GluN2B was only required for the consolidation of memory elicited by five conditioning trials (5 CS-US), not by 1 CS-US. Strikingly, the expression of membrane GluN2B in CA1was training-strength-dependently increased after conditioning, and that the amount of membrane GluN2B determined its involvement in memory consolidation. Additionally, we demonstrated the increases in the activities of cAMP, ERK, and CREB in the CA1 after conditioning, as well as the enhanced intrinsic excitability and synaptic efficacy in CA1 neurons. Up-regulation of membrane GluN2B contributed to these enhancements. These studies uncover a novel mechanism for the involvement of GluN2B in memory consolidation by its accumulation at the cell surface in response to behavioral training.

Sparse coding and lateral inhibition arising from balanced and unbalanced dendrodendritic excitation and inhibition

The precise mechanism by which synaptic excitation and inhibition interact with each other in odor coding through the unique dendrodendritic synaptic microcircuits present in olfactory bulb is unknown. Here a scaled-up model of the mitral-granule cell network in the rodent olfactory bulb is used to analyze dendrodendritic processing of experimentally determined odor patterns. We found that the interaction between excitation and inhibition is responsible for two fundamental computational mechanisms: (1) a balanced excitation/inhibition in strongly activated mitral cells, leading to a sparse representation of odorant input, and (2) an unbalanced excitation/inhibition (inhibition dominated) in surrounding weakly activated mitral cells, leading to lateral inhibition. These results suggest how both mechanisms can carry information about the input patterns, with optimal level of synaptic excitation and inhibition producing the highest level of sparseness and decorrelation in the network response. The results suggest how the learning process, through the emergent development of these mechanisms, can enhance odor representation of olfactory bulb.

Modulation of AD neuropathology and memory impairments by the isoprostane F2α is mediated by the thromboxane receptor

Beside amyloid-β plaques and neurofibrillary tangles, brain oxidative damage has been constantly implicated in Alzheimer's disease (AD) pathogenesis. Numerous studies demonstrated that F2-isoprostanes, markers of in vivo lipid peroxidation, are elevated in AD patients and mouse models of the disease. Previously, we showed that the 8-isoprostaneF2α, (8ISO) increases brain amyloid-β levels and deposition in the Tg2576 mice. However, no data are available on its effects on behavior and tau metabolism. To this end, we characterize the behavioral, biochemical, and neuropathologic effects of 8ISO in the triple transgenic mouse model. Compared with controls, mice receiving 8ISO showed significant memory deficits, increase in tau phosphorylation, activation of the cyclin kinase 5 pathway, and neuroinflammation. All these effects were blocked by pharmacologic blockade of the thromboxane receptor. Our findings establish the novel functional role that oxidative stress via the formation of this isoprostane plays in the development of cognitive impairments and AD-related tau neuropathology. It provides important preclinical support to the neurobiological importance of the thromboxane receptor as an active player in the pathogenesis of AD.