Specificity, Versatility, and Continual Development: The Power of Optogenetics for Epilepsy Research

Abstract

Optogenetics is a powerful and rapidly expanding set of techniques that use genetically encoded light sensitive proteins such as opsins. Through the selective expression of these exogenous light-sensitive proteins, researchers gain the ability to modulate neuronal activity, intracellular signaling pathways, or gene expression with spatial, directional, temporal, and cell-type specificity. Optogenetics provides a versatile toolbox and has significantly advanced a variety of neuroscience fields. In this review, using recent epilepsy research as a focal point, we highlight how the specificity, versatility, and continual development of new optogenetic related tools advances our understanding of neuronal circuits and neurological disorders. We additionally provide a brief overview of some currently available optogenetic tools including for the selective expression of opsins.

Introduction

Optogenetics rests on the use of genetically encoded light sensitive proteins including opsins (Figure 1, Box 1; Deisseroth, 2011, 2015; Boyden, 2015). Through exogenous expression of these light sensitive proteins, researchers gain the ability to modulate neuronal activity, intracellular signaling pathways, or gene expression through the delivery of light (Figure 2). Spatially and temporally restricted delivery of light provides optogenetic approaches with both temporal and spatial specificity. The use of different light-sensitive proteins provides an additional element of experimental control, including the direction of modulation (e.g., excitation vs. inhibition of neuronal populations). Finally, the restricted expression of light-sensitive proteins allows cell-type specific modulation, that is, selectivity for which cell populations are directly affected when light is delivered (Figure 3, Box 2).

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Optogenetics thereby gives researchers an unprecedented ability to manipulate neuronal populations with cell type, spatial, directional and temporal specificity. This specificity makes optogenetics a powerful tool. Adding to its success, optogenetic techniques can be relatively easy to implement, in large part due to active decisions by tool developers to make tools widely accessible. Importantly, optogenetics is also a flexible platform, which can be adapted to the experimenter’s research needs. The continual creation and application of new optogenetic related tools further expands the usefulness of optogenetics in neuroscience research. Given these strengths (specificity, versatility, and continual development) of optogenetic approaches, it is not surprising that optogenetics is being rapidly embraced by a variety of neuroscience fields (Deisseroth, 2015; Montgomery et al., 2016).

Optogenetics has proven to be a highly powerful and useful tool for studying healthy and pathological brain activity from a cellular and systems/circuit level perspective (Boyden, 2015; Deisseroth, 2015). Indeed, optogenetics has become so ubiquitous a tool for studying the brain that it is not feasible to describe all of its uses and contributions in a single review. To illustrate the broad versatility and power of optogenetics, Table 1 highlights a few key examples of optogenetics being used to study neurological disorders that the World Health Organization has named among the most burdensome. One such neurological condition is epilepsy (Table 2; Krook-Magnuson and Soltesz, 2015).

Epilepsy is the fourth most common neurological disorder, affecting roughly 65 million people worldwide (England et al., 2012). Unfortunately, in large part due to a critical lack of understanding of many fundamental aspects of epilepsy, current treatment options fall short. It is estimated that one third or more of epilepsy patients do not achieve adequate seizure control with current treatment options, and current treatment options can have major negative side effects (de Tisi et al., 2011; Perucca and Gilliam, 2012; Duchowny and Bhatia, 2014; Laxer et al., 2014). Optogenetics, by providing researchers with a powerful and flexible tool, is allowing significant advances in our understanding of epilepsy (for additional reviews related to the general topic of epilepsy and optogenetics, see also Krook-Magnuson et al., 2014a; Krook-Magnuson and Soltesz, 2015; Tung et al., 2016; Choy et al., 2017; Forcelli, 2017; Tønnesen and Kokaia, 2017). Here, we discuss this work in epilepsy research highlighting how well-suited an optogenetic approach is to epilepsy research in particular and how the benefits of an optogenetic approach can be leveraged in future research. Optogenetic techniques have been uniquely valuable in identifying and testing brain circuit elements that may be capable of seizure control, testing hypotheses related to epilepsy, and determining the roles of specific neuronal populations in epilepsy and seizures.

While optogenetics is a powerful tool in neuroscience research, there are a number of practical hurdles which must be overcome before optogenetics can be used in a clinical setting for epilepsy or other neurological disorders. As discussed in more depth elsewhere (Kullmann et al., 2014; Krook-Magnuson and Soltesz, 2015), these challenges include demonstrating safety of opsin expression, determining the optimal methods to achieve opsin expression in human tissue, and devices for appropriate light delivery. To date, optogenetic techniques have had the most success in smaller animals, including rodents (see e.g., Tables 1, 2), zebrafish (e.g., Motta-Mena et al., 2014; Cosentino et al., 2015; Kainrath et al., 2017), and Drosophila (e.g., Zemelman et al., 2002; Dawydow et al., 2014; Liu et al., 2017; Mohammad et al., 2017). The “scaling up” of optogenetic techniques to larger animals is an additional potential issue which needs to be addressed; however, recent advancements in this arena have been made (e.g., Yazdan-Shahmorad et al., 2018). Despite these challenges, optogenetics may one day be used not only as a research tool, but also as a clinical tool. Optogenetic based approaches are currently entering early clinical trials for treating retinitis pigmentosa through companies including GenSight Biologics (Paris, France) and RetroSense Therapeutics (Ann Arbor, MI, USA). If these trials demonstrate that exogenous opsins can be safely expressed in humans, further investigation of the clinical potential of optogenetics can be expected. Epilepsy is likely to be one of the subsequent early targets for the clinical application of optogenetics. This is due to: (i) the success of some early preclinical optogenetic work in the epilepsy field discussed in part below; (ii) the opportunity for optogenetics to fill a clinical need for new treatment strategies for epilepsy that are more efficacious with fewer negative side effects; and (iii) practical reasons, as one current treatment strategy for epilepsy is surgical removal of brain tissue. Thus, an optogenetic approach could be tried prior to surgical resection, with opsin expression attempted in the brain area that would otherwise be removed; if the optogenetic approach failed, one could then proceed with tissue removal. A caveat to this, however, is if opsin expression were to spread outside of the would-be-transected region, for example through retrograde transport of the viral vector (Kaemmerer, 2017). Importantly, even if optogenetics itself does not reach the clinic, the insight gained through research using optogenetic methods is certain to have a beneficial impact on patient care (Rajasethupathy et al., 2016).

Using epilepsy research as a focal point, we discuss in turn examples of how the specificity of optogenetics can be harnessed; the diverse ways in which optogenetics can be used in research; and finally, how newly developed and future tools can aid in addressing outstanding questions in the field.

Specificity

One of the major benefits of an optogenetic approach is the level of specificity of intervention one can achieve. Therapeutically, an intervention strategy which is highly restricted may be able to avoid unnecessary negative side effects and intervention-related complications. Therefore, one of the first epilepsy research questions tested using this tool was whether optogenetics—a highly restricted intervention strategy—could also be an effective intervention strategy. Epilepsy is often referred to as a network or circuit disorder (Bernhardt et al., 2015; Jin et al., 2015; Vaughan et al., 2016) to emphasize the importance of neuronal interactions in seizures. In part because of this “network” view, it was unclear if a highly restricted intervention strategy (targeting one cell type in one region) could effectively inhibit seizures. This fundamental question was tested through a variety of experimental approaches, and optogenetics was ultimately shown to be effective at inhibiting: (i) epileptiform activity in vitro (Tønnesen et al., 2009; Berglind et al., 2014; Sessolo et al., 2015); (ii) induced, acute seizures in non-epileptic animals (Sukhotinsky et al., 2013; Berglind et al., 2014; Chiang et al., 2014; Ladas et al., 2015; Lu et al., 2016; Soper et al., 2016); and (iii) spontaneous seizures in various rodent models of different forms of epilepsy (Wykes et al., 2012; Krook-Magnuson et al., 2013; Krook-Magnuson et al., 2014b; Krook-Magnuson et al., 2015a; Paz et al., 2013; Kros et al., 2015; Sorokin et al., 2017; Bui et al., 2018). Table 2 provides an overview of studies using in vivo optogenetics in the field of epilepsy research. Importantly, a number of studies have harnessed not only the spatial specificity and cell-type specificity of optogenetics, but also the tool’s temporal specificity by implementing a closed-loop approach (Figure 4; Armstrong et al., 2013; Grosenick et al., 2015; Krook-Magnuson et al., 2015b; Nagaraj et al., 2015). In these studies, on-line seizure detection allowed light delivery to be restricted to times of spontaneous seizures (referred to as on-demand optogenetics), providing an additional element of specificity to the interventions (Krook-Magnuson et al., 2013; Krook-Magnuson et al., 2014b; Krook-Magnuson et al., 2015a; Paz et al., 2013; Sorokin et al., 2017; Bui et al., 2018).

Optogenetic Neuromodulation Allows More Direct Testing of Hypotheses Through Selective Perturbation

The specificity of intervention possible through on-demand optogenetic techniques also provides an important research tool to test additional hypotheses related to epilepsy. For example, in the field of temporal lobe epilepsy (TLE) research there is a long-standing hypothesis called “the dentate gate hypothesis” (Heinemann et al., 1992; Lothman et al., 1992). In healthy control tissue, the dentate gyrus is thought to act as a gate; due to intrinsic and circuit level properties, dentate gyrus granule cells only show very sparse activation in response to entorhinal cortex input. The dentate gate hypothesis posits that, in TLE, there is a breakdown of this gating function, granule cells become relatively over-active, and too much excitation flows through the hippocampal circuit. Whether the excitatory drive pushing seizures arrives from the entorhinal cortex, some other outside source, or from the dentate itself is currently under consideration (Meyer et al., 2016; Krook-Magnuson, 2017). Regardless of the source of the excitatory drive, fundamental to the dentate gate hypothesis is that granule cells show heightened activity levels. Supporting the dentate gate hypothesis, there are a number of changes in epileptic tissue that would reduce inhibition and increase excitation of dentate gyrus granule cells (Nadler et al., 1980; Coulter, 1999; Bouilleret et al., 2000; Zhang et al., 2012).

While there is abundant correlative data showing an increase in dentate excitability in TLE, until recently, strong evidence indicating causality (rather than merely correlation) to support the dentate gate hypothesis was lacking; prior to the advent of optogenetic techniques, it was difficult to directly test the dentate gate hypothesis. With on-demand optogenetic techniques however, researchers were able to test causality rather than just correlation by artificially causing a breakdown of the dentate gate (by optogenetically exciting granule cells) and by “restoring” the dentate gate (by optogenetically inhibiting granule cells) at the time of spontaneous seizures in epileptic animals (Krook-Magnuson et al., 2015a; Table 2). In doing so, researchers took advantage of: (i) the cell type specificity (targeting just granule cells); (ii) the temporal specificity (restoring the gate selectively at the time of seizures); and (iii) the control over the direction of modulation (excitation vs. inhibition) achievable with optogenetic approaches. Additionally, the authors examined whether the location of inhibition or excitation of granule cells mattered by delivering light either ipsilateral or contralateral to where the “break-down of the gate” was thought to occur.

Supporting the dentate gate hypothesis, the authors found that on-demand optogenetic excitation of granule cells (“flooding the gate”) worsened seizures in epileptic animals and even induced seizures in non-epileptic animals (Krook-Magnuson et al., 2015a). These findings also highlight that optogenetics can be used not only to inhibit seizures, but also to induce seizures. Optogenetic induction of seizures is not unique to activation of granule cells, and several studies have used optogenetic approaches to induce seizures (Osawa et al., 2013; Weitz et al., 2015; Khoshkhoo et al., 2017; Table 2). However, activation of the dentate may still be fairly distinct in regard to generating seizures: it was recently reported that optogenetic activation of neurons to produce cortical seizures requires increasingly stronger activations, with subsequent light deliveries, in order to continue to produce seizures (Khoshkhoo et al., 2017; Table 2). In contrast, optogenetic excitation of dentate granule cells in control animals displayed a rapid kindling effect, such that each subsequent light delivery produced a larger, more severe, seizure (Krook-Magnuson et al., 2015a). Providing further support for the dentate gate hypothesis, using non-optogenetic methods to increase the excitability of dentate granule cells is also sufficient to induce epilepsy (Pun et al., 2012).

Perhaps the greatest optogenetic support for the dentate gate hypothesis, however, comes from the experiments which have not induced seizures, but rather inhibited seizures in chronically epileptic mice. Experimenters used optogenetics to restore the dentate gate by selectively inhibiting granule cells at the time of seizures. When this optogenetic restoration of the dentate gate occurred ipsilateral to the presumed seizure focus (i.e., where the breakdown of the gate would be expected to occur), focal seizures were immediately inhibited (Figures 4C,D). Interestingly, and in-line with the hypothesis, inhibition of granule cells in the contralateral dentate gyrus (where the native gate should still be functioning) had no effect on the seizures.

Together, the findings provide strong support for the dentate gate hypothesis and illustrate the power of using the cell-type selective and temporally precise neuromodulation achievable with on-demand optogenetic methods. It also demonstrates the importance of direct control of the direction of modulation (excitation vs. inhibition), which can sometimes be difficult with electrical stimulation methods. Finally, the seizure inhibition achieved by selectively inhibiting granule cells was comparable to the level of seizure inhibition achieved previously by broadly inhibiting excitatory cells in the hippocampal formation (Krook-Magnuson et al., 2013; Table 2). Therefore, these findings further illustrate that identifying key components of the network (in this case, dentate gyrus granule cells) can allow refinement of an intervention without sacrificing efficacy.

An additional example of using the specificity of in vivo on-demand optogenetics to test hypotheses in epilepsy research is provided by work conducted by Paz and colleagues (Table 2). Paz et al. (2013) found that after an experimentally induced cortical stroke, rats developed epilepsy. The researchers hypothesized that cortical stroke was inducing thalamocortical epilepsy (i.e., that the thalamus was critically engaged during these seizures). To test this hypothesis, Paz et al. (2013) used a viral vector with a CaMKIIα promoter to express the inhibitory chloride pump halorhodopsin (HR) in the ventrobasal thalamus ipsilateral to the cortical stroke. Light was then delivered in an on-demand fashion to inhibit thalamic neurons. Seizures were truncated by this approach, supporting the importance of the thalamus in cortical stroke-induced epilepsy.

More recently, Paz and colleagues have used optogenetic methods to further demonstrate that the firing pattern of thalamocortical cells is critically important for seizure control (Sorokin et al., 2017; Table 2). Rather than briefly inhibiting thalamocortical neurons in an on-demand manner at the time of seizures as done previously, the researchers examined the effect of using an excitatory stabilized step function opsin (SSFO, see Figure 1A and Box 1) in genetic models of absence epilepsy. Light delivery placed the thalamocortical neurons in a tonic mode of firing, and (just as with brief inhibition in stroke induced seizures) inhibited seizures.

In the same study, Paz and colleagues also examined the effect of inducing burst firing interictally (that is, when the animal was not actively seizing) in animals genetically prone to absence seizures (Sorokin et al., 2017). Using pulses of light to briefly and repeatedly inhibit thalamocortical cells (which have strong T-type calcium channel-mediated currents), researchers were able to induce rebound firing in thalamocortical cells, entrain their firing, and mimic a burst firing mode. Such pulsed light delivery was able to initiate absence seizures in these animals. Together, the work by Paz and colleagues indicates that in epileptic animals, manipulation directed at the thalamus is able to induce thalamocortical absence seizures, and that disruption of on-going activity of thalamocortical cells during seizures (either by gentle excitation to induce tonic firing or by brief direct inhibition) can be used to truncate thalamocortical seizures, underscoring the importance of the thalamus in this seizure network.

The Broader Seizure Network?

The examples discussed above demonstrate the utility of on-demand optogenetics in more directly testing the importance of circuit elements hypothesized to be critically engaged in seizure networks. That is, by directly manipulating the thalamus, Paz and colleagues were able to provide strong support for the causal role the thalamus can play in thalamocortical absence epilepsy (Paz et al., 2013; Sorokin et al., 2017). Likewise, the importance of the dentate gyrus in TLE was demonstrated through direct manipulation of dentate gyrus granule cells (Krook-Magnuson et al., 2015a). Optogenetic methods are also useful in examining circuit elements that are not typically considered to be part of the seizure network but may still be able to provide seizure control. In this context, we will briefly discuss work using optogenetic methods to examine the utility of targeting the cerebellum or deep layers of the superior colliculus for a variety of epilepsies.

The cerebellum is not typically associated with epilepsy, but in large part due to recent work, including optogenetic work, that viewpoint is changing. Specifically, recent studies (Table 2) have shown that on-demand optogenetic manipulation of the cerebellum can inhibit seizures in rodent models of both thalamocortical absence epilepsy (Kros et al., 2015) and TLE (Krook-Magnuson et al., 2014b). Surprisingly, on-demand optogenetic excitation or inhibition of the cerebellar cortex was able to inhibit temporal lobe seizures (Krook-Magnuson et al., 2014b), suggesting that simply disrupting cerebellar activity was able to terminate hippocampal seizures. This is perhaps somewhat akin to results examining the effects of manipulation of thalamic cells during thalamocortical seizures discussed above (Paz et al., 2013; Sorokin et al., 2017; Table 2). More recently, Kros et al. (2015) examined the effect of optogenetic activation of the cerebellum (in particular, deep cerebellar nucleus neurons) during absence seizures, and found that on-demand stimulation was able to also abort thalamocortical seizures. Taken together, these results are very exciting, and suggest a potentially broad applicability of targeting the cerebellum in various epilepsies. Such a target with broad applicability would be valuable in treating patients whose seizures have multiple, diffuse, uncertain, or progressing seizure foci—these patient populations are currently very difficult to treat through directed neuromodulation methods.

Interestingly, when Kros et al. (2015) took a pharmacological, rather than optogenetic, approach to manipulate the deep cerebellar nuclei in absence epilepsy, inhibition (through local application of the GABA_A receptor agonist muscimol) increased the likelihood of absence seizures (Kros et al., 2015). In contrast, and paralleling their optogenetic work, excitation (through application of the GABA_A antagonist gabazine) increased the firing frequency and regularity of deep cerebellar neurons and inhibited seizures. These findings, and previous work showing mixed results using open-loop electrical stimulation of the cerebellum in epilepsy (e.g., Cooper et al., 1973; Šramka et al., 1976; Van Buren et al., 1978; Levy and Auchterlonie, 1979; Wright et al., 1984; Davis and Emmonds, 1992; Chkhenkeli et al., 2004; Velasco et al., 2005), raise an important question: is the timing of intervention a critical factor? That is, if Kros et al. (2015) had used on-demand optogenetics to briefly and acutely inhibit deep cerebellar nucleus neurons, would they have worsened seizures (paralleling their pharmacological experiments; Kros et al., 2015), or instead inhibited seizures (paralleling the on-demand optogenetics work targeting the cerebellar cortex for TLE (Krook-Magnuson et al., 2014b), as well as the on-demand optogenetics work targeting the thalamus for thalamocortical epilepsy; Paz et al., 2013; Sorokin et al., 2017)? Fortunately, the temporal specificity achievable with optogenetic methods allows future examination of this important topic.

In these studies, the researchers not only used optogenetics to manipulate neuronal activity, they also used more traditional methods to record neuronal activity. Paralleling previous findings in a variety of seizure models (e.g., Fernandez-Guardiola et al., 1962; Julien and Laxer, 1974; Heath et al., 1980; Kandel and Buzsáki, 1993) as well as in some human patients (Niedermeyer and Uematsu, 1974), both groups found that activity in the cerebellum was modulated by seizures; both hippocampal temporal lobe seizures (Krook-Magnuson et al., 2014b) and thalamocortical absence seizures (Kros et al., 2015) were mirrored in the cerebellum. The ability to record spontaneous seizure activity in the cerebellum suggests that scientists and clinicians may need to reject the adage that the cerebellum “does not seize.” It may also warrant reconsideration of which brain regions are considered to be part of the core hippocampal and thalamocortical seizure networks. When targeting the cerebellum, are we targeting a region distinct from the seizure network which is merely able to inhibit seizures as an outsider? Or, is the cerebellum a core part of the seizure network? Optogenetics, in combination with other brain mapping tools, can help address these questions.

The cerebellum is certainly not the only brain region which has been examined as a potential site for inhibiting a variety of seizure types (Gale, 1992; Dybdal and Gale, 2000; Fisher et al., 2010; Connor et al., 2012; Valentín et al., 2013). One such brain region, which was recently examined using an optogenetic approach, is the superior colliculus—in particular, the deep/intermediate layers of the superior colliculus (Soper et al., 2016; Table 2). Optogenetic activation of neurons in the superior colliculus was able to inhibit seizures induced by: (i) systemic administration of pentylenetetrazole; (ii) focal bicuculline application to Area Tempestas in the piriform cortex; (iii) systemic administration of gamma butyrolactone; or (iv) acoustic stimulation in animals genetically prone to audiogenic seizures (Soper et al., 2016). These results support the ability of intervention targeted at the deep layers of the superior colliculus to be effective in a wide variety of seizure types. Notably, the superior colliculus is a downstream target of the cerebellum and in turn projects to areas that are also directly targeted by the cerebellum, suggesting that the two structures may be part of a unified seizure-controlling network (Yu and Krook-Magnuson, 2015; Zeidler and Krook-Magnuson, 2016). Optogenetics provides an important toolbox also for testing this possibility in future work.

Optogenetic Cell-Type Selectivity for Local Circuit Dissection

The examples discussed above utilized the specificity achievable with on-demand optogenetic techniques to gain insight into the circuitry of epilepsy and seizures at a fairly macroscopic level—the importance of the dentate gyrus or thalamocortical interactions, and the relevance of other regions not classically associated with specific seizure networks. However, the cell-type specificity achievable with optogenetic techniques also allows probing of local circuit elements and the roles of different interneuron populations in healthy physiology and in neurological disorders such as epilepsy. In epilepsy research to date, these optogenetic tools have been most extensively used to investigate parvalbumin-expressing (PV) inhibitory neurons.

PV interneurons are a heterogeneous group of fast spiking inhibitory neurons, which together make up less than 5% of hippocampal neurons (Woodson et al., 1989; Freund and Buzsáki, 1996; Bezaire and Soltesz, 2013). While relatively few in number, PV cells are known to have a powerful impact in healthy circuits (Cobb et al., 1995; Freund and Buzsáki, 1996; Hajos and Paulsen, 2009; Sohal et al., 2009). Their role in seizures, however, has become somewhat controversial (Cammarota et al., 2013; Ledri et al., 2014; Maguire, 2015; Yi et al., 2015; Jiang et al., 2016). Using on-demand optogenetics with light delivery to the hippocampus and a model of chronic TLE, it was demonstrated that activation of PV cells selectively at the time of seizures, and early during seizures (before they progressed to having overtly behavioral manifestations), was able to significantly reduce the duration of electrographic seizures and reduce the likelihood that a seizure would progress to becoming a behavioral seizure (Krook-Magnuson et al., 2013). Seizures were significantly inhibited when light was delivered to the hippocampus ipsilateral to the presumed seizure focus or, somewhat surprisingly, to the contralateral hippocampus (Figure 5). This suggests that activation of PV cells can be an effective approach to inhibiting seizures. However, work done with evoked seizures in healthy tissue suggests that the role of PV cells may be more complicated than simply always providing seizure control. The effect of activating PV cells may be dependent on the timing of their activation (Ellender et al., 2014; Assaf and Schiller, 2016), the relative location of PV cell activation compared to the seizure focus (Sessolo et al., 2015), the type of seizure (Shiri et al., 2015, 2016; Lu et al., 2016) and/or the brain region targeted and expression levels of NKCC1/KCC2 (Wang et al., 2017). More work examining the role of PV neurons during spontaneous seizures in vivo in epileptic animals, as well as work parsing the role of different types of PV interneurons (as discussed more below) would help clarify these issues.

Optogenetics is a powerful tool for providing new insight into the role of populations of cells in neurological disorders. However, in the case of determining the role of PV interneurons during seizures, and to some extent interneurons more broadly, optogenetics has so far produced more questions than answers (Chiang et al., 2014; Ledri et al., 2014; Ladas et al., 2015; Yekhlef et al., 2015; Shiri et al., 2016; Khoshkhoo et al., 2017). Luckily, optogenetics also provides the opportunity to address these questions more thoroughly moving forward.

Versatility

The optogenetic toolbox is filled with a wide variety of tools (Box 1; Figure 1) which can be used for different purposes to distinct experimental ends. Moreover, optogenetic techniques are typically relatively easy to implement in conjunction with more traditional methods, further increasing the versatility of use of optogenetic techniques. As discussed above, in the context of epilepsy research, optogenetic methods have been used to study networks in epilepsy through both seizure inhibition and seizure induction. To date, this has relied primarily on the widely used opsins HR and ChR2 (an overview of optogenetic methods currently used in in vivo epilepsy research is included in Table 2), and a full harnessing of the varied tools (Box 1) available is still to come, as discussed more in the “Further Development and Application” section below. Importantly, optogenetics has also been used in epilepsy research to monitor changes in networks in epilepsy and with interventions. To illustrate the versatility of optogenetics, we discuss here some of the ways optogenetics has been paired with other techniques to gain these insights. While our discussion focuses on epilepsy research, it is important to note that these methods are more broadly applicable (Table 1); for example, optogenetics has not only been used to track changes in neuronal circuits in epileptic tissue, but also following stroke (Lim et al., 2014; listed in Table 1).

Axonal sprouting is a key example of network reorganization that can occur in epilepsy. A classic example of this in TLE is axonal sprouting of dentate granule cells (i.e., mossy fiber sprouting; Laurberg and Zimmer, 1981; Tauck and Nadler, 1985; Sutula et al., 1989; Buckmaster and Dudek, 1997; Althaus et al., 2016). Inhibitory axonal sprouting can also occur (Mathern et al., 1995; Zhang et al., 2009; Peng et al., 2013; Soussi et al., 2015; Christenson Wick et al., 2017), and this is one area in which optogenetic techniques have been paired with more traditional methods of studying anatomical reorganization. Recently, Peng et al. (2013) selectively expressed the excitatory opsin ChR2 in SOM cells of CA1 by injecting a Cre-dependent virus (Figure 3Aii) into the dorsal hippocampus of healthy and epileptic SOM-Cre mice. Because the expression of the virus was limited to SOM cells in the stratum oriens of CA1 and because the opsin was fluorescently tagged, the authors were able to clearly view the axonal arbors of SOM cells in healthy and epileptic mice. Upon visualizing the fluorescently labeled axonal arbors, Peng et al. (2013) found that SOM cells displayed axonal sprouting in epileptic mice. In healthy mice, the axonal arbors of labeled SOM cells in CA1 stopped rigidly before the hippocampal fissure—not crossing into the dentate. However, in epileptic mice, labeled SOM cells in CA1 had axonal arbors that crossed the hippocampal fissure, invading the dentate gyrus. Using optogenetics to activate labeled cells, and whole cell patch clamp methods to record from dentate granule cells in hippocampal slices, Peng et al. (2013) confirmed that these sprouted axons made functional connections onto dentate granule cells in the epileptic mice. More recently, work by others has shown that some CA1 SOM cells have axons that cross into the dentate in non-epileptic tissue (Katona et al., 2017; Szabo et al., 2017), suggesting the possibility that the specific population of CA1 SOM cells targeted may be a critical factor. An additional experimental caveat to consider in using optogenetic approaches in epilepsy research is that seizures can alter the traditional expression profile of markers including somatostatin, which can be transiently expressed in principal cells following seizures (Drexel et al., 2012). As discussed in a later section, intersectional approaches (Figure 3iii) may help alleviate both of these experimental considerations in future work. Importantly, in the study by Peng et al. (2013), an optogenetic approach, in combination with more traditional methods, allowed the demonstration of a novel form of plasticity in chronically epileptic tissue. Additionally, invasion of inhibitory axons from CA1 SOM neurons into the dentate gyrus, including in epileptic tissue, highlights that not only may local inhibitory neurons be harnessed to reinforce the dentate “gate,” but also that inhibitory input from other regions invading the dentate may also be able to help support the gate. In this context, recent work from our laboratory looking at axonal sprouting of PV cells is of interest.

As noted above, it was previously demonstrated that optogenetic activation of PV cells contralateral to the presumed seizure focus significantly inhibited temporal lobe seizures (Krook-Magnuson et al., 2013). This was a somewhat surprising finding, as directly inhibiting granule cells in the contralateral hippocampus had no effect on the seizures (Krook-Magnuson et al., 2015a). There are several possibilities for why activating contralateral PV cells might produce seizure inhibition when direct inhibition of contralateral granule cells does not. One such potential explanation was that PV cells, despite being typically thought of as local interneurons, might project to the contralateral hippocampus (Goodman and Sloviter, 1992; Zappone and Sloviter, 2001), and provide “outsider” inhibition (somewhat akin to SOM cells invading the dentate from CA1). To test this, we injected the retrograde tracer Fluorogold into the hippocampus of control (non-epileptic) and kainate-injected (to induce TLE) mice (Christenson Wick et al., 2017). In control animals, we found only very sparse Fluorogold labeling in contralateral PV cells (~0.5% of contralateral PV cells displayed Fluorogold labeling). In animals injected with kainate 6 months earlier, however, we found a significant increase in Fluorogold-labeled contralateral PV neurons (Figure 5E). To further examine this issue, we used viral injection to achieve Cre-dependent expression of enhanced yellow fluorescent protein (eYFP) in PV cells, allowing visualization of their contralaterally projecting fibers (Christenson Wick et al., 2017). We found long-range sprouting of this commissural inhibitory connection 6 months post-kainate injection (Figure 5F). Specifically, dramatic increases (greater than 100× increase) in labeled fiber lengths were found in the dentate gyrus, further supporting the relevance of this area (and inhibition to it) in TLE. As these studies illustrate, the versatility and flexibility of optogenetic tools to work alongside currently existing well-established techniques greatly increases the power of optogenetics to allow relatively easy expansion on previous findings, including in the domain of circuit changes in epilepsy.

Circuit reorganization can also occur following treatment approaches for epilepsy, including cell transplantation. Here, too, optogenetics can be useful. Specifically, it has been shown that transplantation of interneurons or of progenitors of GABAergic neurons into adult epileptic mice can reduce seizures (Hunt et al., 2013; Cunningham et al., 2014; Henderson et al., 2014; Hunt and Baraban, 2015). When such transplants express opsins, and standard slice electrophysiology techniques are combined with optogenetics, it is possible to demonstrate that the transplanted cells integrate into the circuit and provide synaptic inhibition to endogenous excitatory cells (Avaliani et al., 2014; Cunningham et al., 2014; Henderson et al., 2014; Hsieh and Baraban, 2017). Again, it is worth noting that such research is certainly not limited to the field of epilepsy. For example, optogenetics to study the integration of grafted cells has been used to examine integration of stem cell-derived dopaminergic neurons in a model of Parkinson’s disease (Tønnesen et al., 2011; Table 1). Further illustrating the relative ease at which optogenetics can be combined with other approaches, opsin expression in transplanted neurons was recently used alongside functional magnetic resonance imaging (fMRI; when paired with optogenetics, referred to as ofMRI) to examine larger-scale integration of transplants (Weitz and Lee, 2016).

The technique of ofMRI has also been used in epilepsy research to examine seizure dynamics across brain areas (Duffy et al., 2015; Choy et al., 2017). While ofMRI research so far has focused on using optogenetics to induce seizures in the scanner, future research could use ofMRI to examine seizure networks with spontaneous (rather than evoked) seizures in epileptic animals and use optogenetics to inhibit those seizures to examine seizure suppressive networks. This would be useful, for example, in gaining a better understanding of the networks engaged by interventions targeting the superior colliculus or the cerebellum. Indeed, ofMRI has recently been applied to cerebellar optogenetic stimulation in healthy animals (Choe et al., 2018). As a side note, ofMRI work also provides an excellent example of the need to always have appropriate experimental controls, and specifically the possibility of off-target effects of light; a recent report suggests that light itself can alter blood flow (Rungta et al., 2017).

With appropriate consideration of activation spectra and other methodological considerations, optogenetics can also be combined with cellular level imaging techniques such as calcium imaging. Khoshkhoo et al. (2017) paired optogenetically induced cortical seizures with fiber photometry in order to monitor the activity of different populations of neurons contralateral to the hemisphere receiving the optogenetic activation. In particular, they examined PV, SOM, vasoactive intestinal peptide-expressing (VIP) neurons and excitatory neurons. They found that evoked seizures produced significant increases in calcium signals for all cell types studied, with inhibitory populations showing a calcium signal increase coinciding with electrographic seizure onset, and excitatory cells showing increases after several seconds of lag. For these optogenetically induced seizures, PV and SOM calcium signals remained high for the duration of the seizure events. In addition to optogenetically inducing seizures, Khoshkhoo et al. (2017) used optogenetics to then investigate the effect of inhibiting populations of neurons on evoked seizures and found mixed effects. An additional method to track the activity of neuronal populations during seizures, or potentially across the development of epilepsy itself, is “opto-tagging.” For this, optogenetics is paired with unit recordings, with responsiveness to light being used to determine the cell-type, supplementing putative identification based on waveforms or firing patterns. This use has led to the development of optetrodes, capable of delivering light while performing multichannel recordings (Anikeeva et al., 2011). Relevant to epilepsy research, opto-tagging allows in vivo monitoring of the activity of specific cell types with higher temporal resolution than, for instance, calcium imaging. In addition, recordings with optetrodes provide details on how the optogenetic manipulation is affecting the cells’ activity (i.e., is the cell entraining to the stimulus; is the cell rebounding after photostimulation; has the chloride potential shifted such that chloride channel opsins are depolarizing rather than hyperpolarizing, etc.).

The examples discussed above demonstrate how optogenetics can be used in flexible ways and in combination with different technologies to illustrate its versatility. However, given the large diversity of optogenetic tools currently available (Box 1), and the continued development of new tools, the use of optogenetics in epilepsy research to date represents only a small fraction of what is possible. Continued development, alongside the use of the more varied tools, will allow epilepsy research to fully harness the versatility of optogenetic approaches. These issues are considered below.

Further Development and Application

There are several areas in which optogenetic techniques are being further developed. These include: (1) the light-sensitive proteins themselves and what they couple to; (2) methods for delivering light; and (3) methods for improving selectivity of expression (see also Box 2). Box 1 discusses classical opsins as well as more recently developed tools. Methods for delivering light, beyond traditional optical fibers, include μLEDs (Kim et al., 2013), transparent microprobe arrays (Lee et al., 2015), and wireless devices (for reviews, see Warden et al., 2014; Gutruf and Rogers, 2018). Light delivery approaches also include methods which take a fundamentally different approach, such as luminopsins (Tung et al., 2015) or upconversion nanoparticles (Chen et al., 2018; both briefly discussed in Box 1). A combination of fully harnessing available tools and development of new tools will allow optogenetics to make a significant impact on epilepsy research moving forward. Here, we discuss some ways in which newly developed, and not-yet developed, tools could be applied to epilepsy research to further the field, particularly focusing on improved cell-type specific targeting.

Increased cell-type specific targeting is certain to have a major impact on epilepsy research. For example, relatively little work has been done examining SOM-expressing inhibitory neurons in epileptic tissue (the work by Peng et al., 2013 discussed above is a notable exception). One major reason for this dearth of research is that GABAergic SOM neurons are difficult to selectively label and manipulate in animal models of epilepsy that display transient SOM expression in pyramidal cells (Hashimoto and Obata, 1991; Drexel et al., 2012). Use of Cre-based systems (Figure 3), in which opsin expression is Cre-dependent and Cre-expression is placed under the SOM promoter is problematic in these circumstances, as even transient expression of SOM (and thus Cre) in pyramidal cells is sufficient to irreversibly induce expression of the opsin in these excitatory cells. Therefore, maintenance of selective expression in inhibitory SOM neurons is compromised.

However, the increasing availability of Cre- and Flp-recombinase expressing mouse lines and systems (Taniguchi et al., 2011; Madisen et al., 2012, 2015; Tang et al., 2015; He et al., 2016) and recent advances in intersectional approaches for the restricted expression of opsins (Figure 3iii; Fenno et al., 2014) finally allow for more selective targeting and manipulation of cell populations. Importantly, these intersectional tools can aid in maintaining consistent selective expression in epileptic tissue. For instance, by crossing SOM-Cre mice with Dlx5/6-Flp mice, one can produce mice in which inhibitory SOM neurons express both Cre- and Flp-recombinase. Then, to selectively target and manipulate these neurons in a given brain region, one could inject an INTRSECT virus expressing the opsin of choice in a manner dependent on both Cre- and Flp-mediated recombination (Con/Fon; Figure 3iii). In this situation, transient expression of SOM in non-GABAergic populations would not compromise specificity of expression, as non-GABAergic populations would not express Flp and therefore not express the opsin. Therefore, through application of already available tools, the epilepsy field can harness optogenetic techniques to study populations of cells previously difficult to selectively target.

While the example above focuses on SOM cells in epileptic tissue, such intersectional approaches can also be used to examine other GABAergic populations that have been traditionally difficult to study, such as cholecystokinin-expressing (CCK) or neuronal nitric oxide synthase-expressing (nNOS) interneurons. While CCK and nNOS are both used as markers of certain populations of interneurons (Freund and Buzsáki, 1996; Armstrong et al., 2012), both are also natively expressed in excitatory principal cells (Burette et al., 2002; Lee and Soltesz, 2011). Therefore, targeting these cell populations with a traditional Cre-based system is insufficient. This has been a major problem for the field, but one which is now relatively straightforward to overcome; all the necessary tools are available to optogenetically excite populations of interneurons defined by their expression of molecular markers such as CCK or nNOS.

However, an additional hurdle for the field, alluded to earlier, is that neuronal markers such as SOM, PV, CCK or nNOS are fairly broad markers, with several distinct cell-types encompassed by each marker. For example, PV is expressed in hippocampal fast-spiking perisomatic targeting basket cells, in some dendritically targeting bistratified and oriens-lacunosum moleculare (O-LM) cells, and in axon-initial-segment targeting chandelier (axo-axonic) cells (Freund and Buzsáki, 1996; Bezaire and Soltesz, 2013). Each of these neuronal populations target different segments of the principal cell and may even have diametrically opposing effects on the postsynaptic cell. For instance, axo-axonic cells may actually increase the firing of the postsynaptic cell (Szabadics et al., 2006). Moving forward, it will be critically important for the field to gain better selectivity—to stop targeting “PV cells,” and start targeting more specifically defined cell populations. This may also help clarify the different outcomes seen when targeting broadly defined PV cells in epilepsy research (discussed above). Here too, intersectional approaches can be of great value. For example, in addition to expressing PV, bistratified and O-LM cells can express SOM (Freund and Buzsáki, 1996). Therefore, an intersectional approach, using PV-Flp or SOM-Flp mice in combination with SOM-Cre or PV-Cre mice, respectively, and INTRSECT viruses (Con/Fon, Coff/Fon, Con/Foff, Coff/Foff) can limit expression to subpopulations of PV cells (Fenno et al., 2014). Despite the progress this represents, selective targeting of just PV basket cells, for example, is not yet achievable, and will require the continued development of new tools. The need for continued development of new tools includes tools and insight not directly associated with optogenetics. For example, new mouse lines based on cell-type specific expression profiles gleamed through single-cell transcriptomics can provide important new avenues to specific opsin expression: a recent study identified neuron-derived neurotropic factor (Ndnf) expression in cortical neurogliaform cells, and then produced a Cre-line to allow targeting of this neuronal population (Tasic et al., 2016). While not directly an optogenetic development, this insight and new tool (the Ndnf-Cre line) allows the expansion of optogenetic methods. The same study which identified Ndnf-expression in cortical neurogliaform cells suggested that PV cells expressing Cpne5 (copine 5, a calcium-dependent protein) may correspond to axo-axonic cells (Tasic et al., 2016), which could provide a genetic handle for separating different classes of PV interneurons in the future.

In addition to gene-expression profiles, cell populations can also be segregated by projection targets. While the discussion above has focused on the use of intersectional approaches to improve cell-type specificity through the use of Flp and Cre mouse lines, intersectional approaches can be used in combination with other methods, including to selectively express opsins in cell populations defined by their projection targets. This development increases optogenetics’ power for exploring circuit-level interactions between brain regions in health and disease. For example, Cre and/or Flp expression can be achieved through retrograde expression systems, such as CAV2 (Junyent and Kremer, 2015), modified rabies (Wall et al., 2010; Chatterjee et al., 2018), or AAV2-retro (Tervo et al., 2016; Box 2, Figure 6). In this manner, dopaminergic neurons in the VTA that project to the nucleus accumbens (NAc) were selectively targeted in the original INTRSECT publication (Fenno et al., 2014). One potential application for an optogenetic examination of projection-defined neuronal populations in epilepsy is to explore the distinct contributions of deep vs. superficial CA1 pyramidal cells to epileptic neural activity, including seizure spread or generalization of spikes (Sheybani et al., 2018). Deep vs. superficial CA1 pyramidal cells project to distinct areas (Slomianka et al., 2011), interact with distinct local circuit elements (Krook-Magnuson et al., 2012; Lee et al., 2014), and display different neuromodulatory pathways, including via cannabinoid type-1 receptors (Maroso et al., 2016). For a review on CA1 pyramidal cell diversity, see reference (Soltesz and Losonczy, 2018). As deep and superficial pyramidal cells have distinct projection targets, it is possible to use a retrograde expression system to selectively label and manipulate just one population of pyramidal cells. This would not necessarily require an intersectional approach, but by combining retrograde-expression with an additional restriction (e.g., promoter-based Cre expression), opsin expression could be further limited. Such an approach would be useful, for example, in studying GABAergic projection neurons in situations where the target region is also targeted by excitatory principal neurons, such as entorhinal cortex inputs to the hippocampus (Melzer et al., 2012; Basu et al., 2016).

In addition to increased opsin delivery tools, increases in the availability of diverse light-sensitive proteins make it possible to combine two distinct optogenetic approaches within the same animal. For instance, the increasing availability of good red-shifted and blue-light activated opsins (Figure 2, Box 1) allow for simultaneous/dual-opto experiments in which two cell populations can be independently targeted and then independently manipulated. Building on the discussion of different targeting approaches, this dual-opto approach could be applied to epilepsy research in several ways. For instance, one could retrogradely target, and then manipulate, deep and superficial CA1 pyramidal cells independently, in the same animal. To do this, one could inject a CAV (or other retrograde) virus carrying a blue-light activated opsin (e.g., Chronos) into the NAc (to target deep pyramidal cells; McGeorge and Faull, 1989) and inject a CAV virus carrying a red-shifted opsin (such as Chrimson) into the medial temporal cortex (mTC; to target superficial pyramidal cells; Slomianka et al., 2011; Figure 6). Because the activation spectra of these two opsins are sufficiently distinct, one could activate one opsin without activating the other by using different wavelengths of light. With such an approach, one could examine if a given postsynaptic cell within CA1 was targeted by both populations of pyramidal cells, or selectively by one population or the other, and how synaptic reorganization associated with epilepsy might disrupt deep and superficial pyramidal cell wiring. To date INTRSECT has only been published for fluorescent proteins and ChR2 (Fenno et al., 2014). With further development of INTRSECT vectors to include additional opsins, INTRSECT approaches could be applied in dual-opto situations or in cases benefiting from inhibitory opsins.

The examples provided here are merely examples—the opportunities for combinatorial approaches that current and developing optogenetic tools provide are nearly endless. Clearly, optogenetic approaches are experimentally powerful, and will continue to be so moving forward.

While we have focused on the possibility for expansion of cell-type specific targeting of optogenetic methods in epilepsy research, and while the majority of optogenetic-aided epilepsy research (Table 2), and to some extent optogenetic-aided study of neurological disorders more broadly (Table 1), has been focused at the circuit level, use of optogenetics for molecular-level research is becoming increasingly possible, and deserves a brief discussion here. Indeed, several optogenetic tools are already available that would be very useful for studying neurological disorders, including epilepsy, from a molecular vantage (Box 1). These tools include light-activated G-protein coupled receptors (Opto-XRs; Airan et al., 2009), as well as a more diverse and ever-expanding tool box of specialized light-sensitive proteins.

One line of optogenetic tools which may be particularly useful for investigations at a more molecular level are systems in which light induces association or dissociation of proteins (Figure 1F, Box 1). There are a variety of these, including OptoSOS and the Phy-PIF system (Toettcher et al., 2013), LITEs and the CRY2-CIB1 system (Konermann et al., 2013), mutated Dronpa-based caging (Zhou et al., 2012), VP-EL222 and light-oxygen-voltage (LOV) domains (Motta-Mena et al., 2014), LOVTRAP (Wang et al., 2016), and photoactivatable Cre (Kawano et al., 2016). A recently developed system, PhoCl, is somewhat similar but rests on light-induced cleavage of a protein, making for a direct irreversible alteration by light (Zhang et al., 2017). The availability and advances to tools using light-induced alterations in protein-protein associations (or photocleavage of proteins) increases the possibilities for how optogenetics can be used to study the nervous system more broadly, by directly influencing the activity of intracellular proteins. As with other optogenetic approaches, these approaches can be used with the temporal and spatial specificity afforded through the use of light, and the cell-type specificity achievable through genetic encoding. Another use for systems in which light alters protein-protein interactions is to directly alter gene expression (e.g., LITEs (Konermann et al., 2013) and VP-EL222 (Motta-Mena et al., 2014)) or DNA itself (e.g., through a photoactivatable Cre system (Kawano et al., 2016)). Such light-based approaches may be able to achieve greater temporal specificity and reduced complications compared to more traditional methods like the Tetracycline/tamoxifen systems (Saunders, 2011). These tools are currently available, but not yet adopted in epilepsy research.

Additionally, the continued development and sharing of optogenetic tools, including optogenetic pharmacological tools, increases the potential for optogenetics as a strategy in cases where pharmacological tools may be lacking. In optogenetic pharmacology, a receptor (or subunit of a receptor) of interest is modified (e.g., through a single cysteine substitution) to allow a chemical photoswitch to (selectively and irreversibly) tether, thus combining genetic and chemical approaches. The photoswitches can be engineered to act as either an agonist or antagonist of the receptor in the absence or presence of a given wavelength of light. Previously, optogenetic pharmacology was available for various potassium channels (e.g., “SPARK” (Banghart et al., 2004)), glutamate receptors (e.g., “LiGluR” (Volgraf et al., 2006)) and acetylcholine receptors (e.g., “MAACh” (Tochitsky et al., 2012)). An optogenetic pharmacology approach was also recently developed for GABA_A receptors (Lin et al., 2015). While the use of optogenetic pharmacology methods are arguably less straightforward than some other approaches, it offers the unique possibility of manipulating a select type of receptor in a particular cell type, in a particular brain region, at a particular time, and could be useful in the study of subunit specific contributions to epilepsy.

In addition to these optogenetic pharmacological tools, there have also been developments in other light-sensitive tools that may be useful where traditional pharmacological tools are currently lacking. Recently, Zhang et al. (2017) developed a photoactivatable version of the large-pore channel pannexin-1, which they termed Opto-Panx1. This tool could be directly relevant to research investigating pannexins or ATP release (as Opto-Panx1 activation induces ATP release) in health or disease states. Relevant to epilepsy research, ATP release is known to propagate glial calcium waves (Cotrina et al., 1998), which have been suggested to play a role in seizures (Gómez-Gonzalo et al., 2010). Also of note, glial calcium waves are blocked by some conventional anti-epileptic drugs (Tian et al., 2005). The potential impact of photocleavable proteins extends beyond pannexins and ATP, however. For an example, consider connexins. Connexins and the role of gap junctions in epilepsy is notoriously difficult to study owing to the lack of drugs that specifically and cleanly target connexins. While no Opto-connexin yet exists, one can easily imagine the potential uses for such a tool, and this is therefore an example of how continued tool development can aid future epilepsy research.

In addition to tools specifically designed to manipulate cellular processes at a molecular level, the currently widely available tools typically used for circuit-level investigations have been cleverly used in some instances for the study of more molecular phenomena. For example, in a stroke study by Beppu et al. (2014; listed in Table 1), the authors used ChR2(C128S) to induce inward flow of H+, and the inhibitory opsin ArchT to pump H+ out of the cell, in order to manipulate pH in glial cells. By optogenetically manipulating the acidity of glia in this manner, the authors demonstrated an impact on neurodegeneration at the site of ischemia. This is additionally an example of how considering how optogenetics has been used outside of the field of epilepsy (Table 1) could inspire future uses of optogenetics in epilepsy research.

The optogenetic tools available, both in terms of the opsins themselves and methods to achieve selective opsin expression, are rapidly expanding. As with any quickly growing field (or arguably any method), and discussed more in Box 1, optogenetics has limitations and considerations that must be acknowledged and controlled for in experimental design, including controlling for non-specific effects of light (including heat or sensory stimulation); expression of Cre, Flp, fluorescent proteins, or opsins themselves; potential effects of viral-based delivery; and “off-target” effects, including changes in ionic concentrations. Moreover, development of new tools is only one step in the process; the application of the tools to epilepsy research will depend in large part on the continued broad availability of the tools, and thus relies on tool developers making the conscious decision to make their tools widely available. With the specificity achievable with optogenetics, the versatility of optogenetic approaches, and the continued development (and sharing) of new tools, optogenetics will have a lasting impact on epilepsy research.

Conclusion

Optogenetics is a quickly growing field that provides researchers with the ability to manipulate neurons with unprecedented specificity. It is also a versatile tool, easily used in combination with other techniques to answer diverse experimental questions. Continued development and sharing of new methods, including for selective opsin expression, ensure the further expansion of optogenetics’ utility in epilepsy research.

References

• 1

  AiranR. D.ThompsonK. R.FennoL. E.BernsteinH.DeisserothK. (2009). Temporally precise in vivo control of intracellular signalling. Nature458, 1025–1029. 10.1038/nature07926

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 2

  AlfonsaH.MerricksE. M.CodaduN. K.CunninghamM. O.DeisserothK.RaccaC.et al. (2015). The contribution of raised intraneuronal chloride to epileptic network activity. J. Neurosci.35, 7715–7726. 10.1523/JNEUROSCI.4105-14.2015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 3

  AlthausA. L.ZhangH.ParentJ. M. (2016). Axonal plasticity of age-defined dentate granule cells in a rat model of mesial temporal lobe epilepsy. Neurobiol. Dis.86, 187–196. 10.1016/j.nbd.2015.11.024

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4

  AnikeevaP.AndalmanA. S.WittenI.WardenM.GoshenI.GrosenickL.et al. (2011). Optetrode: a multichannel readout for optogenetic control in freely moving mice. Nat. Neurosci.15, 163–170. 10.1038/nn.2992

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5

  ArmstrongC.Krook-MagnusonE.OijalaM.SolteszI. (2013). Closed-loop optogenetic intervention in mice. Nat. Protoc.8, 1475–1493. 10.1038/nprot.2013.080

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6

  ArmstrongC.Krook-MagnusonE.SolteszI. (2012). Neurogliaform and Ivy cells: a major family of nNOS expressing GABAergic neurons. Front. Neural Circuits6:23. 10.3389/fncir.2012.00023

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  AssafF.SchillerY. (2016). The antiepileptic and ictogenic effects of optogenetic neurostimulation of PV-expressing interneurons. J. Neurophysiol.116, 1694–1704. 10.1152/jn.00744.2015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8

  AtasoyD.AponteY.SuH. H.SternsonS. M. (2008). A FLEX switch targets Channelrhodopsin-2 to multiple cell types for imaging and long-range circuit mapping. J. Neurosci.28, 7025–7030. 10.1523/JNEUROSCI.1954-08.2008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9

  AvalianiN.SorensenA. T.LedriM.BengzonJ.KochP.BrüstleO.et al. (2014). Optogenetics reveal delayed afferent synaptogenesis on grafted human-induced pluripotent stem cell-derived neural progenitors. Stem Cells32, 3088–3098. 10.1002/stem.1823

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10

  BamannC.GuetaR.KleinlogelS.NagelG.BambergE. (2010). Structural guidance of the photocycle of channelrhodopsin-2 by an interhelical hydrogen bond. Biochemistry49, 267–278. 10.1021/bi901634p

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  BanghartM.BorgesK.IsacoffE.TraunerD.KramerR. H. (2004). Light-activated ion channels for remote control of neuronal firing. Nat. Neurosci.7, 1381–1386. 10.1038/nn1356

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12

  BasuJ.ZarembaJ. D.CheungS. K.HittiF. L.ZemelmanB. V.LosonczyA.et al. (2016). Gating of hippocampal activity, plasticity, and memory by entorhinal cortex long-range inhibition. Science351:aaa5694. 10.1126/science.aaa5694

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  BeppuK.SasakiT.TanakaK. F.YamanakaA.FukazawaY.ShigemotoR.et al. (2014). Optogenetic countering of glial acidosis suppresses glial glutamate release and ischemic brain damage. Neuron81, 314–320. 10.1016/j.neuron.2013.11.011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14

  BerglindF.LedriM.SørensenA. T.NikitidouL.MelisM.BielefeldP.et al. (2014). Optogenetic inhibition of chemically induced hypersynchronized bursting in mice. Neurobiol. Dis.65, 133–141. 10.1016/j.nbd.2014.01.015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  BerndtA.LeeS. Y.RamakrishnanC.DeisserothK. (2014). Structure-guided transformation of channelrhodopsin into a light-activated chloride channel. Science344, 420–424. 10.1126/science.1252367

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16

  BerndtA.LeeS. Y.WietekJ.RamakrishnanC.SteinbergE. E.RashidA. J.et al. (2016). Structural foundations of optogenetics: determinants of channelrhodopsin ion selectivity. Proc. Natl. Acad. Sci. U S A113, 822–829. 10.1073/pnas.1523341113

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17

  BerndtA.SchoenenbergerP.MattisJ.TyeK. M.DeisserothK.HegemannP.et al. (2011). High-efficiency channelrhodopsins for fast neuronal stimulation at low light levels. Proc. Natl. Acad. Sci. U S A108, 7595–7600. 10.1073/pnas.1017210108

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 18

  BerndtA.YizharO.GunaydinL. A.HegemannP.DeisserothK. (2009). Bi-stable neural state switches. Nat. Neurosci.12, 229–234. 10.1038/nn.2247

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 19

  BernhardtB. C.BonilhaL.GrossD. W. (2015). Network analysis for a network disorder: the emerging role of graph theory in the study of epilepsy. Epilepsy Behav.50, 162–170. 10.1016/j.yebeh.2015.06.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20

  BezaireM. J.SolteszI. (2013). Quantitative assessment of CA1 local circuits: knowledge base for interneuron-pyramidal cell connectivity. Hippocampus23, 751–785. 10.1002/hipo.22141

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21

  BouilleretV.LoupF.KienerT.MarescauxC.FritschyJ. M. (2000). Early loss of interneurons and delayed subunit-specific changes in GABA_A-receptor expression in a mouse model of mesial temporal lobe epilepsy. Hippocampus10, 305–324. 10.1002/1098-1063(2000)10:3<305::aid-hipo11>3.3.co;2-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 22

  BoydenE. S. (2015). Optogenetics and the future of neuroscience. Nat. Neurosci.18, 1200–1201. 10.1038/nn1215-1862b

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23

  BoydenE. S.ZhangF.BambergE.NagelG.DeisserothK. (2005). Millisecond-timescale, genetically targeted optical control of neural activity. Nat. Neurosci.8, 1263–1268. 10.1038/nn1525

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24

  BuckmasterP. S.DudekF. E. (1997). Neuron loss, granule cell axon reorganization, and functional changes in the dentate gyrus of epileptic kainate-treated rats. J. Comp. Neurol.385, 385–404. 10.1002/(sici)1096-9861(19970901)385:3<385::aid-cne4>3.3.co;2-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25

  BuiA. D.NguyenT. M.LimouseC.KimH. K.SzaboG. G.FelongS.et al. (2018). Dentate gyrus mossy cells control spontaneous convulsive seizures and spatial memory. Science359, 787–790. 10.1126/science.aan4074

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  BuretteA.ZabelU.WeinbergR. J.SchmidtH. H.ValtschanoffJ. G. (2002). Synaptic localization of nitric oxide synthase and soluble guanylyl cyclase in the hippocampus. J. Neurosci.22, 8961–8970. 10.1523/JNEUROSCI.22-20-08961.2002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27

  CammarotaM.LosiG.ChiavegatoA.ZontaM.CarmignotoG. (2013). Fast spiking interneuron control of seizure propagation in a cortical slice model of focal epilepsy. J. Physiol.591, 807–822. 10.1113/jphysiol.2012.238154

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28

  ChangM.DianJ. A.DufourS.WangL.Moradi ChamehH.RamaniM.et al. (2018). Brief activation of GABAergic interneurons initiates the transition to ictal events through post-inhibitory rebound excitation. Neurobiol. Dis.109, 102–116. 10.1016/j.nbd.2017.10.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29

  ChatterjeeS.SullivanH. A.MacLennanB. J.XuR.HouY.LavinT. K.et al. (2018). Nontoxic, double-deletion-mutant rabies viral vectors for retrograde targeting of projection neurons. Nat. Neurosci.21, 638–646. 10.1038/s41593-018-0091-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30

  ChenS.WeitemierA. Z.ZengX.HeL.WangX.TaoY.et al. (2018). Near-infrared deep brain stimulation via upconversion nanoparticle-mediated optogenetics. Science359, 679–684. 10.1126/science.aaq1144

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31

  ChiangC. C.LadasT. P.Gonzalez-ReyesL. E.DurandD. M. (2014). Seizure suppression by high frequency optogenetic stimulation using in vitro and in vivo animal models of epilepsy. Brain Stimul.7, 890–899. 10.1016/j.brs.2014.07.034

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32

  ChkhenkeliS. A.SramkaM.LortkipanidzeG. S.RakviashviliT. N.BregvadzeE.MagalashviliG. E.et al. (2004). Electrophysiological effects and clinical results of direct brain stimulation for intractable epilepsy. Clin. Neurol. Neurosurg.106, 318–329. 10.1016/j.clineuro.2004.01.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33

  ChoeK.SanchezC.HarrisN.OtisT.MathewsP. (2018). Optogenetic fMRI and electrophysiological identification of region-specific connectivity between the cerebellar cortex and forebrain. Neuroimage173, 370–383. 10.1016/j.neuroimage.2018.02.047

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  ChowB. Y.HanX.DobryA. S.QianX.ChuongA. S.LiM.et al. (2010). High-performance genetically targetable optical neural silencing by light-driven proton pumps. Nature463, 98–102. 10.1038/nature08652

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35

  ChoyM.DuffyB. A.LeeJ. H. (2017). Optogenetic study of networks in epilepsy. J. Neurosci. Res.95, 2325–2335. 10.1002/jnr.23767

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36

  Christenson WickZ.LeintzC. H.XamonthieneC.HuangB. H.Krook-MagnusonE. (2017). Axonal sprouting in commissurally projecting parvalbumin-expressing interneurons. J. Neurosci. Res.95, 2336–2344. 10.1002/jnr.24011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37

  ChuongA. S.MiriM. L.BusskampV.MatthewsG. A.AckerL. C.SorensenA. T.et al. (2014). Noninvasive optical inhibition with a red-shifted microbial rhodopsin. Nat. Neurosci.17, 1123–1129. 10.1038/nn.3752

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38

  CobbS. R.BuhlE. H.HalasyK.PaulsenO.SomogyiP. (1995). Synchronization of neuronal activity in hippocampus by individual GABAergic interneurons. Nature378, 75–78. 10.1038/378075a0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39

  ConnorD. E.Jr.NixonM.NandaA.GuthikondaB. (2012). Vagal nerve stimulation for the treatment of medically refractory epilepsy: a review of the current literature. Neurosurg. Focus32:E12. 10.3171/2011.12.FOCUS11328

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 40

  CooperI. S.AminI.GilmanS. (1973). The effect of chronic cerebellar stimulation upon epilepsy in man. Trans. Am. Neurol. Assoc.98, 192–196.

  • Pubmed Abstract
  • Google Scholar

• 41

  CosentinoC.AlberioL.GazzarriniS.AquilaM.RomanoE.CermenatiS.et al. (2015). Optogenetics. Engineering of a light-gated potassium channel. Science348, 707–710. 10.1126/science.aaa2787

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42

  CotrinaM. L.LinJ. H.Alves-RodriguesA.LiuS.LiJ.Azmi-GhadimiH.et al. (1998). Connexins regulate calcium signaling by controlling ATP release. Proc. Natl. Acad. Sci. U S A95, 15735–15740. 10.1073/pnas.95.26.15735

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43

  CoulterD. A. (1999). Chronic epileptogenic cellular alterations in the limbic system after status epilepticus. Epilepsia40, S23–33; discussion S40–S21. 10.1111/j.1528-1157.1999.tb00875.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 44

  CunninghamM.ChoJ. H.LeungA.SavvidisG.AhnS.MoonM.et al. (2014). hPSC-derived maturing GABAergic interneurons ameliorate seizures and abnormal behavior in epileptic mice. Cell Stem Cell15, 559–573. 10.1016/j.stem.2014.10.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  DavisR.EmmondsS. E. (1992). Cerebellar stimulation for seizure control: 17-year study. Stereotact. Funct. Neurosurg.58, 200–208. 10.1159/000098996

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 46

  DawydowA.GuetaR.LjaschenkoD.UllrichS.HermannM.EhmannN.et al. (2014). Channelrhodopsin-2-XXL, a powerful optogenetic tool for low-light applications. Proc. Natl. Acad. Sci. U S A111, 13972–13977. 10.1073/pnas.1408269111

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 47

  de TisiJ.BellG. S.PeacockJ. L.McEvoyA. W.HarknessW. F.SanderJ. W.et al. (2011). The long-term outcome of adult epilepsy surgery, patterns of seizure remission, and relapse: a cohort study. Lancet378, 1388–1395. 10.1016/S0140-6736(11)60890-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 48

  DeisserothK. (2011). Optogenetics. Nat. Methods8, 26–29. 10.1038/nmeth.f.324

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49

  DeisserothK. (2015). Optogenetics: 10 years of microbial opsins in neuroscience. Nat. Neurosci.18, 1213–1225. 10.1038/nn.4091

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50

  DrexelM.KirchmairE.Wieselthaler-HölzlA.PreidtA. P.SperkG. (2012). Somatostatin and neuropeptide Y neurons undergo different plasticity in parahippocampal regions in kainic acid-induced epilepsy. J. Neuropathol. Exp. Neurol.71, 312–329. 10.1097/NEN.0b013e31824d9882

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51

  DuchownyM.BhatiaS. (2014). Epilepsy: preserving memory in temporal lobectomy-are networks the key?Nat. Rev. Neurol.10, 245–246. 10.1038/nrneurol.2014.67

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52

  DuffyB. A.ChoyM.ChuapocoM. R.MadsenM.LeeJ. H. (2015). MRI compatible optrodes for simultaneous LFP and optogenetic fMRI investigation of seizure-like afterdischarges. Neuroimage123, 173–184. 10.1016/j.neuroimage.2015.07.038

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  DybdalD.GaleK. (2000). Postural and anticonvulsant effects of inhibition of the rat subthalamic nucleus. J. Neurosci.20, 6728–6733. 10.1523/JNEUROSCI.20-17-06728.2000

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 54

  EllenderT. J.RaimondoJ. V.IrkleA.LamsaK. P.AkermanC. J. (2014). Excitatory effects of parvalbumin-expressing interneurons maintain hippocampal epileptiform activity via synchronous afterdischarges. J. Neurosci.34, 15208–15222. 10.1523/JNEUROSCI.1747-14.2014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55

  EnglandM. J.LivermanC. T.SchultzA. M.StrawbridgeL. M. (2012). Epilepsy across the spectrum: promoting health and understanding. A summary of the Institute of Medicine report. Epilepsy Behav.25, 266–276. 10.1016/j.yebeh.2012.06.016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 56

  FennoL. E.MattisJ.RamakrishnanC.HyunM.LeeS. Y.HeM.et al. (2014). Targeting cells with single vectors using multiple-feature Boolean logic. Nat. Methods11, 763–772. 10.1038/nmeth.2996

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 57

  Fernandez-GuardiolaA.ManniE.WilsonJ. H.DowR. S. (1962). Microelectrode recording of cerebellar and cerebral unit activity during convulsive afterdischarge. Exp. Neurol.6, 48–69. 10.1016/0014-4886(62)90014-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 58

  FisherR.SalanovaV.WittT.WorthR.HenryT.GrossR.et al. (2010). Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy. Epilepsia51, 899–908. 10.1111/j.1528-1167.2010.02536.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59

  ForcelliP. A. (2017). Applications of optogenetic and chemogenetic methods to seizure circuits: where to go next?J. Neurosci. Res.95, 2345–2356. 10.1002/jnr.24135

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 60

  FreundT. F.BuzsákiG. (1996). Interneurons of the hippocampus. Hippocampus6, 347–470. 10.1002/(sici)1098-1063(1996)6:4<347::aid-hipo1>3.0.co;2-i

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 61

  GaleK. (1992). Subcortical structures and pathways involved in convulsive seizure generation. J. Clin. Neurophysiol.9, 264–277. 10.1097/00004691-199204010-00007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 62

  GibsonE. M.PurgerD.MountC. W.GoldsteinA. K.LinG. L.WoodL. S.et al. (2014). Neuronal activity promotes oligodendrogenesis and adaptive myelination in the mammalian brain. Science344:1252304. 10.1126/science.1252304

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63

  Gómez-GonzaloM.LosiG.ChiavegatoA.ZontaM.CammarotaM.BrondiM.et al. (2010). An excitatory loop with astrocytes contributes to drive neurons to seizure threshold. PLoS Biol.8:e1000352. 10.1371/journal.pbio.1000352

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 64

  GoodmanJ. H.SloviterR. S. (1992). Evidence for commissurally projecting parvalbumin-immunoreactive basket cells in the dentate gyrus of the rat. Hippocampus2, 13–21. 10.1002/hipo.450020103

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 65

  GovorunovaE. G.SineshchekovO. A.JanzR.LiuX.SpudichJ. L. (2015). NEUROSCIENCE. Natural light-gated anion channels: a family of microbial rhodopsins for advanced optogenetics. Science349, 647–650. 10.1126/science.aaa7484

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 66

  GradinaruV.MogriM.ThompsonK. R.HendersonJ. M.DeisserothK. (2009). Optical deconstruction of parkinsonian neural circuitry. Science324, 354–359. 10.1126/science.1167093

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 67

  GradinaruV.ThompsonK. R.DeisserothK. (2008). eNpHR: a Natronomonas halorhodopsin enhanced for optogenetic applications. Brain Cell Biol.36, 129–139. 10.1007/s11068-008-9027-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 68

  GradinaruV.ZhangF.RamakrishnanC.MattisJ.PrakashR.DiesterI.et al. (2010). Molecular and cellular approaches for diversifying and extending optogenetics. Cell141, 154–165. 10.1016/j.cell.2010.02.037

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69

  GrosenickL.MarshelJ. H.DeisserothK. (2015). Closed-loop and activity-guided optogenetic control. Neuron86, 106–139. 10.1016/j.neuron.2015.03.034

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70

  GunaydinL. A.YizharO.BerndtA.SohalV. S.DeisserothK.HegemannP. (2010). Ultrafast optogenetic control. Nat. Neurosci.13, 387–392. 10.1038/nn.2495

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 71

  GutrufP.RogersJ. A. (2018). Implantable, wireless device platforms for neuroscience research. Curr. Opin. Neurobiol.50, 42–49. 10.1016/j.conb.2017.12.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72

  HajosN.PaulsenO. (2009). Network mechanisms of γ oscillations in the CA3 region of the hippocampus. Neural Netw.22, 1113–1119. 10.1016/j.neunet.2009.07.024

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 73

  HanX.ChowB. Y.ZhouH.KlapoetkeN. C.ChuongA.RajimehrR.et al. (2011). A high-light sensitivity optical neural silencer: development and application to optogenetic control of non-human primate cortex. Front. Syst. Neurosci.5:18. 10.3389/fnsys.2011.00018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74

  HashimotoT.ObataK. (1991). Induction of somatostatin by kainic acid in pyramidal and granule cells of the rat hippocampus. Neurosci. Res.12, 514–527. 10.1016/s0168-0102(09)80004-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75

  HeM.TucciaroneJ.LeeS.NigroM. J.KimY.LevineJ. M.et al. (2016). Strategies and tools for combinatorial targeting of GABAergic neurons in mouse cerebral cortex. Neuron92:555. 10.1016/j.neuron.2016.10.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76

  HeathR. G.DempesyC. W.FontanaC. J.FitzjarrellA. T. (1980). Feedback loop between cerebellum and septal-hippocampal sites: its role in emotion and epilepsy. Biol. Psychiatry15, 541–556.

  • Pubmed Abstract
  • Google Scholar

• 77

  HeinemannU.BeckH.DreierJ. P.FickerE.StabelJ.ZhangC. L. (1992). The dentate gyrus as a regulated gate for the propagation of epileptiform activity. Epilepsy Res. Suppl.7, 273–280.

  • Pubmed Abstract
  • Google Scholar

• 78

  HendersonK. W.GuptaJ.TagliatelaS.LitvinaE.ZhengX.Van ZandtM. A.et al. (2014). Long-term seizure suppression and optogenetic analyses of synaptic connectivity in epileptic mice with hippocampal grafts of GABAergic interneurons. J. Neurosci.34, 13492–13504. 10.1523/JNEUROSCI.0005-14.2014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79

  HoubenT.LoonenI. C.BacaS. M.SchenkeM.MeijerJ. H.FerrariM. D.et al. (2017). Optogenetic induction of cortical spreading depression in anesthetized and freely behaving mice. J. Cereb. Blood Flow Metab.37, 1641–1655. 10.1177/0271678x16645113

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80

  HoweM. W.DombeckD. A. (2016). Rapid signalling in distinct dopaminergic axons during locomotion and reward. Nature535, 505–510. 10.1038/nature18942

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81

  HsiehJ. Y.BarabanS. C. (2017). Medial ganglionic eminence progenitors transplanted into hippocampus integrate in a functional and subtype-appropriate manner. eNeuro4:ENEURO.0359-16.2017. 10.1523/ENEURO.0359-16.2017

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 82

  HuntR. F.BarabanS. C. (2015). Interneuron transplantation as a treatment for epilepsy. Cold Spring Harb. Perspect. Med.5:a022376. 10.1101/cshperspect.a022376

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 83

  HuntR. F.GirskisK. M.RubensteinJ. L.Alvarez-BuyllaA.BarabanS. C. (2013). GABA progenitors grafted into the adult epileptic brain control seizures and abnormal behavior. Nat. Neurosci.16, 692–697. 10.1038/nn.3392

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84

  IaccarinoH. F.SingerA. C.MartorellA. J.RudenkoA.GaoF.GillinghamT. Z.et al. (2016). γ frequency entrainment attenuates amyloid load and modifies microglia. Nature540, 230–235. 10.1038/nature20587

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 85

  JiangX.LachanceM.RossignolE. (2016). Involvement of cortical fast-spiking parvalbumin-positive basket cells in epilepsy. Prog. Brain Res.226, 81–126. 10.1016/bs.pbr.2016.04.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 86

  JinS. H.JeongW.ChungC. K. (2015). Mesial temporal lobe epilepsy with hippocampal sclerosis is a network disorder with altered cortical hubs. Epilepsia56, 772–779. 10.1111/epi.12966

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87

  JulienR. M.LaxerK. D. (1974). Cerebellar responses to penicillin-induced cerebral cortical epileptiform discharge. Electroencephalogr. Clin. Neurophysiol.37, 123–132. 10.1016/0013-4694(74)90002-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88

  JunyentF.KremerE. J. (2015). CAV-2—why a canine virus is a neurobiologist’s best friend. Curr. Opin. Pharmacol.24, 86–93. 10.1016/j.coph.2015.08.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89

  KaemmererW. F. (2017). “Optogenetics for neurological disorders: what is the path to the clinic?,” in Optogenetics: From Neuronal Function to Mapping and Disease Biology, ed. AppasaniK. (Cambridge: Cambridge University Press), 169–180.

  • Google Scholar

• 90

  KainrathS.StadlerM.ReichhartE.DistelM.JanovjakH. (2017). Green-light-induced inactivation of receptor signaling using cobalamin-binding domains. Angew. Chem. Int. Ed Engl.56, 4608–4611. 10.1002/anie.201611998

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91

  KandelA.BuzsákiG. (1993). Cerebellar neuronal activity correlates with spike and wave EEG patterns in the rat. Epilepsy Res.16, 1–9. 10.1016/0920-1211(93)90033-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92

  KatonaL.MicklemB.BorhegyiZ.SwiejkowskiD. A.ValentiO.VineyT. J.et al. (2017). Behavior-dependent activity patterns of GABAergic long-range projecting neurons in the rat hippocampus. Hippocampus27, 359–377. 10.1002/hipo.22696

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93

  KawanoF.OkazakiR.YazawaM.SatoM. (2016). A photoactivatable Cre-loxP recombination system for optogenetic genome engineering. Nat. Chem. Biol.12, 1059–1064. 10.1038/nchembio.2205

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94

  KhoshkhooS.VogtD.SohalV. S. (2017). Dynamic, cell-type-specific roles for GABAergic interneurons in a mouse model of optogenetically inducible seizures. Neuron93, 291–298. 10.1016/j.neuron.2016.11.043

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95

  KimT. I.McCallJ. G.JungY. H.HuangX.SiudaE. R.LiY.et al. (2013). Injectable, cellular-scale optoelectronics with applications for wireless optogenetics. Science340, 211–216. 10.1126/science.1232437

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96

  KlapoetkeN. C.MurataY.KimS. S.PulverS. R.Birdsey-BensonA.ChoY. K.et al. (2014). Independent optical excitation of distinct neural populations. Nat. Methods11, 338–346. 10.1038/nmeth.2836

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97

  KondaboluK.RobertsE. A.BucklinM.McCarthyM. M.KopellN.HanX. (2016). Striatal cholinergic interneurons generate β and γ oscillations in the corticostriatal circuit and produce motor deficits. Proc. Natl. Acad. Sci. U S A113, E3159–E3168. 10.1073/pnas.1605658113

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98

  KonermannS.BrighamM. D.TrevinoA. E.HsuP. D.HeidenreichM.CongL.et al. (2013). Optical control of mammalian endogenous transcription and epigenetic states. Nature500, 472–476. 10.1038/nature12466

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99

  KramerR. H.MourotA.AdesnikH. (2013). Optogenetic pharmacology for control of native neuronal signaling proteins. Nat. Neurosci.16, 816–823. 10.1038/nn.3424

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100

  Krook-MagnusonE. (2017). The gate and the source? The dentate gyrus takes central stage in temporal lobe epilepsy. Epilepsy Curr.17, 48–49. 10.5698/1535-7511-17.1.48

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 101

  Krook-MagnusonE.ArmstrongC.BuiA.LewS.OijalaM.SolteszI. (2015a). In vivo evaluation of the dentate gate theory in epilepsy. J. Physiol.593, 2379–2388. 10.1113/JP270056

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102

  Krook-MagnusonE.GelinasJ. N.SolteszI.BuzsákiG. (2015b). Neuroelectronics and biooptics: closed-loop technologies in neurological disorders. JAMA Neurol.72, 823–829. 10.1001/jamaneurol.2015.0608

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 103

  Krook-MagnusonE.ArmstrongC.OijalaM.SolteszI. (2013). On-demand optogenetic control of spontaneous seizures in temporal lobe epilepsy. Nat. Commun.4:1376. 10.1038/ncomms2376

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 104

  Krook-MagnusonE.LedriM.SolteszI.KokaiaM. (2014a). How might novel technologies such as optogenetics lead to better treatments in epilepsy?Adv. Exp. Med. Biol.813, 319–336. 10.1007/978-94-017-8914-1_26

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 105

  Krook-MagnusonE.SzaboG. G.ArmstrongC.OijalaM.SolteszI. (2014b). Cerebellar directed optogenetic intervention inhibits spontaneous hippocampal seizures in a mouse model of temporal lobe epilepsy. eNeuro1:e2014. 10.1523/eneuro.0005-14.2014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 106

  Krook-MagnusonE.SolteszI. (2015). Beyond the hammer and the scalpel: selective circuit control for the epilepsies. Nat. Neurosci.18, 331–338. 10.1038/nn.3943

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 107

  Krook-MagnusonE.VargaC.LeeS. H.SolteszI. (2012). New dimensions of interneuronal specialization unmasked by principal cell heterogeneity. Trends Neurosci.35, 175–184. 10.1016/j.tins.2011.10.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 108

  KrosL.Eelkman RoodaO. H.SpankeJ. K.AlvaP.van DongenM. N.KarapatisA.et al. (2015). Cerebellar output controls generalized spike-and-wave discharge occurrence. Ann. Neurol.77, 1027–1049. 10.1002/ana.24399

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 109

  KullmannD. M.SchorgeS.WalkerM. C.WykesR. C. (2014). Gene therapy in epilepsy-is it time for clinical trials?Nat. Rev. Neurol.10, 300–304. 10.1038/nrneurol.2014.43

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 110

  LadasT. P.ChiangC. C.Gonzalez-ReyesL. E.NowakT.DurandD. M. (2015). Seizure reduction through interneuron-mediated entrainment using low frequency optical stimulation. Exp. Neurol.269, 120–132. 10.1016/j.expneurol.2015.04.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111

  LaurbergS.ZimmerJ. (1981). Lesion-induced sprouting of hippocampal mossy fiber collaterals to the fascia dentata in developing and adult rats. J. Comp. Neurol.200, 433–459. 10.1002/cne.902000310

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 112

  LaxerK. D.TrinkaE.HirschL. J.CendesF.LangfittJ.DelantyN.et al. (2014). The consequences of refractory epilepsy and its treatment. Epilepsy Behav.37C, 59–70. 10.1016/j.yebeh.2014.05.031

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113

  LedriM.MadsenM. G.NikitidouL.KirikD.KokaiaM. (2014). Global optogenetic activation of inhibitory interneurons during epileptiform activity. J. Neurosci.34, 3364–3377. 10.1523/JNEUROSCI.2734-13.2014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 114

  LeeJ.OzdenI.SongY. K.NurmikkoA. V. (2015). Transparent intracortical microprobe array for simultaneous spatiotemporal optical stimulation and multichannel electrical recording. Nat. Methods12, 1157–1162. 10.1038/nmeth.3620

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115

  LeeS. H.MarchionniI.BezaireM.VargaC.DanielsonN.Lovett-BarronM.et al. (2014). Parvalbumin-positive basket cells differentiate among hippocampal pyramidal cells. Neuron82, 1129–1144. 10.1016/j.neuron.2014.03.034

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 116

  LeeS. Y.SolteszI. (2011). Cholecystokinin: a multi-functional molecular switch of neuronal circuits. Dev. Neurobiol.71, 83–91. 10.1002/dneu.20815

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 117

  LevyL. F.AuchterlonieW. C. (1979). Chronic cerebellar stimulation in the treatment of epilepsy. Epilepsia20, 235–245. 10.1111/j.1528-1157.1979.tb04800.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 118

  LibbrechtS.Van den HauteC.MalinouskayaL.GijsbersR.BaekelandtV. (2017). Evaluation of WGA-Cre-dependent topological transgene expression in the rodent brain. Brain Struct. Funct.222, 717–733. 10.1007/s00429-016-1241-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 119

  LimD. H.LeDueJ. M.MohajeraniM. H.MurphyT. H. (2014). Optogenetic mapping after stroke reveals network-wide scaling of functional connections and heterogeneous recovery of the peri-infarct. J. Neurosci.34, 16455–16466. 10.1523/JNEUROSCI.3384-14.2014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 120

  LinJ. Y.KnutsenP. M.MullerA.KleinfeldD.TsienR. Y. (2013). ReaChR: a red-shifted variant of channelrhodopsin enables deep transcranial optogenetic excitation. Nat. Neurosci.16, 1499–1508. 10.1038/nn.3502

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 121

  LinJ. Y.LinM. Z.SteinbachP.TsienR. Y. (2009). Characterization of engineered channelrhodopsin variants with improved properties and kinetics. Biophys. J.96, 1803–1814. 10.1016/j.bpj.2008.11.034

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 122

  LinW. C.TsaiM. C.DavenportC. M.SmithC. M.VeitJ.WilsonN. M.et al. (2015). A comprehensive optogenetic pharmacology toolkit for in vivo control of GABA_A receptors and synaptic inhibition. Neuron88, 879–891. 10.1016/j.neuron.2015.10.026

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 123

  LiuQ.GanL.NiJ.ChenY.ChenY.HuangZ.et al. (2017). Dcf1 improves behavior deficit in Drosophila and mice caused by optogenetic suppression. J. Cell. Biochem.118, 4210–4215. 10.1002/jcb.26048

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 124

  LothmanE. W.StringerJ. L.BertramE. H. (1992). The dentate gyrus as a control point for seizures in the hippocampus and beyond. Epilepsy Res. Suppl.7, 301–313.

  • Pubmed Abstract
  • Google Scholar

• 125

  LuY.ZhongC.WangL.WeiP.HeW.HuangK.et al. (2016). Optogenetic dissection of ictal propagation in the hippocampal-entorhinal cortex structures. Nat. Commun.7:10962. 10.1038/ncomms12019

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 126

  MadisenL.GarnerA. R.ShimaokaD.ChuongA. S.KlapoetkeN. C.LiL.et al. (2015). Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance. Neuron85, 942–958. 10.1016/j.neuron.2015.02.022

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 127

  MadisenL.MaoT.KochH.ZhuoJ. M.BerenyiA.FujisawaS.et al. (2012). A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing. Nat. Neurosci.15, 793–802. 10.1038/nn.3078

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 128

  MaguireJ. L. (2015). Implicating interneurons: optogenetic studies suggest that interneurons are guilty of contributing to epileptiform activity. Epilepsy Curr.15, 213–216. 10.5698/1535-7511-15.4.213

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 129

  MahnM.GiborL.Cohen-Kashi MalinaK.PatilP.PrintzY.OringS.et al. (2017). High-efficiency optogenetic silencing with soma-targeted anion-conducting channelrhodopsins. bioRxiv [Epub ahead of print]. . 10.1101/225847

  • CrossRef
  • Google Scholar

• 130

  MahnM.PriggeM.RonS.LevyR.YizharO. (2016). Biophysical constraints of optogenetic inhibition at presynaptic terminals. Nat. Neurosci.19, 554–556. 10.1038/nn.4266

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 131

  MarosoM.SzaboG. G.KimH. K.AlexanderA.BuiA. D.LeeS. H.et al. (2016). Cannabinoid control of learning and memory through HCN channels. Neuron89, 1059–1073. 10.1016/j.neuron.2016.01.023

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 132

  MastroK. J.ZitelliK. T.WillardA. M.LeblancK. H.KravitzA. V.GittisA. H. (2017). Cell-specific pallidal intervention induces long-lasting motor recovery in dopamine-depleted mice. Nat. Neurosci.20, 815–823. 10.1038/nn.4559

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133

  MathernG. W.BabbT. L.PretoriusJ. K.LeiteJ. P. (1995). Reactive synaptogenesis and neuron densities for neuropeptide Y, somatostatin, and glutamate decarboxylase immunoreactivity in the epileptogenic human fascia dentata. J. Neurosci.15, 3990–4004. 10.1523/jneurosci.15-05-03990.1995

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 134

  MattisJ.TyeK. M.FerencziE. A.RamakrishnanC.O’SheaD. J.PrakashR.et al. (2012). Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins. Nat. Methods9, 159–172. 10.1038/nmeth.1808

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 135

  McGeorgeA. J.FaullR. L. (1989). The organization of the projection from the cerebral cortex to the striatum in the rat. Neuroscience29, 503–537. 10.1016/0306-4522(89)90128-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 136

  MelzerS.MichaelM.CaputiA.EliavaM.FuchsE. C.WhittingtonM. A.et al. (2012). Long-range-projecting GABAergic neurons modulate inhibition in hippocampus and entorhinal cortex. Science335, 1506–1510. 10.1126/science.1217139

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 137

  MeyerM.Kienzler-NorwoodF.BauerS.RosenowF.NorwoodB. A. (2016). Removing entorhinal cortex input to the dentate gyrus does not impede low frequency oscillations, an EEG-biomarker of hippocampal epileptogenesis. Sci. Rep.6:25660. 10.1038/srep25660

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 138

  MohammadF.StewartJ. C.OttS.ChlebikovaK.ChuaJ. Y.KohT. W.et al. (2017). Optogenetic inhibition of behavior with anion channelrhodopsins. Nat. Methods14, 271–274. 10.1038/nmeth.4148

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 139

  MontgomeryK. L.IyerS. M.ChristensenA. J.DeisserothK.DelpS. L. (2016). Beyond the brain: optogenetic control in the spinal cord and peripheral nervous system. Sci. Transl. Med.8:337rv5. 10.1126/scitranslmed.aad7577

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 140

  MooreY. E.KelleyM. R.BrandonN. J.DeebT. Z.MossS. J. (2017). Seizing control of KCC2: a new therapeutic target for epilepsy. Trends Neurosci.40, 555–571. 10.1016/j.tins.2017.06.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 141

  Motta-MenaL. B.ReadeA.MalloryM. J.GlantzS.WeinerO. D.LynchK. W.et al. (2014). An optogenetic gene expression system with rapid activation and deactivation kinetics. Nat. Chem. Biol.10, 196–202. 10.1038/nchembio.1430

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 142

  NadlerJ. V.PerryB. W.GentryC.CotmanC. W. (1980). Loss and reacquisition of hippocampal synapses after selective destruction of CA3-CA4 afferents with kainic acid. Brain Res.191, 387–403. 10.1016/0006-8993(80)91289-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 143

  NagarajV.LeeS. T.Krook-MagnusonE.SolteszI.BenquetP.IrazoquiP. P.et al. (2015). Future of seizure prediction and intervention: closing the loop. J. Clin. Neurophysiol.32, 194–206. 10.1097/wnp.0000000000000139

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 144

  NagelG.BraunerM.LiewaldJ. F.AdeishviliN.BambergE.GottschalkA. (2005). Light activation of channelrhodopsin-2 in excitable cells of Caenorhabditis elegans triggers rapid behavioral responses. Curr. Biol.15, 2279–2284. 10.1016/j.cub.2005.11.032

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 145

  NagelG.SzellasT.HuhnW.KateriyaS.AdeishviliN.BertholdP.et al. (2003). Channelrhodopsin-2, a directly light-gated cation-selective membrane channel. Proc. Natl. Acad. Sci. U S A100, 13940–13945. 10.1073/pnas.1936192100

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 146

  NamburiP.BeyelerA.YorozuS.CalhoonG. G.HalbertS. A.WichmannR.et al. (2015). A circuit mechanism for differentiating positive and negative associations. Nature520, 675–678. 10.1038/nature14366

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 147

  NiedermeyerE.UematsuS. (1974). Electroencephalographic recordings from deep cerebellar structures in patients with uncontrolled epileptic seizures. Electroencephalogr. Clin. Neurophysiol.37, 355–365. 10.1016/0013-4694(74)90111-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 148

  OsawaS.IwasakiM.HosakaR.MatsuzakaY.TomitaH.IshizukaT.et al. (2013). Optogenetically induced seizure and the longitudinal hippocampal network dynamics. PLoS One8:e60928. 10.1371/journal.pone.0060928

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 149

  PathakH. R.WeissingerF.TerunumaM.CarlsonG. C.HsuF. C.MossS. J.et al. (2007). Disrupted dentate granule cell chloride regulation enhances synaptic excitability during development of temporal lobe epilepsy. J. Neurosci.27, 14012–14022. 10.1523/JNEUROSCI.4390-07.2007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 150

  PazJ. T.DavidsonT. J.FrechetteE. S.DelordB.ParadaI.PengK.et al. (2013). Closed-loop optogenetic control of thalamus as a tool for interrupting seizures after cortical injury. Nat. Neurosci.16, 64–70. 10.1038/nn.3269

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 151

  PengZ.ZhangN.WeiW.HuangC. S.CetinaY.OtisT. S.et al. (2013). A reorganized GABAergic circuit in a model of epilepsy: evidence from optogenetic labeling and stimulation of somatostatin interneurons. J. Neurosci.33, 14392–14405. 10.1523/jneurosci.2045-13.2013

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 152

  PeruccaP.GilliamF. G. (2012). Adverse effects of antiepileptic drugs. Lancet Neurol.11, 792–802. 10.1016/S1474-4422(12)70153-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 153

  PunR. Y.RolleI. J.LasargeC. L.HosfordB. E.RosenJ. M.UhlJ. D.et al. (2012). Excessive activation of mTOR in postnatally generated granule cells is sufficient to cause epilepsy. Neuron75, 1022–1034. 10.1016/j.neuron.2012.08.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 154

  RajasethupathyP.FerencziE.DeisserothK. (2016). Targeting neural circuits. Cell165, 524–534. 10.1016/j.cell.2016.03.047

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 155

  RoyD. S.AronsA.MitchellT. I.PignatelliM.RyanT. J.TonegawaS. (2016). Memory retrieval by activating engram cells in mouse models of early Alzheimer’s disease. Nature531, 508–512. 10.1038/nature17172

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 156

  RungtaR. L.OsmanskiB. F.BoidoD.TanterM.CharpakS. (2017). Light controls cerebral blood flow in naive animals. Nat. Commun.8:14191. 10.1038/ncomms14191

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 157

  SaundersT. L. (2011). Inducible transgenic mouse models. Methods Mol Biol693, 103–115. 10.1007/978-1-60761-974-1_7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 158

  SessoloM.MarconI.BovettiS.LosiG.CammarotaM.RattoG. M.et al. (2015). Parvalbumin-positive inhibitory interneurons oppose propagation but favor generation of focal epileptiform activity. J. Neurosci.35, 9544–9557. 10.1523/jneurosci.5117-14.2015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 159

  SheybaniL.BirotG.ContestabileA.SeeckM.Zoltan KissJ.SchallerK.et al. (2018). Electrophysiological evidence for the development of a self-sustained large-scale epileptic network in the kainate mouse-model of temporal lobe epilepsy. J. Neurosci.38, 3776’3791. 10.1523/JNEUROSCI.2193-17.2018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 160

  ShiriZ.ManseauF.LévesqueM.WilliamsS.AvoliM. (2015). Interneuron activity leads to initiation of low-voltage fast-onset seizures. Ann. Neurol.77, 541–546. 10.1002/ana.24342

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 161

  ShiriZ.ManseauF.LévesqueM.WilliamsS.AvoliM. (2016). Activation of specific neuronal networks leads to different seizure onset types. Ann. Neurol.79, 354–365. 10.1002/ana.24570

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 162

  SlomiankaL.AmreinI.KnueselI.SørensenJ. C.WolferD. P. (2011). Hippocampal pyramidal cells: the reemergence of cortical lamination. Brain Struct. Funct.216, 301–317. 10.1007/s00429-011-0322-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 163

  SohalV. S.ZhangF.YizharO.DeisserothK. (2009). Parvalbumin neurons and γ rhythms enhance cortical circuit performance. Nature459, 698–702. 10.1038/nature07991

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 164

  SolteszI.LosonczyA. (2018). CA1 pyramidal cell diversity enabling parallel information processing in the hippocampus. Nat. Neurosci.21, 484–493. 10.1038/s41593-018-0118-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 165

  SoperC.WickerE.KulickC. V.N’GouemoP.ForcelliP. A. (2016). Optogenetic activation of superior colliculus neurons suppresses seizures originating in diverse brain networks. Neurobiol. Dis.87, 102–115. 10.1016/j.nbd.2015.12.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 166

  SorokinJ. M.DavidsonT. J.FrechetteE.AbramianA. M.DeisserothK.HuguenardJ. R.et al. (2017). Bidirectional control of generalized epilepsy networks via rapid real-time switching of firing mode. Neuron93, 194–210. 10.1016/j.neuron.2016.11.026

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 167

  SoussiR.BoullandJ. L.BassotE.BrasH.CoulonP.ChaudhryF. A.et al. (2015). Reorganization of supramammillary-hippocampal pathways in the rat pilocarpine model of temporal lobe epilepsy: evidence for axon terminal sprouting. Brain Struct. Funct.220, 2449–2468. 10.1007/s00429-014-0800-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 168

  ŠramkaM.FritzG.GalandaM.NádvorníkP. (1976). Some observations in treatment stimulation of epilepsy. Acta Neurochir.23, 257–262. 10.1007/978-3-7091-8444-8_41

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 169

  SukhotinskyI.ChanA. M.AhmedO. J.RaoV. R.GradinaruV.RamakrishnanC.et al. (2013). Optogenetic delay of status epilepticus onset in an in vivo rodent epilepsy model. PLoS One8:e62013. 10.1371/journal.pone.0062013

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 170

  SunY.NguyenA. Q.NguyenJ. P.LeL.SaurD.ChoiJ.et al. (2014). Cell-type-specific circuit connectivity of hippocampal CA1 revealed through Cre-dependent rabies tracing. Cell Rep.7, 269–280. 10.1016/j.celrep.2014.02.030

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 171

  SutulaT.CascinoG.CavazosJ.ParadaI.RamirezL. (1989). Mossy fiber synaptic reorganization in the epileptic human temporal lobe. Ann. Neurol.26, 321–330. 10.1002/ana.410260303

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 172

  SzabadicsJ.VargaC.MolnárG.OláhS.BarzóP.TamásG. (2006). Excitatory effect of GABAergic axo-axonic cells in cortical microcircuits. Science311, 233–235. 10.1126/science.1121325

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 173

  SzaboG. G.DuX.OijalaM.VargaC.ParentJ. M.SolteszI. (2017). Extended interneuronal network of the dentate gyrus. Cell Rep.20, 1262–1268. 10.1016/j.celrep.2017.07.042

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 174

  TangJ. C.RudolphS.DhandeO. S.AbrairaV. E.ChoiS.LapanS. W.et al. (2015). Cell type-specific manipulation with GFP-dependent Cre recombinase. Nat. Neurosci.18, 1334–1341. 10.1038/nn.4081

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 175

  TaniguchiH.HeM.WuP.KimS.PaikR.SuginoK.et al. (2011). A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex. Neuron71, 995–1013. 10.1016/j.neuron.2011.07.026

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 176

  TasicB.MenonV.NguyenT. N.KimT. K.JarskyT.YaoZ.et al. (2016). Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci.19, 335–346. 10.1038/nn.4216

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 177

  TauckD. L.NadlerJ. V. (1985). Evidence of functional mossy fiber sprouting in hippocampal formation of kainic acid-treated rats. J. Neurosci.5, 1016–1022. 10.1523/jneurosci.05-04-01016.1985

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 178

  TervoD. G.HwangB. Y.ViswanathanS.GajT.LavzinM.RitolaK. D.et al. (2016). A designer AAV variant permits efficient retrograde access to projection neurons. Neuron92, 372–382. 10.1016/j.neuron.2016.09.021

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 179

  TianG. F.AzmiH.TakanoT.XuQ.PengW.LinJ.et al. (2005). An astrocytic basis of epilepsy. Nat. Med.11, 973–981. 10.1038/nm1277

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 180

  TochitskyI.BanghartM. R.MourotA.YaoJ. Z.GaubB.KramerR. H.et al. (2012). Optochemical control of genetically engineered neuronal nicotinic acetylcholine receptors. Nat. Chem.4, 105–111. 10.1038/nchem.1234

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 181

  ToettcherJ. E.WeinerO. D.LimW. A. (2013). Using optogenetics to interrogate the dynamic control of signal transmission by the Ras/Erk module. Cell155, 1422–1434. 10.1016/j.cell.2013.11.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 182

  TønnesenJ.KokaiaM. (2017). Epilepsy and optogenetics: can seizures be controlled by light?Clin. Sci.131, 1605–1616. 10.1042/cs20160492

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 183

  TønnesenJ.ParishC. L.SørensenA. T.AnderssonA.LundbergC.DeisserothK.et al. (2011). Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model. PLoS One6:e17560. 10.1371/journal.pone.0017560

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 184

  TønnesenJ.SorensenA. T.DeisserothK.LundbergC.KokaiaM. (2009). Optogenetic control of epileptiform activity. Proc. Natl. Acad. Sci. U S A106, 12162–12167. 10.1073/pnas.0901915106

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 185

  TungJ. K.BerglundK.GrossR. E. (2016). Optogenetic approaches for controlling seizure activity. Brain Stimul.9, 801–810. 10.1016/j.brs.2016.06.055

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 186

  TungJ. K.GutekunstC. A.GrossR. E. (2015). Inhibitory luminopsins: genetically-encoded bioluminescent opsins for versatile, scalable, and hardware-independent optogenetic inhibition. Sci. Rep.5:14366. 10.1038/srep14366

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 187

  TungJ. K.ShiuF. H.DingK.GrossR. E. (2018). Chemically activated luminopsins allow optogenetic inhibition of distributed nodes in an epileptic network for non-invasive and multi-site suppression of seizure activity. Neurobiol. Dis.109, 1–10. 10.1016/j.nbd.2017.09.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 188

  ValentínA.García NavarreteE.ChelvarajahR.TorresC.NavasM.VicoL.et al. (2013). Deep brain stimulation of the centromedian thalamic nucleus for the treatment of generalized and frontal epilepsies. Epilepsia54, 1823–1833. 10.1111/epi.12352

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 189

  Van BurenJ. M.WoodJ. H.OakleyJ.HambrechtF. (1978). Preliminary evaluation of cerebellar stimulation by double-blind stimulation and biological criteria in the treatment of epilepsy. J. Neurosurg.48, 407–416. 10.3171/jns.1978.48.3.0407

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 190

  VaughanD. N.RaynerG.TailbyC.JacksonG. D. (2016). MRI-negative temporal lobe epilepsy: a network disorder of neocortical connectivity. Neurology87, 1934–1942. 10.1212/wnl.0000000000003289

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 191

  VelascoF.Carrillo-RuizJ. D.BritoF.VelascoM.VelascoA. L.MarquezI.et al. (2005). Double-blind, randomized controlled pilot study of bilateral cerebellar stimulation for treatment of intractable motor seizures. Epilepsia46, 1071–1081. 10.1111/j.1528-1167.2005.70504.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 192

  VolgrafM.GorostizaP.NumanoR.KramerR. H.IsacoffE. Y.TraunerD. (2006). Allosteric control of an ionotropic glutamate receptor with an optical switch. Nat. Chem. Biol.2, 47–52. 10.1038/nchembio756

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 193

  WallN. R.WickershamI. R.CetinA.De La ParraM.CallawayE. M. (2010). Monosynaptic circuit tracing in vivo through Cre-dependent targeting and complementation of modified rabies virus. Proc. Natl. Acad. Sci. U S A107, 21848–21853. 10.1073/pnas.1011756107

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 194

  WangH.VilelaM.WinklerA.TarnawskiM.SchlichtingI.YumerefendiH.et al. (2016). LOVTRAP: an optogenetic system for photoinduced protein dissociation. Nat. Methods13, 755–758. 10.1038/nmeth.3926

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 195

  WangY.XuC.XuZ.JiC.LiangJ.WangY.et al. (2017). Depolarized GABAergic signaling in subicular microcircuits mediates generalized seizure in temporal lobe epilepsy. Neuron95:1221. 10.3410/f.727748591.793543895

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 196

  WardenM. R.CardinJ. A.DeisserothK. (2014). Optical neural interfaces. Annu. Rev. Biomed. Eng.16, 103–129. 10.1146/annurev-bioeng-071813-104733

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 197

  WeitzA. J.FangZ.LeeH. J.FisherR. S.SmithW. C.ChoyM.et al. (2015). Optogenetic fMRI reveals distinct, frequency-dependent networks recruited by dorsal and intermediate hippocampus stimulations. Neuroimage107, 229–241. 10.1016/j.neuroimage.2014.10.039

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 198

  WeitzA. J.LeeJ. H. (2016). Probing neural transplant networks in vivo with optogenetics and optogenetic fMRI. Stem Cells Int.2016:8612751. 10.1155/2016/8612751

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 199

  WietekJ.WiegertJ. S.AdeishviliN.SchneiderF.WatanabeH.TsunodaS. P.et al. (2014). Conversion of channelrhodopsin into a light-gated chloride channel. Science344, 409–412. 10.1126/science.1249375

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 200

  WoodsonW.NiteckaL.Ben-AriY. (1989). Organization of the GABAergic system in the rat hippocampal formation: a quantitative immunocytochemical study. J. Comp. Neurol.280, 254–271. 10.1002/cne.902800207

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 201

  WrightG. D.McLellanD. L.BriceJ. G. (1984). A double-blind trial of chronic cerebellar stimulation in twelve patients with severe epilepsy. J. Neurol. Neurosurg. Psychiatry47, 769–774. 10.1136/jnnp.47.8.769

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 202

  WuJ. W.HussainiS. A.BastilleI. M.RodriguezG. A.MrejeruA.RilettK.et al. (2016). Neuronal activity enhances tau propagation and tau pathology in vivo. Nat. Neurosci.19, 1085–1092. 10.1038/nn.4328

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 203

  WykesR. C.HeeromaJ. H.MantoanL.ZhengK.MacDonaldD. C.DeisserothK.et al. (2012). Optogenetic and potassium channel gene therapy in a rodent model of focal neocortical epilepsy. Sci. Transl. Med.4:161ra152. 10.1126/scitranslmed.3004190

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 204

  YamamotoK.TaneiZ.HashimotoT.WakabayashiT.OkunoH.NakaY.et al. (2015). Chronic optogenetic activation augments aβ pathology in a mouse model of Alzheimer disease. Cell Rep.11, 859–865. 10.1016/j.celrep.2015.04.017

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 205

  Yazdan-ShahmoradA.TianN.KharaziaV.SamaranchL.KellsA.BringasJ.et al. (2018). Widespread optogenetic expression in macaque cortex obtained with MR-guided, convection enhanced delivery (CED) of AAV vector to the thalamus. J. Neurosci. Methods293, 347–358. 10.1016/j.jneumeth.2017.10.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 206

  YekhlefL.BreschiG. L.LagostenaL.RussoG.TavernaS. (2015). Selective activation of parvalbumin- or somatostatin-expressing interneurons triggers epileptic seizurelike activity in mouse medial entorhinal cortex. J. Neurophysiol.113, 1616–1630. 10.1152/jn.00841.2014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 207

  YiF.DeCanE.StollK.MarceauE.DeisserothK.LawrenceJ. J. (2015). Muscarinic excitation of parvalbumin-positive interneurons contributes to the severity of pilocarpine-induced seizures. Epilepsia56, 297–309. 10.1111/epi.12883

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 208

  YizharO.FennoL. E.DavidsonT. J.MogriM.DeisserothK. (2011a). Optogenetics in neural systems. Neuron71, 9–34. 10.1016/j.neuron.2011.06.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 209

  YizharO.FennoL. E.PriggeM.SchneiderF.DavidsonT. J.O’SheaD. J.et al. (2011b). Neocortical excitation/inhibition balance in information processing and social dysfunction. Nature477, 171–178. 10.1038/nature10360

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 210

  YuW.Krook-MagnusonE. (2015). Cognitive collaborations: bidirectional functional connectivity between the cerebellum and the hippocampus. Front. Syst. Neurosci.9:177. 10.3389/fnsys.2015.00177

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 211

  ZapponeC. A.SloviterR. S. (2001). Commissurally projecting inhibitory interneurons of the rat hippocampal dentate gyrus: a colocalization study of neuronal markers and the retrograde tracer Fluoro-gold. J. Comp. Neurol.441, 324–344. 10.1002/cne.1415

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 212

  ZeidlerZ.Krook-MagnusonE. (2016). One site to rule them all: toward a master regulator of ictal activity. Epilepsy Curr.16, 170–171. 10.5698/1535-7511-16.3.170

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 213

  ZemelmanB. V.LeeG. A.NgM.MiesenböckG. (2002). Selective photostimulation of genetically chARGed neurons. Neuron33, 15–22. 10.1016/s0896-6273(01)00574-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 214

  ZhangF.GradinaruV.AdamantidisA. R.DurandR.AiranR. D.de LeceaL.et al. (2010). Optogenetic interrogation of neural circuits: technology for probing mammalian brain structures. Nat. Protoc.5, 439–456. 10.1038/nprot.2009.226

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 215

  ZhangW.HuguenardJ. R.BuckmasterP. S. (2012). Increased excitatory synaptic input to granule cells from hilar and CA3 regions in a rat model of temporal lobe epilepsy. J. Neurosci.32, 1183–1196. 10.1523/jneurosci.5342-11.2012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 216

  ZhangW.LohmanA. W.ZhuravlovaY.LuX.WiensM. D.HoiH.et al. (2017). Optogenetic control with a photocleavable protein, PhoCl. Nat. Methods14, 391–394. 10.1038/nmeth.4222

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 217

  ZhangF.PriggeM.BeyrièreF.TsunodaS.MattisJ.YizharO.et al. (2008). Red-shifted optogenetic excitation: a tool for fast neural control derived from Volvox carteri. Nat. Neurosci.11, 631–633. 10.1038/nn.2120

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 218

  ZhangW.YamawakiR.WenX.UhlJ.DiazJ.PrinceD. A.et al. (2009). Surviving hilar somatostatin interneurons enlarge, sprout axons, and form new synapses with granule cells in a mouse model of temporal lobe epilepsy. J. Neurosci.29, 14247–14256. 10.1523/jneurosci.3842-09.2009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 219

  ZhangH.ZhaoH.ZengC.Van DortC.FaingoldC. L.TaylorN. E.et al. (2018). Optogenetic activation of 5-HT neurons in the dorsal raphe suppresses seizure-induced respiratory arrest and produces anticonvulsant effect in the DBA/1 mouse SUDEP model. Neurobiol. Dis.110, 47–58. 10.1016/j.nbd.2017.11.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 220

  ZhouX. X.ChungH. K.LamA. J.LinM. Z. (2012). Optical control of protein activity by fluorescent protein domains. Science338, 810–814. 10.1126/science.1226854

  • Pubmed Abstract
  • CrossRef
  • Google Scholar