A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia

Postnatal hypofunction of N-methyl-D-aspartate receptors alters perforant path synaptic plasticity and filtering and impairs dentate gyrus-mediated spatial discrimination

BACKGROUND AND PURPOSE

Transient hypofunction of the NMDA receptor represents a convergence point for the onset and further development of psychiatric disorders, including schizophrenia. Although the cumulative evidence indicates dysregulation of the hippocampal formation in schizophrenia, the integrity of the synaptic transmission and plasticity conveyed by the somatosensorial inputs to the dentate gyrus, the perforant pathway synapses, have barely been explored in this pathological condition.

EXPERIMENTAL APPROACH

We identified a series of synaptic alterations of the lateral and medial perforant paths in animals postnatally treated with the NMDA antagonist MK-801. This dysregulation suggests decreased cognitive performance, for which the dentate gyrus is critical.

KEY RESULTS

We identified alterations in the synaptic properties of the lateral and medial perforant paths to the dentate gyrus synapses in slices from MK-801-treated animals. Altered glutamate release and decreased synaptic strength precede an impairment in the induction and expression of long-term potentiation (LTP) and CB1 receptor-mediated long-term depression (LTD). Remarkably, by inhibiting the degradation of 2-arachidonoylglycerol (2-AG), an endogenous ligand of the CB1 receptor, we restored the LTD in animals treated with MK-801. Additionally, we showed for the first time, that spatial discrimination, a cognitive task that requires dentate gyrus integrity, is impaired in animals exposed to transient hypofunction of NMDA receptors.

CONCLUSION AND IMPLICATIONS

Dysregulation of glutamatergic transmission and synaptic plasticity from the entorhinal cortex to the dentate gyrus has been demonstrated, which may explain the cellular dysregulations underlying the altered cognitive processing in the dentate gyrus associated with schizophrenia.

Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus

Pattern separation is a valuable computational function performed by neuronal circuits, such as the dentate gyrus, where dissimilarity between inputs is increased, reducing noise and increasing the storage capacity of downstream networks. Pattern separation is studied from both in vivo experimental and computational perspectives and, a number of different measures (such as orthogonalisation, decorrelation, or spike train distance) have been applied to quantify the process of pattern separation. However, these are known to give conclusions that can differ qualitatively depending on the choice of measure and the parameters used to calculate it. We here demonstrate that arbitrarily increasing sparsity, a noticeable feature of dentate granule cell firing and one that is believed to be key to pattern separation, typically leads to improved classical measures for pattern separation even, inappropriately, up to the point where almost all information about the inputs is lost. Standard measures therefore both cannot differentiate between pattern separation and pattern destruction, and give results that may depend on arbitrary parameter choices. We propose that techniques from information theory, in particular mutual information, transfer entropy, and redundancy, should be applied to penalise the potential for lost information (often due to increased sparsity) that is neglected by existing measures. We compare five commonly-used measures of pattern separation with three novel techniques based on information theory, showing that the latter can be applied in a principled way and provide a robust and reliable measure for comparing the pattern separation performance of different neurons and networks. We demonstrate our new measures on detailed compartmental models of individual dentate granule cells and a dentate microcircuit, and show how structural changes associated with epilepsy affect pattern separation performance. We also demonstrate how our measures of pattern separation can predict pattern completion accuracy. Overall, our measures solve a widely acknowledged problem in assessing the pattern separation of neural circuits such as the dentate gyrus, as well as the cerebellum and mushroom body. Finally we provide a publicly available toolbox allowing for easy analysis of pattern separation in spike train ensembles.

Treadmill Running Regulates Adult Neurogenesis, Spatial and Non-spatial Learning, Parvalbumin Neuron Activity by ErbB4 Signaling

Exercise can promote adult neurogenesis and improve symptoms associated with schizophrenia and other mental disorders via parvalbumin (PV)-positive GABAergic interneurons in the dentate gyrus ErbB4 is the receptor of neurotrophic factor neuregulin 1, expressed mostly in PV-positive interneurons. Whether ErbB4 in PV-positive neurons mediates the beneficial effect of exercise and adult neurogenesis on mental disorder needs to be further investigation. Here, we first conducted a four-week study on the effects of AG1478, an ErbB4 inhibitor, on memory and neurogenesis. AG1478 significantly impaired the performance in several memory tasks, including the T-maze, Morris water maze, and contextual fear conditioning, downregulated the expression of total ErbB4 (T-ErbB4) and the ratio of phosphate-ErbB4 (p-ErbB4) to T-ErbB4, and associated with neurogenesis impairment. Interestingly, AG1478 also appeared to decrease intracellular calcium levels in PV neurons, which could be reversed by exercise. These results suggest exercise may regulate adult neurogenesis and PV neuron activity through ErbB4 signaling. Overall, these findings provide further evidence of the importance of exercise for neurogenesis and suggest that targeting ErbB4 may be a promising strategy for improving memory and other cognitive functions in individuals with mental disorders.

Spiking neural network-based computational modeling of episodic memory

In this research article, a spiking neural network-based simulation of the hippocampus is performed to model the functionalities of episodic memory. The purpose of the simulation is to find a computational model through the biological architecture of the hippocampus and correct values for their architectural biological parameters to support the episodic memory functionalities. The episodic store of the model is represented by the collection of events, where each event is further subdivided into coactive activities of experience. The model has tried to mimic the three functionalities of episodic memory, which are pattern separation, pattern association, and their recallings. In pattern separation model used the dentate biological connectivity to generate almost different output patterns corresponding to similar input patterns to reduce interference between two similar memory traces so that ambiguity can be reduced during recalling. In pattern association, an STDP based event encoding and forgetting mechanism are used to mimic the encoding function of the CA3 region in which the coactive activities get associated with each other. A decoder is proposed based on CA1, which can answer the stored event related queries. Along with these functionalities model also supports recalling and encoding based forgetting. Experimental work is performed on the model for the given set of events to check for the pattern separation efficiency, pattern completion efficiency and to check the capability of decoding the answer. An empirical analysis of the results is done and compared with the SMRITI model of episodic memory.

Effect of APOE4 Allele and Gender on the Rate of Atrophy in the Hippocampus, Entorhinal Cortex, and Fusiform Gyrus in Alzheimer's Disease

BACKGROUND

The hippocampus, entorhinal cortex, and fusiform gyrus are brain areas that deteriorate during early-stage Alzheimer's disease (AD). The ApoE4 allele has been identified as a risk factor for AD development, is linked to an increase in the aggregation of amyloid ß (Aß) plaques in the brain, and is responsible for atrophy of the hippocampal area. However, to our knowledge, the rate of deterioration over time in individuals with AD, with or without the ApoE4 allele, has not been investigated.

METHOD

In this study, we, for the first time, analyze atrophy in these brain structures in AD patients with and without the ApoE4 using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

RESULTS

It was found that the rate of decrease in the volume of these brain areas over 12 months was related to the presence of ApoE4. Further, we found that neural atrophy was not different for female and male patients, unlike prior studies, suggesting that the presence of ApoE4 is not linked to the gender difference in AD.

CONCLUSION

Our results confirm and extend previous findings, showing that the ApoE4 allele gradually impacts brain regions impacted by AD.

A neurocomputational model of creative processes

Creativity is associated with finding novel, surprising, and useful solutions. We argue that creative cognitive processes, divergent thinking, abstraction, and improvisation are constructed on different novelty-based processes. The prefrontal cortex plays a role in creative ideation by providing a control mechanism. Moreover, thinking about novel solutions activates the distant or loosely connected neurons of a semantic network that involves the hippocampus. Novelty can also be interpreted as different combinations of earlier learned processes, such as the motor sequencing mechanism of the basal ganglia. In addition, the cerebellum is responsible for the precise control of movements, which is particularly important in improvisation. Our neurocomputational perspective is based on three creative processes centered on novelty seeking, subserved by the prefrontal cortex, hippocampus, cerebellum, basal ganglia, and dopamine. The algorithmic implementation of our model would enable us to describe commonalities and differences between these creative processes based on the proposed neural circuitry. Given that most previous studies have mainly provided theoretical and conceptual models of creativity, this article presents the first brain-inspired neural network model of creative cognition.

Biased 5-HT1A receptor agonists F13714 and NLX-101 differentially affect pattern separation and neuronal plasticity in rats after acute and chronic treatment

Pattern separation is a hippocampal process in which highly similar stimuli are recognized as separate representations, and deficits could lead to memory impairments in neuropsychiatric disorders such as schizophrenia. The 5-HT_1A receptor (5-HT_1AR) is believed to be involved in these hippocampal pattern separation processes. However, in the dorsal raphe nucleus (DRN), the 5-HT_1AR is expressed as a somatodendritic autoreceptor, negatively regulates serotonergic signaling, and could thereby counteract the effects of hippocampal postsynaptic 5-HT_1A receptors. Therefore, this study aims to identify how pre- and post-synaptic 5-HT_1AR activity affects pattern separation. Object pattern separation (OPS) performance was measured in male Wistar rats after both acute and chronic treatment (i.p.) with 5-HT_1AR biased agonists F13714 (0.0025 mg/kg acutely, 0.02 mg/kg/day chronically) or NLX-101 (0.08 mg/kg acutely, 0.32 mg/kg/day chronically), which preferentially activate autoreceptors or postsynaptic receptors respectively, for 14 days. Body temperature - a functional correlate of hypothalamic 5-HT_1AR stimulation - was measured daily. Additionally, 5-HT_1AR density (DRN) and plasticity markers (hippocampus) were assessed. Acute treatment with F13714 impaired OPS performance, whereas chronic treatment normalized this, and a drop in body temperature was found from day 4 onwards. NLX-101 enhanced OPS performance acutely and chronically, and caused an acute drop in body temperature. Chronic NLX-101 treatment increased doublecortin positive neurons in the dorsal hippocampus, while chronic treatment with F13714 resulted in a downregulation of 5-HT_1A autoreceptors, which likely reversed the acute impairment in OPS performance. Chronic treatment with NLX-101 appears to have therapeutic potential to improve brain plasticity and OPS performance.

Exercise to spot the differences: a framework for the effect of exercise on hippocampal pattern separation in humans

Exercise has a beneficial effect on mental health and cognitive functioning, but the exact underlying mechanisms remain largely unknown. In this review, we focus on the effect of exercise on hippocampal pattern separation, which is a key component of episodic memory. Research has associated exercise with improvements in pattern separation. We propose an integrated framework mechanistically explaining this relationship. The framework is divided into three pathways, describing the pro-neuroplastic, anti-inflammatory and hormonal effects of exercise. The pathways are heavily intertwined and may result in functional and structural changes in the hippocampus. These changes can ultimately affect pattern separation through direct and indirect connections. The proposed framework might guide future research on the effect of exercise on pattern separation in the hippocampus.

Mnemonic Discrimination Deficits in First-Episode Psychosis and a Ketamine Model Suggest Dentate Gyrus Pathology Linked to NMDA Receptor Hypofunction

BACKGROUND

Converging evidence from neuroimaging and postmortem studies suggests that hippocampal subfields are differentially affected in schizophrenia. Recent studies report dentate gyrus dysfunction in chronic schizophrenia, but the underlying mechanisms remain to be elucidated. Here, we sought to examine if this deficit is already present in first-episode psychosis and if NMDA receptor hypofunction, a putative central pathophysiological mechanism in schizophrenia, experimentally induced by ketamine, would result in a similar abnormality.

METHODS

We applied a mnemonic discrimination task selectively taxing pattern separation in two experiments: 1) a group of 23 patients with first-episode psychosis and 23 matched healthy volunteers and 2) a group of 19 healthy volunteers before and during a ketamine challenge (0.27 mg/kg over 10 min, then 0.25 mg/kg/hour for 50 min, 0.01 mL/s). We calculated response bias-corrected pattern separation and recognition scores. We also examined the relationships between task performance and symptom severity as well as ketamine levels.

RESULTS

We reported a deficit in pattern separation performance in patients with first-episode psychosis compared with healthy volunteers (p = .04) and in volunteers during the ketamine challenge compared with baseline (p = .003). Pattern recognition was lower in patients with first-episode psychosis than in control subjects (p < .01). Exploratory analyses revealed no correlation between task performance and Repeatable Battery for the Assessment of Neuropsychological Status total scores or positive symptoms in patients with first-episode psychosis or with ketamine serum levels.

CONCLUSIONS

We observed a mnemonic discrimination deficit in both datasets. Our findings suggest a tentative mechanistic link between dentate gyrus dysfunction in first-episode psychosis and NMDA receptor hypofunction.

SyNC, a Computationally Extensive and Realistic Neural Net to Identify Relative Impacts of Synaptopathy Mechanisms on Glutamatergic Neurons and Their Networks in Autism and Complex Neurological Disorders

Synaptic function and experience-dependent plasticity across multiple synapses are dependent on the types of neurons interacting as well as the intricate mechanisms that operate at the molecular level of the synapse. To understand the complexity of information processing at synaptic networks will rely in part on effective computational models. Such models should also evaluate disruptions to synaptic function by multiple mechanisms. By co-development of algorithms alongside hardware, real time analysis metrics can be co-prioritized along with biological complexity. The hippocampus is implicated in autism spectrum disorders (ASD) and within this region glutamatergic neurons constitute 90% of the neurons integral to the functioning of neuronal networks. Here we generate a computational model referred to as ASD interrogator (ASDint) and corresponding hardware to enable in silicon analysis of multiple ASD mechanisms affecting glutamatergic neuron synapses. The hardware architecture Synaptic Neuronal Circuit, SyNC, is a novel GPU accelerator or neural net, that extends discovery by acting as a biologically relevant realistic neuron synapse in real time. Co-developed ASDint and SyNC expand spiking neural network models of plasticity to comparative analysis of retrograde messengers. The SyNC model is realized in an ASIC architecture, which enables the ability to compute increasingly complex scenarios without sacrificing area efficiency of the model. Here we apply the ASDint model to analyse neuronal circuitry dysfunctions associated with autism spectral disorder (ASD) synaptopathies and their effects on the synaptic learning parameter and demonstrate SyNC on an ideal ASDint scenario. Our work highlights the value of secondary pathways in regard to evaluating complex ASD synaptopathy mechanisms. By comparing the degree of variation in the synaptic learning parameter to the response obtained from simulations of the ideal scenario we determine the potency and time of the effect of a particular evaluated mechanism. Hence simulations of such scenarios in even a small neuronal network now allows us to identify relative impacts of changed parameters and their effect on synaptic function. Based on this, we can estimate the minimum fraction of a neuron exhibiting a particular dysfunction scenario required to lead to complete failure of a neural network to coordinate pre-synaptic and post-synaptic outputs.

Parvalbumin Interneuron Activation-Dependent Adult Hippocampal Neurogenesis Is Required for Treadmill Running to Reverse Schizophrenia-Like Phenotypes

Physical exercise can alleviate some of the schizophrenia symptoms in patients, the mechanisms, however, are still unclear. To investigate whether the GABAergic interneuron involved in the therapeutic effect of treadmill running on schizophrenia, the parvalbumin (PV)-positive GABAergic interneurons in the dentate gyrus (DG) was specifically activated or abolished and the effects were evaluated. In the MK801-induced schizophrenia-like animal model, we found:(1) Treadmill running rescued the schizophrenia-related behavioral phenotypes, promoted the adult hippocampal neurogenesis, and increased the dendrite number and complexity of newborn neurons. (2) Treadmill running increased the number of PV-positive interneurons in the DG; genetic ablation of these interneurons reduced adult neurogenesis and abolished the effect of treadmill running on the schizophrenia-related behaviors. Consistently, chemogenetic activation of these interneurons improved neurogenesis and alleviated the schizophrenia-related behaviors. These results suggest a pivotal role of PV-positive interneuron-mediated adult neurogenesis in exercise. (3) However, schizophrenia-related behavioral phenotypes and adult neurogenesis in the DG could still be reversed by exercise after specifically knocking out the schizophrenia-related gene ErbB4 in PV interneurons, as a means to reduce their GABA release. These results suggest that activation of PV interneurons in the DG is sufficient for treadmill running to reverse schizophrenia-like phenotypes.

Parvalbumin+ interneurons obey unique connectivity rules and establish a powerful lateral-inhibition microcircuit in dentate gyrus

Parvalbumin-positive (PV⁺) GABAergic interneurons in hippocampal microcircuits are thought to play a key role in several higher network functions, such as feedforward and feedback inhibition, network oscillations, and pattern separation. Fast lateral inhibition mediated by GABAergic interneurons may implement a winner-takes-all mechanism in the hippocampal input layer. However, it is not clear whether the functional connectivity rules of granule cells (GCs) and interneurons in the dentate gyrus are consistent with such a mechanism. Using simultaneous patch-clamp recordings from up to seven GCs and up to four PV⁺ interneurons in the dentate gyrus, we find that connectivity is structured in space, synapse-specific, and enriched in specific disynaptic motifs. In contrast to the neocortex, lateral inhibition in the dentate gyrus (in which a GC inhibits neighboring GCs via a PV⁺ interneuron) is ~ 10-times more abundant than recurrent inhibition (in which a GC inhibits itself). Thus, unique connectivity rules may enable the dentate gyrus to perform specific higher-order computations.

GABAergic interneurons are known to provide inhibition to allow computational function of neuronal network. Here, Espinoza and colleagues show that connectivity of granule cells and interneurons in the dentate gyrus of mouse hippocampus are consistent with the circuit architecture capable of performing a winners-take-all mechanism.

Pattern separation and completion of distinct axonal inputs transmitted via micro-tunnels between co-cultured hippocampal dentate, CA3, CA1 and entorhinal cortex networks

OBJECTIVE

Functions ascribed to the hippocampal sub-regions for encoding episodic memories include the separation of activity patterns propagated from the entorhinal cortex (EC) into the dentate gyrus (DG) and pattern completion in CA3 region. Since a direct assessment of these functions is lacking at the level of specific axonal inputs, our goal is to directly measure the separation and completion of distinct axonal inputs in engineered pairs of hippocampal sub-regional circuits.

APPROACH

We co-cultured EC-DG, DG-CA3, CA3-CA1 or CA1-EC neurons in a two-chamber PDMS device over a micro-electrode array (MEA60), inter-connected via distinct axons that grow through the micro-tunnels between the compartments. Taking advantage of the axonal accessibility, we quantified pattern separation and completion of the evoked activity transmitted through the tunnels from source into target well. Since pattern separation can be inferred when inputs are more correlated than outputs, we first compared the correlations among axonal inputs with those of target somata outputs. We then compared, in an analog approach, the distributions of correlation distances between rate patterns of the axonal inputs inside the tunnels with those of the somata outputs evoked in the target well. Finally, in a digital approach, we measured the spatial population distances between binary patterns of the same axonal inputs and somata outputs.

MAIN RESULTS

We found the strongest separation of the propagated axonal inputs when EC was axonally connected to DG, with a decline in separation to CA3 and to CA1 for both rate and digital approaches. Furthermore, the digital approach showed stronger pattern completion in CA3, then CA1 and EC.

SIGNIFICANCE

To the best of our knowledge, these are the first direct measures of pattern separation and completion for axonal transmission to the somata target outputs at the rate and digital population levels in each of four stages of the EC-DG-CA3-CA1 circuit.

RETRACTED: Mnemonic Discrimination Deficits in First-Episode Psychosis and a Ketamine Model Suggests Dentate Gyrus Pathology Linked to N-Methyl-D-Aspartate Receptor Hypofunction

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). Retraction notice to: “Mnemonic Discrimination Deficits in First-Episode Psychosis and a Ketamine Model Suggests Dentate Gyrus Pathology Linked to N-Methyl-D-Aspartate Receptor Hypofunction” by Nina Vanessa Kraguljac, Matthew Carle, Michael A. Frölich, Steve Tran, Michael A. Yassa, David Matthew White, Abhishek Reddy, and Adrienne Carol Lahti (Biol Psychiatry Cogn Neurosci Neuroimaging 2018; 3:231-238); https://doi.org/10.1016/j.bpsc.2017.02.005. This article has been retracted at the request of Cameron S. Carter, M.D., Editor of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, with agreement from all authors. The authors discovered an error in the calculation of the response bias–corrected pattern recognition score in this article, which has significantly changed the results for experiment 1. Specifically, the authors discovered that the response bias corrected pattern recognition score was erroneously computed as P(‘old’|target) minus P(‘old’|lure) rather than P(‘old’|target) minus P(‘old’|foil). After re-running statistical analyses with the correct values, the authors found a significant difference in the response bias–corrected pattern recognition score in healthy volunteers (HV) compared with first-episode psychosis (FEP) patients (HV: 84.13 ± 10.96; FEP: 63.70 ± 21.83; t = 4.01; p < .01) in experiment 1. This finding is not consistent with the original report, where the authors reported no group differences in bias-corrected pattern recognition scores (originally reported values: t = 0.93, p = .36). The authors again found no significant correlations between pattern completion scores and BPRS total, positive, or negative symptom scores or RBANS scores, consistent with the original report. In experiment 2, bias-corrected pattern recognition scores did not differ between the saline and ketamine conditions (saline: 78.29 ± 28.04; ketamine: 73.59 ± 18.94; t = 0.81; p = 0.43), which is consistent with the original report (originally reported values: t = −0.69, p = .50). Contrary to the original report, task performance during the saline and ketamine infusions was no longer correlated at trend level for pattern recognition. Repeat analyses showed no correlations between pattern recognition scores during the ketamine challenge and BPRS total, positive, and negative symptom scores, or ketamine plasma levels at either time point, consistent with the original report. The authors have verified that bias-corrected pattern separation scores were calculated correctly for both experiments in the initial report. This error affects the abstract, the results, Figure 1, and discussion of the manuscript. The authors voluntarily informed the Journal of this honest error upon its discovery. Because of the extent and nature of the changes to the paper, the editors and authors concluded that, to ensure maximum clarity and transparency, the only course of action was to retract this version of the paper. The authors are revising the paper, which the Journal will re-review and consider further for publication.

Prenatal Immune Challenge in Mice Leads to Partly Sex-Dependent Behavioral, Microglial, and Molecular Abnormalities Associated with Schizophrenia

Epidemiological studies revealed that environmental factors comprising prenatal infection are strongly linked to risk for later development of neuropsychiatric disorders such as schizophrenia. Considering strong sex differences in schizophrenia and its increased prevalence in males, we designed a methodological approach to investigate possible sex differences in pathophysiological mechanisms. Prenatal immune challenge was modeled by systemic administration of the viral mimic polyinosinic-polycytidylic acid (Poly I:C) to C57BL/6 mice at embryonic day 9.5. The consequences on behavior, gene expression, and microglia—brain immune cells that are critical for normal development—were characterized in male vs. female offspring at adulthood. The cerebral cortex, hippocampus, and cerebellum, regions where structural and functional alterations were mainly described in schizophrenia patients, were selected for cellular and molecular analyses. Confocal and electron microscopy revealed most pronounced differences in microglial distribution, arborization, cellular stress, and synaptic interactions in the hippocampus of male vs. female offspring exposed to Poly I:C. Sex differences in microglia were also measured under both steady-state and Poly I:C conditions. These microglial alterations were accompanied by behavioral impairment, affecting for instance sensorimotor gating, in males. Consistent with these results, increased expression of genes related to inflammation was measured in cerebral cortex and hippocampus of males challenged with Poly I:C. Overall, these findings suggest that schizophrenia's higher incidence in males might be associated, among other mechanisms, with an increased microglial reactivity to prenatal immune challenges, hence determining disease outcomes into adulthood.

The effects of dynamical synapses on firing rate activity: a spiking neural network model

Accumulating evidence relates the fine-tuning of synaptic maturation and regulation of neural network activity to several key factors, including GABA_A signaling and a lateral spread length between neighboring neurons (i.e., local connectivity). Furthermore, a number of studies consider short-term synaptic plasticity (STP) as an essential element in the instant modification of synaptic efficacy in the neuronal network and in modulating responses to sustained ranges of external Poisson input frequency (IF). Nevertheless, evaluating the firing activity in response to the dynamical interaction between STP (triggered by ranges of IF) and these key parameters in vitro remains elusive. Therefore, we designed a spiking neural network (SNN) model in which we incorporated the following parameters: local density of arbor essences and a lateral spread length between neighboring neurons. We also created several network scenarios based on these key parameters. Then, we implemented two classes of STP: (1) short-term synaptic depression (STD) and (2) short-term synaptic facilitation (STF). Each class has two differential forms based on the parametric value of its synaptic time constant (either for depressing or facilitating synapses). Lastly, we compared the neural firing responses before and after the treatment with STP. We found that dynamical synapses (STP) have a critical differential role on evaluating and modulating the firing rate activity in each network scenario. Moreover, we investigated the impact of changing the balance between excitation (E) and inhibition (I) on stabilizing this firing activity.

Hippocampal subregion volume changes associated with antipsychotic treatment in first-episode psychosis

BACKGROUND

Hippocampal dysfunction is considered central to many neurobiological models of schizophrenia, yet there are few longitudinal in vivo neuroimaging studies that have investigated the relationship between antipsychotic treatment and morphologic changes within specific hippocampal subregions among patients with psychosis.

METHOD

A total of 29 patients experiencing a first episode of psychosis with little or no prior antipsychotic exposure received structural neuroimaging examinations at illness onset and then following 12 weeks of treatment with either risperidone or aripiprazole in a double-blind randomized clinical trial. In addition, 29 healthy volunteers received structural neuroimaging examinations at baseline and 12-week time points. We manually delineated six hippocampal subregions [i.e. anterior cornu ammonis (CA) 1-3, posterior CA1-3, subiculum, dentate gyrus/CA4, entorhinal cortex, and fimbria] from 3T magnetic resonance images using an established method with high inter- and intra-rater reliability.

RESULTS

Following antipsychotic treatment patients demonstrated significant reductions in dentate gyrus/CA4 volume and increases in subiculum volume. Healthy volunteers demonstrated non-significant volumetric changes in these subregions across the two time points. We observed a significant quadratic (i.e. inverted U) association between changes in dentate gyrus/CA4 volume and cumulative antipsychotic dosage between the scans.

CONCLUSIONS

This study provides the first evidence to our knowledge regarding longitudinal in vivo volumetric changes within specific hippocampal subregions in patients with psychosis following antipsychotic treatment. The finding of a non-linear relationship between changes in dentate gyrus/CA4 subregion volume and antipsychotic exposure may provide new avenues into understanding dosing strategies for therapeutic interventions relevant to neurobiological models of hippocampal dysfunction in psychosis.

Pattern separation in the hippocampus through the eyes of computational modeling

Pattern separation is a mnemonic process that has been extensively studied over the years. It entails the ability -of primarily hippocampal circuits- to distinguish between highly similar inputs, via generating different neuronal activity (output) patterns. The dentate gyrus (DG) in particular has long been hypothesized to implement pattern separation by detecting and storing similar inputs as distinct representations. The ways in which these distinct representations can be generated have been explored in a number of theoretical and computational modeling studies. Here, we review two categories of pattern separation models: those that address the phenomenon in an abstract mathematical fashion and those that delve into the underlying biological mechanisms by taking into account the anatomy and/or physiology of hippocampal circuits. We summarize the strategies, findings and limitations of these modeling approaches in the light of new experimental findings and propose a unifying framework whereby different network, cellular and sub-cellular mechanisms converge to a common goal: controlling sparsity, the key determinant of pattern separation in the DG.

Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity

The hippocampus plays a key role in pattern separation, the process of transforming similar incoming information to highly dissimilar, nonverlapping representations. Sparse firing granule cells (GCs) in the dentate gyrus (DG) have been proposed to undertake this computation, but little is known about which of their properties influence pattern separation. Dendritic atrophy has been reported in diseases associated with pattern separation deficits, suggesting a possible role for dendrites in this phenomenon. To investigate whether and how the dendrites of GCs contribute to pattern separation, we build a simplified, biologically relevant, computational model of the DG. Our model suggests that the presence of GC dendrites is associated with high pattern separation efficiency while their atrophy leads to increased excitability and performance impairments. These impairments can be rescued by restoring GC sparsity to control levels through various manipulations. We predict that dendrites contribute to pattern separation as a mechanism for controlling sparsity. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc.

Neurogenesis and pattern separation: time for a divorce

The generation of new neurons in the adult mammalian brain has led to numerous theories as to their functional significance. One of the most widely held views is that adult neurogenesis promotes pattern separation, a process by which overlapping patterns of neural activation are mapped to less overlapping representations. While a large body of evidence supports a role for neurogenesis in high interference memory tasks, it does not support the proposed function of neurogenesis in mediating pattern separation. Instead, the adult-generated neurons seem to generate highly overlapping and yet distinct distributed representations for similar events. One way in which these immature, highly plastic, hyperactive neurons may contribute to novel memory formation while avoiding interference is by virtue of their extremely sparse connectivity with incoming perforant path fibers. Another intriguing proposal, awaiting empirical confirmation, is that the young neurons' recruitment into memory formation is gated by a novelty/mismatch mechanism mediated by CA3 or hilar back-projections. Ongoing research into the intriguing link between neurogenesis, stress-related mood disorders, and age-related neurodegeneration may lead to promising neurogenesis-based treatments for this wide range of clinical disorders. WIREs Cogn Sci 2017, 8:e1427. doi: 10.1002/wcs.1427 For further resources related to this article, please visit the WIREs website.

Impaired neurogenesis of the dentate gyrus is associated with pattern separation deficits: A computational study

The separation of input patterns received from the entorhinal cortex (EC) by the dentate gyrus (DG) is a well-known critical step of information processing in the hippocampus. Although the role of interneurons in separation pattern efficiency of the DG has been theoretically known, the balance of neurogenesis of excitatory neurons and interneurons as well as its potential role in information processing in the DG is not fully understood. In this work, we study separation efficiency of the DG for different rates of neurogenesis of interneurons and excitatory neurons using a novel computational model in which we assume an increase in the synaptic efficacy between excitatory neurons and interneurons and then its decay over time. Information processing in the EC and DG was simulated as information flow in a two layer feed-forward neural network. The neurogenesis rate was modeled as the percentage of new born neurons added to the neuronal population in each time bin. The results show an important role of an optimal neurogenesis rate of interneurons and excitatory neurons in the DG in efficient separation of inputs from the EC in pattern separation tasks. The model predicts that any deviation of the optimal values of neurogenesis rates leads to different decreased levels of the separation deficits of the DG which influences its function to encode memory.

On the Complexity of Brain Disorders: A Symptom-Based Approach

Mounting evidence shows that brain disorders involve multiple and different neural dysfunctions, including regional brain damage, change to cell structure, chemical imbalance, and/or connectivity loss among different brain regions. Understanding the complexity of brain disorders can help us map these neural dysfunctions to different symptom clusters as well as understand subcategories of different brain disorders. Here, we discuss data on the mapping of symptom clusters to different neural dysfunctions using examples from brain disorders such as major depressive disorder (MDD), Parkinson’s disease (PD), schizophrenia, posttraumatic stress disorder (PTSD) and Alzheimer’s disease (AD). In addition, we discuss data on the similarities of symptoms in different disorders. Importantly, computational modeling work may be able to shed light on plausible links between various symptoms and neural damage in brain disorders.

Adult hippocampal neurogenesis and pattern separation in DG: a role for feedback inhibition in modulating sparseness to govern population-based coding

The dentate gyrus (DG) of mammals harbors neural stem cells that generate new dentate granule cells (DGCs) throughout life. Behavioral studies using the contextual fear discrimination paradigm have found that selectively augmenting or blocking adult hippocampal neurogenesis enhances or impairs discrimination under conditions of high, but not low, interference suggestive of a role in pattern separation. Although contextual discrimination engages population-based coding mechanisms underlying pattern separation such as global remapping in the DG and CA3, how adult hippocampal neurogenesis modulates pattern separation in the DG is poorly understood. Here, we propose a role for adult-born DGCs in re-activation coupled modulation of sparseness through feed-back inhibition to govern global remapping in the DG.