Computational models of dentate gyrus with epilepsy-induced morphological alterations in granule cells.

YEBEH-03751; No. of pages: 8; 4C: 2, 4 Epilepsy & Behavior xxx (2014) xxx–xxx

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Epilepsy & Behavior journal homepage: www.elsevier.com/locate/yebeh

Review

Computational models of dentate gyrus with epilepsy-induced morphological alterations in granule cells Julian Tejada a,b,⁎, Antonio C. Roque a a b

Departamento de Física, FFCLRP, Universidade de Sao Paulo, Ribeirao Preto, SP 14040-901, Brazil Departamento de Psicologia, DPS, Universidade Federal de Sergipe, SE 49100-000, Brazil

a r t i c l e

i n f o

Article history: Revised 5 February 2014 Accepted 5 February 2014 Available online xxxx Keywords: Computational modeling of dentate gyrus Computational model of dentate gyrus granule cells Morphology–function relation Conductance-based computational models Temporal lobe epilepsy

a b s t r a c t Temporal lobe epilepsy provokes a number of different morphological alterations in granule cells of the hippocampus dentate gyrus. These alterations may be associated with the hyperactivity and hypersynchrony found in the epileptic dentate gyrus, and their study requires the use of different kinds of approaches including computational modeling. Conductance-based models of both normal and epilepsy-induced morphologically altered granule cells have been used in the construction of network models of dentate gyrus to study the effects of these alterations on epilepsy. Here, we review these models and discuss their contributions to the understanding of the association between alterations in neuronal morphology and epilepsy in the dentate gyrus. This article is part of a Special Issue entitled “NEWroscience 2013”.

1. Introduction Granule cells (GCs) are the principal cells of the dentate gyrus (DG) and one of the few cell types that undergo neurogenesis in the adult brain [1,2]. The DG GCs are the gateway to the hippocampus [3–5], through which information is passed from the entorhinal cortex to the CA3 field [4]. Due to their high rate of neurogenesis, they have an important role in different processes related to memory, learning, and diseases such as epilepsy [3,4]. Epilepsy is one of the neurological disorders that have the most impact, affecting up to 1% of the world's population. Among the different types of epilepsy, temporal lobe epilepsy (TLE) is one of the most common [3]. Patients with TLE present recurrent epileptic seizures with onset in one of the circuits that comprise the temporal lobe [3,6,7]. In spite of the large number of studies that have addressed the relationship between DG GCs and TLE, the role of these cells in TLE still remains unknown [1,2,8–23]. Different animal models of epilepsy have offered information about TLE-induced changes in the DG. They include pharmacologic-induced models, e.g., with pilocarpine, kainate acid, or pentylenetetrazol [6,7], kindling-induced models [6,7,13], and knockout gene models [24,25]. The alterations in DG GCs include changes in the

⁎ Corresponding author at: Departamento de Psicologia, DPS, Universidade Federal de Sergipe, SE, 49100-000, Brazil and Departamento de Física, FFCLRP, Universidade de Sao Paulo, Ribeirao Preto, SP, 14040-901, Brazil. Avenida Bandeirantes, 3900, Ribeirão Preto, São Paulo, 14049-900, Brazil. Tel.: +55 16 3602 3859. E-mail address: [email protected] (J. Tejada).

© 2014 Elsevier Inc. All rights reserved.

morphology of the cells [3], changes in the afferent and efferent connections, neurogenesis [3,16], and neurodegeneration [26]. The morphological characteristics of GCs have been widely studied, so alterations in their morphology easily stand out. In normal conditions, GCs have a cone-shaped tree of apical dendrites that grows only towards the DG molecular layer [3,27]. Their dendritic arborizations are highly symmetric with a reduction in the diameter of their dendrites and an increase in the number of spines in proportion with the distance from the soma [3,28]. The size of the GCs depends on their localization in the DG. Neurons located in the subpyramidal blade are larger than those in the infrapyramidal blade [3,29]. After status epilepticus (SE), the main morphological alterations present in the GCs are as follows: mossy fiber sprouting, a collateral axon that extends into the molecular layer [10,11,13]; loss of spines in the apical dendrites [14,22] and increased number of spines in the soma [30]; shorter apical dendrites with modified branching pattern [3,18]; a basal dendrite that extends into the hilus [1,10]; and ectopic migration [3,8,22]. Fig. 1 illustrates the main morphological alterations observed in GCs after SE. Besides the morphological alterations in GCs, the DG undergoes a series of other epilepsy-induced alterations such as loss of interneurons [21] and loss of excitatory inputs coming from the entorhinal cortex [31]. The way in which these morphological and physiological alterations interact to provoke hyperexcitability and hypersynchronization in the DG is still unclear, but some insights can be obtained from in vivo, in vitro, and in silico experiments. The most studied morphological alteration is mossy fiber sprouting. It corresponds to axon collaterals

http://dx.doi.org/10.1016/j.yebeh.2014.02.007 1525-5050/© 2014 Elsevier Inc. All rights reserved.

Please cite this article as: Tejada J, Roque AC, Computational models of dentate gyrus with epilepsy-induced morphological alterations in granule cells, Epilepsy Behav (2014), http://dx.doi.org/10.1016/j.yebeh.2014.02.007

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J. Tejada, A.C. Roque / Epilepsy & Behavior xxx (2014) xxx–xxx

Fig. 1. Main morphological alterations exhibited by the DG GCs. They are highlighted within zoomed views (circular drawings). Some of the alterations are related with axonal target: mossy fiber sprouting. Others are related with dendritic morphology: apical dendrites with altered morphology and aberrant basal dendrites. Another is related with abnormal or ectopic migration, and finally, there are alterations related with dendritic spine loss.

sprouting towards the inner molecular layer and making more than 500 synapses with other GCs (with no autapses) [13] and a few synapses (b 20) with GABAergic interneurons [13]. These synapses create recurrent self-excitatory inputs to GCs [3] and, in a smaller degree, recurrent inhibitory inputs via stimulated hilus interneurons [2,13,15]. In contrast, other morphological alterations may be related with decreased activity of GCs, for example spine loss [14]. With respect to ectopic migration, the integration of these cells in the circuit may provoke increased activity of both inhibitory and excitatory cells depending on the region in which they are integrated [16]. The relationship between hyperexcitability and the other morphological alterations, namely increment of number of spines in the soma, altered dendritic branch, and basal dendrite that extends into the hilus, remains less clear. The latter has been related in some studies with a decrease in activity because of the new connections with inhibitory interneurons [1,10], but other studies found that the basal dendrites also receive recurrent connections from other GCs, which are usually related with increase in activity [25,32]. Recent studies with knockout mice [24,25] have found that deletion of the Pten gene provokes all morphological alterations in GCs as shown in Fig. 1, and animals in which this gene deletion reaches 9% or more developed spontaneous seizures [25]. The deletion of the Pten gene provokes hyperactivation of the mammalian target of rapamycin (mTOR), a protein that regulates cell growth [33] and is activated in several

animal models of epilepsy [34,35]. These studies with knockout mice have opened the door to the possibility of linking morphological alterations in DG GCs with epileptogenesis and pointed out the necessity of deepening the studies on these morphological alterations to clarify the role that each one of them has in the emergence and development of the disease. The clear understanding of the way in which morphological alterations affect the emergence of epilepsy involves the correct identification of the relative involvement of each type of alteration in the increase of activity. To address this question, a possible methodological approach is to use models that allow not only the study of the effect of each single type of morphological alteration independently of the other but also the study of all of them together. It is here that computational neuroscience models become relevant to this problem because they provide just this type of methodological approach. There are a number of computational models of the DG that have addressed the effects of SE-induced morphological alterations in the DG. These include mossy fiber sprouting and mossy cell loss [36], changes in the topology of the dentate network [37,38], changes in ion channels [39–41], and the response of the circuit to paired-pulse inhibitions [42]. These network models are made with single-cell models of normal GCs [43] and of GCs with morphological alterations such as alterations in dendritic spines [44] and dendritic branches [45]. They also are based on data from studies on the different types of ion channels present in



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GCs [46–48] and the propagation of subthreshold dendritic signals in GCs [49]. All these models suggest possible effects that the morphological alterations may have on the activity of GCs and the DG network, and they will be reviewed here. In this work, we review the main computational models of morphological alterations in GCs and their role in epilepsy (a list of them is displayed in Table 1). We first review single GC models, and then we review DG models constructed out of these GC models. We will discuss the contributions of each model and the aspects that remain unclear.

cells to epileptogenic behavior may be controlled by the calcium-related channels, when they are spatially clustered; the slow delayed rectifier potassium current; and calcium buffering properties. The Aradi and Holmes model can be considered as the prototypical reduced compartmental model of a DG GC, consolidating information on the ion channels present in this cell and their dendritic distributions. A NEURON [55,56] implementation of this model is freely available on the neuroDB repository (http://senselab.med.yale.edu/ModelDb/ ShowModel.asp?model=116740).

1.1. Single dentate granule cell models

2. Dentate gyrus models

One of the most interesting features of DG GCs is their dendritic tree, which receives different synaptic inputs depending on the distance from soma [3]. The proximal third of the dendritic tree receives inputs from commissural/associational fibers, the middle third receives inputs from medial regions of the entorhinal cortex, and the distal third receives inputs from lateral regions of the entorhinal cortex [3]. The passive electrotonic properties of DG GCs have been widely studied, with their input resistance, membrane time constant, and electrotonic length being determined in the 1980s [50]. This information was used to build the early computational models of GCs in which the dendritic morphology was collapsed into a single equivalent cylinder [50–53]. Later, reduced compartmental models of dentate GCs were constructed with four [54] and nine [43] types of ionic channels distributed over the dendritic membrane surface. The model of Aradi and Holmes [43] was capable of reproducing accurately the firing pattern of DG GCs. It was also the first computational model to be explored in relation to epilepsy-induced alterations. Given its importance, we provide below a short review of this single-cell model.

2.1. The DG model of Santhakumar, Aradi, and Soltesz

1.2. The Aradi and Holmes GC model The Aradi and Holmes [43] model has nine ion channels: fast sodium, fast and slow delayed rectifier potassium, A-type potassium, BK and SK calcium-dependent potassium, and T-, N-, and L-type calcium channels. Each one of them is modeled according to the Hodgkin– Huxley formalism with their respective channel kinetics. The dendritic arborization is collapsed into two dendrites, each one of them being divided into four compartments: granule cell layer dendrites (GCLD), proximal dendrites (PD), middle dendrites (MD), and distal dendrites (DD). The axon is also subdivided into four compartments, Axon-1 to Axon-4 (Fig. 2A). The Aradi and Holmes model proposes values for the maximal conductance densities and distributions of the ionic channels along the dendritic branches based on fits of a large variety of experimental studies. Aradi and Holmes explored different characteristics of their model related with the depolarizing afterpotential and the resistance of GCs to epileptogenic burst behavior. They found that the resistance of these

The model of Santhakumar, Aradi, and Soltesz [36] (let us call it the SAS model) was the first conductance-based model of the DG built to study the effects of epilepsy-provoked alterations on the dentate activity. It is a scaled-down model (2000:1) of the DG made of reduced compartmental models of four major DG cell types: GCs, mossy cells, basket cells, and hilar perforant path-associated (HIPP) cells. The computational model of the GC was the Aradi and Holmes [43] model with some adaptations, and the models of the other cells are four-dendrite models with morphological and physiological parameters taken from the literature [57–63]. In these latter three cell models, the active conductances were distributed uniformly over all compartments with the exception of sodium and fast delayed potassium conductances. The model has only AMPA and fast GABAergic synapses, and the parameters that characterize the postsynaptic conductances were the peak conductance, the rise and decay time constants, and the average synaptic delay. The SAS model has 500 GCs, 15 mossy cells, 6 basket cells, and 6 HIPP cells connected as shown in the scheme of Fig. 2B. Two network types were studied, topographic and non-topographic. In the nontopographic network, the postsynaptic targets of each cell were selected randomly maintaining the cell-specific convergence and divergence. In the topographic network, the synaptic targets were constrained by the axonal distributions of the cell types and also kept the cell-specific convergence and divergence. The network was stimulated with inputs that simulated excitatory signals from the entorhinal cortex, making synapses to all granule and basket cells and to two randomly selected mossy cells. The resulting network activity in response to this simulated input was in agreement with experimental recordings. The SAS model was used to simulate two alterations commonly found in TLE: mossy fiber sprouting and mossy cell loss. The first was simulated by the creation of new synaptic connections between GCs. According to experimental evidence [10,11,13], the postsynaptic targets of these new synapses were the proximal dendrites of GCs. These recurrent synapses were constructed so that a proportion of up to 50% of the

Table 1 Broad overview of the main kinds of models used to study the morphological and physiological alterations of the dentate gyrus granule cells. Displayed here are some technical details as well as the URL for those which are available. Model

Description

[43]

Single-cell model

Purpose

Study of characteristics related with GC resistance to epileptogenic burst behavior [36] Network model Study of the effects of MFS and mossy cell loss on the dentate activity [37] Topological and functional Study the effects of the network structure on the network model dentate activity [39–41] Network model Study the effects of ion channel alteration and drug-binding on dentate activity [65] Network model Study the effects of ion channel alteration on dentate activity [45,66] Single-cell and Study the effects of dendritic morphological network models alteration on dentate activity

Software used URL to developed NEURON

http://senselab.med.yale.edu/ModelDb/ShowModel.asp?model=116740

NEURON


NEURON


PARPLEX and NEURON NEURON


NEURON

Not available

Not available


4


DD

A

MD PD GCLD Soma Axon

PP

B GC

BC

MFS

HC

Fig. 2. A. Schematic representation of the model of Aradi and Holmes [43] (adapted from [43]) showing the different segments in which the GC is divided: DD, Distal dendrites; MD, medial dendrites; PD, Proximal dendrites; and GCLD, granule cell layer dendrites. B. Scheme of the model of Santhakumar, Aradi, and Soltesz [36] showing, in the left, a tridimensional view of the model, and, in the right, the structure of the network connections (GC, granule cell; PP, perforant path; BC, basket cell; HC, hilar perforant-path associated cell; MC, mossy cell; and, MFS, mossy fiber sprouted) (adapted from [36]).

GCs receive inputs from 10 of their neighboring GCs. Mossy cell loss was simulated by simply deleting all synaptic contacts to and from a given proportion of the mossy cells in the network, which could be as high as 50% of the mossy cells [36]. Their results showed a strong effect of mossy fiber sprouting on the activity of the network. Even a small proportion of sprouting (10%) provoked an activity increase and spreading in the network. A number of tests were made in which parameters of the model were changed to test the robustness of the results. They changed the delay of the axonal conduction and the conductance of the mossy fiber synapses and added spontaneous activity to mossy cells, and the simulation results showed that the average network activity produced by mossy fiber sprouting was not significantly altered [36]. The results of the studies with the SAS model led to the conclusion that weak mossy fiber sprouting is enough to reproduce realistic patterns of excitability increase and activity spread without the need of simulating cell loss or changes in the intrinsic properties of the cell types [36]. Due to its biological fidelity, the SAS model was used in many posterior studies [37–42,64–66]. It is freely available on neuronDB (http:// senselab.med.yale.edu/ModelDb/ShowModel.asp?model=51781) in the NEURON format. 2.2. The model of Dyhrfjeld-Johnsen, Santhakumar, Morgan, Huerta, Tsimring, and Soltesz Dyhrfjeld-Johnsen and colleagues [37] constructed a 1:1 scale structural model of the DG to calculate graph-theoretical parameters for the corresponding network and topologically characterize the degree of mossy fiber sprouting. To do so, different versions of the structural network with varying degrees of mossy fiber sprouting were generated, and, for each one of them, the clustering coefficient and average path length [67,68] were calculated. They found that the normal DG has a

small-world structure with low average path length and high clustering coefficient and that mossy fiber sprouting enhances the small-world features of the network (there are a decrease in the average path length and an increase in the clustering coefficient). They proposed that a consequence of this enhancement of the small-world characteristics of the dentate network is an increase in the excitability of the network. The structural model of Dyhrfjeld-Johnsen and colleagues [37] had eight cell types: GCs, basket cells, axoaxonic cells, molecular layer cells with axonal projections to the perforant path, mossy cells, hilar cells with axonal projections to the perforant path, hilar cells with axonal projections to the commissural–associational pathway, and interneuronspecific cells. The latter four cell types are hilar cells (located in the hilus). Dyhrfjeld-Johnsen and colleagues generated versions of the structural model with progressive degrees of alterations represented by increasing amounts of mossy fiber sprouting and hilar cell loss, alterations that they equated to “sclerosis” by the similarities with the tissue alterations in medial temporal lobe epilepsy. They observed the same effect of increase in the small-world characteristics of the network with increase in the degree of sclerosis until about 90% of the hilar cells were lost. After this level of hilar cell loss, the topological structure of the network suddenly changed from small-world to regular (high values of both average path length and clustering coefficient). They attributed the initial enhancement of the small-world features of the dentate network with the increase in the degree of sclerosis to the creation of new recurrent connections between GCs by mossy fiber sprouting, which increases the clustering coefficient, while the existence of a few hilar cells maintained the average path length low. However, in an advanced sclerosis state with a very high level of hilar cell loss, the average path length changes to high values, and the network becomes structurally regular. Dyhrfjeld-Johnsen and colleagues predicted that this biphasic change in the network topology would reflect in a biphasic change in the network excitability, with an initial



increase in excitability followed by a decrease in excitability at very high sclerotic levels. To test their prediction, Dyhrfjeld-Johnsen and colleagues [37] constructed a “functional” version of the structural model, which was an expanded version of the SAS model with 50,000 model cells (corresponding to a DG model at a scale of 20:1). The functional model had the same four cell types of the SAS model and, due to the increase in size, a more accurate pattern of cell connectivity. They submitted the functional model to the same type of simulated entorhinal cortex excitatory input used in the SAS model, and they studied the activity pattern in the model for varying degrees of sclerosis, from control up to 100% sclerosis. Dyhrfjeld-Johnsen and colleagues found a biphasic behavior in the activity of the functional model, similar to the biphasic behavior found in the structural model. For sclerosis degrees between 20% and 80%, the excitability of the functional network increased, and the activity started to spread to the entire network at about 40% of sclerosis. After 80% sclerosis, the network activity started to decrease, reflecting the structural change in the network from a small-world topology to a more regular topology. The model of Dyhrfjeld-Johnsen and colleagues [37] confirmed the prediction of the SAS model of the importance of one type of epilepsyinduced alteration in GCs, namely mossy fiber sprouting, for the increase of DG hyperexcitability and offered a topological explanation for this effect based on structural alterations in the dentate network. It also showed the importance of concurrent changes in other cell types for maintenance of high excitability levels. The model of DyhrfjeldJohnsen and colleagues is available on ModelDB (http://senselab.med. yale.edu/ModelDb/ShowModel.asp?model=124513) in the NEURON format. 2.3. The Dyhrfjeld-Johnsen and colleagues model adaptation by Morgan and Soltesz Following the same strategy of Dyhrfjeld-Johnsen and colleagues [37], Morgan and Soltesz [38] evaluated the way in which alterations in the topology of the DG network related with the presence of GCs with basal dendrites could contribute to the hyperexcitability of the DG. Morgan and Soltesz used the enlarged SAS model developed by Dyhrfjeld-Johnsen and colleagues (the functional model). They constructed a control version of the model with 50% of hilar cell loss and 50% of mossy fiber sprouting. The connections among GCs were random, constrained only by the extent of their axonal arbors. From this control network, they constructed other network versions with nonrandom connections among the GCs. The objective was to study the effect of non-random epilepsy-induced recurrent connections among GCs on the excitability of the network [38]. They tested 4 different non-random connectivity patterns: Hebbianlike, overrepresentation of some small-network motifs, scale-free topology, and with hubs. In the Hebbian-like case, the probability of connection between two GCs increased in proportion to the number of shared presynaptic neurons. For the overrepresentation of motifs, they chose some three-neuron small network motifs and built networks in which the connections between GCs follow these patterns. In the scale-free topology, the distribution of connections among GCs followed a power law in which a few GCs (5%) received more than 175 connections, and the rest received only between 25 and 100 connections. Finally, in the case with hubs, they modified the scale-free topology of the previous case by increasing the number of connections of the 5% most connected GCs, which became hubs, and rearranged the connections of the remaining GCs so that they became random as in the control case. Their results showed that the activity pattern of the DG network is generally robust to different topological alterations. Neither the Hebbian-like type nor the overrepresentation of some small-network motif type of non-random GC connectivity caused increases in the

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activity of the network in comparison with the control case. However, the cases with scale-free topology and with hubs provoked great increases in the network excitability. They concluded that the key topological element responsible for the increase of excitability is the presence of hubs, which is the common factor in the two cases that led to excitability increases. Morgan and Soltesz [38] hypothesized that the highly interconnected GCs (hubs) that generated the increase in the excitability of the network would correspond to morphologically altered GCs with long basal dendrites that extend to the hilus. The percentage of GCs with such hilar basal dendrites is as low as the percentage of GC hubs that had to be created in the model to increase excitability. Moreover, there is evidence that GCs with hilar basal dendrites receive huge numbers of excitatory synapses from other GCs, therefore acting as hubs [38]. 2.4. Models of Thomas, Reid, Petrou, and colleagues Thomas, Reid, Berkovic, and Petrou [39] used an adaptation of the SAS model to study the effects of small changes in the GC ion channels on the hyperactivity of the network. They performed a large number of alterations in the gating parameters of the nine GC ion channels and studied the effects of these changes either alone or in combinations. The changes in the ion channels were implemented by adding small positive- or negative-voltage shifts in the activation and inactivation curves of the nine conductance-gated ion channels present in the GC model. They found that small changes in the activation curves of three ion channels, namely sodium, fast delayed rectifier potassium, and N-type calcium channel, caused significant changes in the network activity. The changes in the sodium and potassium channels are consistent with reports in the literature, according to which changes in the sodium channel prolong the duration of the GC response and changes in the rapidly activating potassium channel modulate the GC firing frequency. However, there are no reports in the literature relating changes in the N-type calcium channel with epilepsy. Thomas and colleagues [39] drew attention to possible ways in which genetic alterations could provoke these ion channel alterations and to the necessity to identify genes responsible for small changes in the activation currents of ion channels. At the same time, they pointed out the importance of studying the effects of the integration of diverse genetic alterations, which can provoke simultaneous morphological and ion channel changes such as mossy fiber sprouting and alterations in the gating properties of ion channels. The model of Thomas, Reid, Berkovic, and Petrou [39] is available on neuronDB (http://senselab.med.yale.edu/ModelDb/ShowModel.asp? model=124392) in a neural simulation language no longer under development called PARPLEX (http://www.evan-thomas.net/parplex/). In 2010, Thomas, Reid, and Petrou [40] continued with their sensitivity analysis and concentrated on the effects of changes in the gating parameters of the sodium channels on the activity of the dentate network. They introduced some changes in the version of the SAS model used in [40] in comparison with the adaptation of the SAS model used in [39]. For instance, in [40], they introduced a dendritic arborization in the GC model, and the GC sodium channels were modified by the addition of a slow inactivated state. They also changed the way in which the network is stimulated and used synchronized inputs of 20 Hz to stimulate all network cells. Thomas, Reid, and Petrou [40] performed a number of studies, including studies with isolated GCs submitted to simulated current clamp protocols and network studies with synchronized inputs and the same stimulation protocol used in the original SAS model [36], focusing on the possible effects of alterations in the rate of slow inactivation of sodium channels on GC and network excitability. They found that both the single-cell and network models were unresponsive to mutation-like changes in the slow inactivation of the sodium channels.


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In contrast, left shifts in the activation curve of the sodium channels plus mossy fiber sprouting provoked a self-sustained wave of activation with longer duration than observed in the presence of only mossy fiber sprouting. In 2013, Thomas and Petrou [41] explored the activation and inactivation of the GC sodium channels using a Markovian model of sodium channel gating with states that represent the bindings of the antiepileptic drugs carbamazepine and phenytoin. Their studies with single-neuron simulations showed that drug binding provokes firing frequency increase in the first milliseconds of stimulation followed by return to the control firing level after that, showing little accommodation in firing pattern. They also found that this behavior is dependent on the resting membrane potential. For a resting potential of −70 mV, the drugs reduce the activity of the cell, but for a resting potential of − 50 mV, they increase the duration of the GC response by a small amount. In the network context, their adaptation of the SAS model without mossy fiber sprouting did not show drug-related variations in activity. On the other hand, the combination of mossy fiber sprouting with binding drugs showed either an increase or a decrease of network activity depending on the GC resting potential, in the same way as observed in the single-cell model. 2.5. The SAS model adaptation by Winkels, Jedlicka, Weise, Schultz, Deller, and Schwarzacher Winkels and colleagues [65] used the SAS model to study the effects of a mutation that causes reductions in the voltage-gated sodium channel densities at the axon initial segment of GCs on the activity of the dentate network. Their hypothesis was that since this mutation reduces the spike-generating ability of GCs, it could also affect the excitability of the DG. To simulate the effects of the mutation, Winkels and colleagues [65] studied versions of the SAS model with varying levels of reduction of sodium channel densities in the axosomatic compartments of the GC model. Since there is no evidence that this mutation causes sodium channel density deficits only in GCs, they simulated the same density reductions in all cells of the SAS model. The simulations of Winkels and colleagues [65] showed a reduction in the activity of the network in proportion to the reduction in the sodium channel density in the model cells. They further simulated the effect of a pharmacological blockage of all inhibitory synapses and observed that this reestablished the normal firing of the network in response to entorhinal cortex stimulation. These results demonstrate the increase of the control of network activity by inhibitory neurons during reduction of sodium channel densities in the axosomatic compartments of DG cells. 2.6. The SAS model adaptation by Tejada, Garcia-Cairasco, and Roque In 2012, Tejada, Garcia-Cairasco, and Roque [66] used the SAS model to explore the effect of the dendritic morphological alterations in GCs on the network activity. In an early work, Tejada and colleagues [45] constructed multicompartmental models of newborn GCs using morphological data from tridimensional reconstructions provided by Arisi and Garcia-Cairasco [18]. The models constructed by Tejada and colleagues [45] included single-neuron models of newborn control GCs and newborn GCs with pilocarpine-induced SE, which had significant alterations in their dendritic morphology. Tejada and colleagues [45] stimulated their single-neuron models with stimuli that mimicked entorhinal cortex inputs to DG GCs and found that newborn GCs with altered morphology (assuming that anything else was similar, in other words, only the morphological changes were considered, in epileptic and control neurons) are less excitable than control cells. In order to investigate if this single-neuron level excitability reduction would be valid at the network level as well, Tejada, Garcia-

Cairasco, and Roque [66] replaced the reduced compartmental GC models of the SAS model by the morphologically reconstructed models of Tejada and colleagues [45]. They worked with a version of the SAS model with 10% of mossy fiber sprouting, which is enough for generating activity spread through the network after focal stimulation. They found that, in the network context, newborn GCs (either control or with SE-induced morphological alterations) have little effect on the excitability of the network. Interestingly, contrary to what would be expected based on the single-neuron studies, for very high proportions of GCs with morphological alterations (N 50%), the effect of the newborn GCs was to enhance the network excitability. 2.7. Other DG models Besides the computational models of the DG described above, there are other studies that address changes in properties of GC ion channels and their effects on the hyperexcitability of the dentate circuit [47–49]. However, these models do not consider morphological changes in the GCs. The SAS model has also been used to study paired-pulse inhibition [42] but with no connection to epilepsy-induced hyperexcitability. There have also been another full-scale structural model of the DG [69] and, more recently, the first real-scale model of the DG that combined morphology and physiology [70]. Both models were used to study normal circuit properties, without considering epilepsy-induced alterations and, therefore, are out of the scope of this review. 3. Discussion The use of large-scale, compartmental models of the DG has opened a new path of explorations on the effects of epilepsy-induced morphological alterations of GCs on the excitability of the dentate network. The advantage of using compartmental neuron models over spatially lumped neuron models is that the former allows the study of specific dendritic changes while the latter only allows the study of changes in the network connectivity pattern. A single DG model, namely the SAS model [36], has been used as the basic framework for the studies on morphological alterations of DG GCs and their effect on epilepsy. This model reproduces well the behavior of the dentate network both in the absence and in the presence of mossy fiber sprouting and has been used to evaluate the interaction of this axonal target alteration with other kinds of morphological, anatomical, and physiological changes, like GC basal dendrite alterations, GC ion channel property alterations, and hilar cell loss. The popularity of the SAS model is due to many factors like its satisfactory degree of biological realism, the use of the NEURON simulation environment [55,56], and, more importantly, its relatively low computational cost in comparison, for example, with the functional model of Dyhrfjeld-Johnsen and colleagues [37]. It is expected that with the advent of parallel NEURON and the more widespread availability of parallel computers, the model of Dyhrfjeld-Johnsen and colleagues or even its more recent full-scale implementation [70] will become the new standard for DG computational studies. The types of morphological alterations of GCs considered in the DG models reviewed here are summarized in Table 2. It is possible to see that mossy fiber sprouting is, by far, the most studied type of Table 2 Main morphological and physiological alterations observed in the dentate gyrus granule cells and the computational models used to study them. Topics

Models and adaptations of the models

Mossy fiber sprouting Mossy cell loss Ion channel alterations Network connectivity Dendritic morphology alteration Aberrant hilar basal dendrite

[36–41,66] [36,37] [39–41,65] [37,38] [66] [38]



morphological alteration. This is probably because the studies, starting with the original SAS model [36], show that mossy fiber sprouting is the strongest factor responsible for the hyperexcitability of the circuit. Therefore, every new study of a different type of GC morphological alteration has to consider the effect of the co-occurrence of the morphological alteration with mossy fiber sprouting. Interestingly, the interpretation of the reason why mossy fiber sprouting provokes hyperexcitability of the dentate circuit does not come from the SAS model itself but from the full-scale structural DG model [37], which is a lumped neuron model. This illustrates the importance of the interplay between models with different degrees of biological realism for the understanding of a neuroscience question. The implications of mossy fiber sprouting could go beyond its role in the hyperexcitability of the circuit. Mossy fiber sprouting could also be related with memory and learning processes. The simulations of Dyhrfjeld-Johnsen and colleagues [37] with small proportions of sprouting-mediated connections among GCs showed an increase in the efficiency of information transmission in the dentate network, which was lost when the amount of mossy fiber sprouting reached a critical point and the topology of the network changed from smallworld to regular. Regarding the other kinds of morphological alterations, the computational models constructed so far have offered some insights about their role. For instance, the results of the simulations of Tejada and colleagues [45,66] suggest that the alterations in the apical dendrites of GCs would have a protective role at the single-neuron level by rendering the neuron less excitable. However, in the network context, these alterations seem to have the opposite effect and contribute to the increase of network activity [66]. The DG models with conductance-based GC models have also been useful in studying the joint effect of molecular and morphological alterations on dentate excitability, such as ion channel mutations and mossy fiber sprouting. These studies offered insights about the role of changes in the resting potential of GCs on the expected therapeutic effects of antiepileptic drugs, showing that small depolarizing changes in the resting potential may cancel the therapeutic effect of the drugs. In spite of the many efforts so far in building DG computational models with morphological alterations in GCs, a number of epilepsyrelated morphological alterations have not yet been considered such as ectopic migration or spine loss in the apical dendrites. In the same vein, the effects of the interactions of these different morphological alterations are a task for future studies. As pointed out above, the advent of new parallel simulation tools and more powerful computers together with the construction of full-scale structural and functional models will strongly favor the development of models to fill the existing gaps. It is expected then that this new generation of DG computational models becomes an important tool for the study of TLE and epileptogenic circuits in general. Conflict of interest statement The authors declare that there are no conflicts of interest. Acknowledgments JT was the recipient of a postdoctoral scholarship from FAPESP, Brazil (2012/17057-2), and AR was the recipient of a grant from CNPq, Brazil. References [1] Jessberger S, Zhao C, Toni N, Clemenson GD, Li Y, Gage FH. Seizure-associated, aberrant neurogenesis in adult rats characterized with retrovirus-mediated cell labeling. J Neurosci 2007;27:9400–7. [2] Parent JM, Lowenstein DH. Seizure-induced neurogenesis: are more new neurons good for an adult brain? Prog Brain Res 2002;135:121–31. [3] Andersen P. The hippocampus book. USA: Oxford University Press; 2007.

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Corruption of the dentate gyrus by "dominant" granule cells: Implications for dentate gyrus function in health and disease.

ErbB3 signaling and dentate gyrus granule cell alterations in suicide.

ErbB3 signaling and dentate gyrus granule cell alterations in suicide.

The effect of paxilline on early alterations of electrophysiological properties of dentate gyrus granule cells in pilocarpine-treated rats.

Tetanus toxin blocks inhibition of granule cells in the dentate gyrus of the urethane-anaesthetized rat.

Transcriptome profiling of human hippocampus dentate gyrus granule cells in mental illness.

How to make a hippocampal dentate gyrus granule neuron.

Amino acids and the synaptic pharmacology of granule cells in the dentate gyrus of the rat.

Plasticity of intrinsic excitability in mature granule cells of the dentate gyrus.

Electrophysiological characterization of granule cells in the dentate gyrus immediately after birth.

Electrophysiological characterization of associational pathway terminating on dentate gyrus granule cells in the rat.

Tonic Inhibitory Control of Dentate Gyrus Granule Cells by α5-Containing GABAA Receptors Reduces Memory Interference.

Experience-Dependent Regulation of Dentate Gyrus Excitability by Adult-Born Granule Cells.

Functional alpha7 nicotinic receptors are expressed on immature granule cells of the postnatal dentate gyrus.

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

Membrane properties of dentate gyrus granule cells: comparison of sharp microelectrode and whole-cell recordings.

Age-Dependent Degeneration of Mature Dentate Gyrus Granule Cells Following NMDA Receptor Ablation.

5-HT1A receptors on mature dentate gyrus granule cells are critical for the antidepressant response.

Granule cell dispersion in the dentate gyrus of humans with temporal lobe epilepsy.

Alterations in the Interplay between Neurons, Astrocytes and Microglia in the Rat Dentate Gyrus in Experimental Models of Neurodegeneration.

Potential implications of a monosynaptic pathway from mossy cells to adult-born granule cells of the dentate gyrus.

Transcriptional profile of hippocampal dentate granule cells in four rat epilepsy models.

Quantitative, three-dimensional analysis of granule cell dendrites in the rat dentate gyrus.

Preventing effect of L-type calcium channel blockade on electrophysiological alterations in dentate gyrus granule cells induced by entorhinal amyloid pathology.