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Current treatment options are, for many patients with epilepsy, either insufficient or ineffective and, thus, new therapeutic methodologies are necessary. In this Perspective, Esther Krook-Magnuson and Ivan Soltesz look at recent advances in optogenetic-based modulation of circuit activity and seizures with an eye toward the prospect - and challenges - of utilizing these technologies for the treatment of epilepsy. Current treatment options for epilepsy are inadequate, as too many patients suffer from uncontrolled seizures and from negative side effects of treatment. In addition to these clinical challenges, our scientific understanding of epilepsy is incomplete. Optogenetic and designer receptor technologies provide unprecedented and much needed specificity, allowing for spatial, temporal and cell type-selective modulation of neuronal circuits. Using such tools, it is now possible to begin to address some of the fundamental unanswered questions in epilepsy, to dissect epileptic neuronal circuits and to develop new intervention strategies. Such specificity of intervention also has the potential for direct therapeutic benefits, allowing healthy tissue and network functions to continue unaffected. In this Perspective, we discuss promising uses of these technologies for the study of seizures and epilepsy, as well as potential use of these strategies for clinical therapies.

Temporal lobe epilepsy is the most common type of epilepsy in adults, is often medically refractory, and due to broad actions and long-time scales, current systemic treatments have major negative side-effects. However, temporal lobe seizures tend to arise from discrete regions before overt clinical behaviour, making temporally and spatially specific treatment theoretically possible. Here we report the arrest of spontaneous seizures using a real-time, closed-loop, response system and in vivo optogenetics in a mouse model of temporal lobe epilepsy. Either optogenetic inhibition of excitatory principal cells, or activation of a subpopulation of GABAergic cells representing <5% of hippocampal neurons, stops seizures rapidly upon light application. These results demonstrate that spontaneous temporal lobe seizures can be detected and terminated by modulating specific cell populations in a spatially restricted manner. A clinical approach built on these principles may overcome many of the side-effects of currently available treatment options. Temporal lobe epilepsy in adults does not always respond to treatment. Krook-Magnuson and colleagues use optogenetics to inhibit and activate excitatory and inhibitory neurons, respectively, in a mouse model of temporal lobe epilepsy, and find that they can stop seizures on a moment-to-moment basis.

Endogenous brain rhythms occurring at various frequencies and associated with distinct behavioral states provide multiscale temporal windows that enable cells to time their spiking activity with high precision, which is thought to be important for the coding of information in neuronal circuits. However, although the selective timing of GABAergic inputs to specific spatial domains of principal cells are known to play key roles in network oscillations, the in vivo firing patterns of distinct hippocampal interneurons in awake animals are not known. Here we used a combination of juxtacellular labeling techniques with recordings from anesthesia-free, head-fixed mice running or resting on a spherical treadmill to study the oscillation-dependent discharges by two major interneuronal subtypes, the perisomatically projecting parvalbumin-positive basket cells (PVBCs) and distal dendritically projecting oriens lacunosum moleculare (OLM) cells. Recordings of the spiking activity of post hoc-identified CA1 interneurons during theta (5–10 Hz), gamma (25–90Hz), epsilon (“high-gamma”; 90–130 Hz), and ripple (130–200 Hz) oscillations revealed both cell type- and behavioral state-dependent entrainments of PVBC and OLM cell discharges in awake mice. Our results in awake mice differed in several respects from previous data on interneuronal discharge patterns in anesthetized animals. In addition, our results demonstrate a form of frequency-invariant, cell type-specific temporal ordering of inhibitory inputs in which PVBC-derived perisomatic inhibition is followed by OLM cell-generated distal dendritic inhibition during each of the network oscillation bands studied, spanning more than an order of magnitude in frequencies.

Temporal lobe epilepsy is the most common form of epilepsy in adults. Patients have spontaneous seizures and risk developing serious cognitive impairment. Bui et al. studied an animal model of temporal lobe epilepsy (see the Perspective by Scharfman). Selective optogenetic inhibition of dentate gyrus mossy cells increased the likelihood of electrographic seizures generalizing to full behavioral convulsive seizures. Activation of mossy cells reduced the likelihood. Thus, the activity of mossy cells might serve to inhibit seizure propagation. Science , this issue p. 787 ; see also p. 740 There is a direct relationship between mossy cell activity in the dentate gyrus, convulsive seizures, and spatial memory formation in mice. Temporal lobe epilepsy (TLE) is characterized by debilitating, recurring seizures and an increased risk for cognitive deficits. Mossy cells (MCs) are key neurons in the hippocampal excitatory circuit, and the partial loss of MCs is a major hallmark of TLE. We investigated how MCs contribute to spontaneous ictal activity and to spatial contextual memory in a mouse model of TLE with hippocampal sclerosis, using a combination of optogenetic, electrophysiological, and behavioral approaches. In chronically epileptic mice, real-time optogenetic modulation of MCs during spontaneous hippocampal seizures controlled the progression of activity from an electrographic to convulsive seizure. Decreased MC activity is sufficient to impede encoding of spatial context, recapitulating observed cognitive deficits in chronically epileptic mice.

Epilepsy is characterized by recurrent synchronizations of neuronal activity, which are both a cardinal clinical symptom and a debilitating phenomenon. Although the temporal dynamics of epileptiform synchronizations are well described at the macroscopic level using electrophysiological approaches, less is known about how spatially distributed microcircuits contribute to these events. It is important to understand the relationship between micro and macro network activity because the various mechanisms proposed to underlie the generation of such pathological dynamics are united by the assumption that epileptic activity is recurrent and hypersynchronous across multiple scales. However, quantitative analyses of epileptiform spatial dynamics with cellular resolution have been hampered by the difficulty of simultaneously recording from multiple neurons in lesioned, adult brain tissue. We have overcome this experimental limitation and used two-photon calcium imaging in combination with a functional clustering algorithm to uncover the functional network structure of the chronically epileptic dentate gyrus in the mouse pilocarpine model of temporal lobe epilepsy. We show that, under hyperexcitable conditions, slices from the epileptic dentate gyrus display recurrent interictal-like network events with a high diversity in the activity patterns of individual neurons. Analysis reveals that multiple functional clusters of spatially localized neurons comprise epileptic networks, and that network events are composed of the coactivation of variable subsets of these clusters, which show little repetition between events. Thus, these interictal-like recurrent macroscopic events are not necessarily recurrent when viewed at the microcircuit scale and instead display a patterned but variable structure.

The hippocampal theta rhythm plays important roles in information processing; however, the mechanisms of its generation are not well understood. We developed a data-driven, supercomputer-based, full-scale (1:1) model of the rodent CA1 area and studied its interneurons during theta oscillations. Theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct interneuronal types spontaneously emerged from the isolated CA1 circuit without rhythmic inputs. Perturbation experiments identified parvalbumin-expressing interneurons and neurogliaform cells, as well as interneuronal diversity itself, as important factors in theta generation. These simulations reveal new insights into the spatiotemporal organization of the CA1 circuit during theta oscillations.

Many complex neuronal circuits have been shown to display nonrandom features in their connectivity. However, the functional impact of nonrandom network topologies in neurological diseases is not well understood. The dentate gyrus is an excellent circuit in which to study such functional implications because proepileptic insults cause its structure to undergo a number of specific changes in both humans and animals, including the formation of previously nonexistent granule cell-to-granule cell recurrent excitatory connections. Here, we use a large-scale, biophysically realistic model of the epileptic rat dentate gyrus to reconnect the aberrant recurrent granule cell network in four biologically plausible ways to determine how nonrandom connectivity promotes hyperexcitability after injury. We find that network activity of the dentate gyrus is quite robust in the face of many major alterations in granule cell-to-granule cell connectivity. However, the incorporation of a small number of highly interconnected granule cell hubs greatly increases network activity, resulting in a hyperexcitable, potentially seizure-prone circuit. Our findings demonstrate the functional relevance of nonrandom microcircuits in epileptic brain networks, and they provide a mechanism that could explain the role of granule cells with hilar basal dendrites in contributing to hyperexcitability in the pathological dentate gyrus.

Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic calcium spikes, or plateau potentials, has been reported to underlie place field formation in rodent hippocampal CA1 neurons. Here, we found that this behavioral timescale synaptic plasticity (BTSP) can also reshape existing place fields via bidirectional synaptic weight changes that depend on the temporal proximity of plateau potentials to pre-existing place fields. When evoked near an existing place field, plateau potentials induced less synaptic potentiation and more depression, suggesting BTSP might depend inversely on postsynaptic activation. However, manipulations of place cell membrane potential and computational modeling indicated that this anti-correlation actually results from a dependence on current synaptic weight such that weak inputs potentiate and strong inputs depress. A network model implementing this bidirectional synaptic learning rule suggested that BTSP enables population activity, rather than pairwise neuronal correlations, to drive neural adaptations to experience. A new housing development in a familiar neighborhood, a wrong turn that ends up lengthening a Sunday stroll: our internal representation of the world requires constant updating, and we need to be able to associate events separated by long intervals of time to finetune future outcome. This often requires neural connections to be altered. A brain region known as the hippocampus is involved in building and maintaining a map of our environment. However, signals from other brain areas can activate silent neurons in the hippocampus when the body is in a specific location by triggering cellular events called dendritic calcium spikes. Milstein et al. explored whether dendritic calcium spikes in the hippocampus could also help the brain to update its map of the world by enabling neurons to stop being active at one location and to start responding at a new position. Experiments in mice showed that calcium spikes could change which features of the environment individual neurons respond to by strengthening or weaking connections between specific cells. Crucially, this mechanism allowed neurons to associate event sequences that unfold over a longer timescale that was more relevant to the ones encountered in day-to-day life. A computational model was then put together, and it demonstrated that dendritic calcium spikes in the hippocampus could enable the brain to make better spatial decisions in future. Indeed, these spikes are driven by inputs from brain regions involved in complex cognitive processes, potentially enabling the delayed outcomes of navigational choices to guide changes in the activity and wiring of neurons. Overall, the work by Milstein et al. advances the understanding of learning and memory in the brain and may inform the design of better systems for artificial learning.

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness. The brain is an immensely complex organ which regulates many of the behaviors that animals need to survive. To understand how the brain works, scientists monitor and record brain activity under different conditions using a variety of experimental techniques. These neurophysiological studies are often conducted on multiple types of cells in the brain as well as a variety of species, ranging from mice to flies, or even frogs and worms. Such a range of approaches provides us with highly informative, complementary ‘views’ of the brain. However, to form a complete, coherent picture of how the brain works, scientists need to be able to integrate all the data from these different experiments. For this to happen effectively, neurophysiology data need to meet certain criteria: namely, they must be findable, accessible, interoperable, and re-usable (or FAIR for short). However, the sheer diversity of neurophysiology experiments impedes the ‘FAIR’-ness of the information obtained from them. To overcome this problem, researchers need a standardized way to communicate their experiments and share their results – in other words, a ‘standard language’ to describe neurophysiology data. Rübel, Tritt, Ly, Dichter, Ghosh et al. therefore set out to create such a language that was not only FAIR, but could also co-evolve with neurophysiology research. First, they produced a computer software program (called Neurodata Without Borders, or NWB for short) which generated and defined the different components of the new standard language. Then, other tools for data management were created to expand the NWB platform using the standardized language. This included data analysis and visualization methods, as well as an ‘archive’ to store and access data. Testing the new language and associated tools showed that they indeed allowed researchers to access, analyze, and share information from many different types of experiments, in organisms ranging from flies to humans. The NWB software is open-source, meaning that anyone can obtain a copy and make changes to it. Thus, NWB and its associated resources provide the basis for a collaborative, community-based system for sharing neurophysiology data. Rübel et al. hope that NWB will inspire similar developments across other fields of biology that share similar levels of complexity with neurophysiology.

Focused ultrasound (FUS) is a powerful tool for noninvasive modulation of deep brain activity with promising therapeutic potential for refractory epilepsy; however, tools for examining FUS effects on specific cell types within the deep brain do not yet exist. Consequently, how cell types within heterogeneous networks can be modulated and whether parameters can be identified to bias these networks in the context of complex behaviors remains unknown. To address this, we developed a fiber Photometry Coupled focused Ultrasound System (PhoCUS) for simultaneously monitoring FUS effects on neural activity of subcortical genetically targeted cell types in freely behaving animals. We identified a parameter set that selectively increases activity of parvalbumin interneurons while suppressing excitatory neurons in the hippocampus. A net inhibitory effect localized to the hippocampus was further confirmed through whole brain metabolic imaging. Finally, these inhibitory selective parameters achieved significant spike suppression in the kainate model of chronic temporal lobe epilepsy, opening the door for future noninvasive therapies.