A major impediment to progress towards reproducible, rigorous preclinical research in epilepsy is that current experimental approaches often require prohibitively time and labor-intensive 24/7 video-EEG monitoring and inherently subjective scoring of seizures by human observers. In collaboration with the Datta lab at Harvard, who showed that complex animal behaviors are structured in stereotyped modules (“syllables”) at sub-second timescales arranged according to specific rules (“grammar”), we are developing a technique employing video imaging with a 3D camera and artificial intelligence (AI)-assisted video analysis to assess epileptic behavior in an automated and unbiased manner.

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We use high-density silicon probes in the hippocampus and related circuits to record from single neurons and monitor the local field potential dynamics with high spatiotemporal resolution. When combined with optogenetics, we can “tag” neurons to achieve cell-type specific recordings as mice navigate a cue-rich treadmill while head-fixed or during restraint-free behavior. Optogenetics also enables us to study how specific cells within and outside of the hippocampus control important LFP dynamics, including theta and sharp-wave ripples. We use a variety of electrode layouts, including Neuropixel probes, to cover a range of research questions.

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Optogenetics is a control technology that allows the fast, selective excitation or inhibition of specific neurons with light by expressing light-sensitive proteins (opsins) in particular cell types, enabling causal control of neuronal activity in behaving animals. We have developed a closed-loop, real-time, on-demand system to utilize optogenetics to provide effective seizure control in experimental models of temporal lobe epilepsy (TLE) (Krook-Magnuson et al., 2013) in a spatial, temporal, cell-type and direction of modulation-(excitation or inhibition) selective manner. Importantly, closed-loop optogenetic intervention (COI) was capable not only of curtailing electrographic seizures, but also of significantly decreasing the number of behavioral seizures. COI has proven to be a powerful new tool to understand epileptic circuits, and our lab has used it to directly test the role of the cerebellum as a distant site for seizure control in TLE (Krook-Magnuson et al., 2014) and the role of mossy cells in epilepsy (Bui et al., 2018). One of the most attractive features of COI is that the intervention is highly selective, occurring only when needed (at the time of seizures) and where it is needed, causing selective disruption of seizures while only minimally interfering with ongoing computations necessary for normal brain function. We have recently shown that prolonged application of COI can ameliorate cognitive deficits in TLE (Kim et al., 2019), providing evidence that this technology can improve both seizure burden and the associated comorbidities of epilepsy. Continuing work in the Soltesz lab is focused on utilizing COI as well as incorporating recently developed molecular tools to use with the system to identify and understand epileptic circuits both within and outside of the hippocampus. Ultimately our work aims to provide critical insight into potential targets and avenues for intervention in the treatment of epilepsy.

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To monitor the activity of specific cells during unrestricted behavior, we use open-source head-mounted miniscopes developed at UCLA (miniscope.org) to measure calcium activity. The main advantage of this approach over head-fixed 2-photon imaging is the ability to perform a broader range of behavioral tasks including artificial intelligence-based behavioral analysis. Combining these cell type-specific recordings with unbiased sub-second behavioral analysis using AI facilitates the characterization of the cellular underpinnings of behavior at a resolution not possible with conventional approaches.

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We’re using 2-photon microscopy to record neuronal activity with single-cell resolution in the hippocampus of awake, behaving mice. Taking advantage of cell type-specific viral targeting of various biosensors, our goal is to better understand how distinct populations of excitatory and inhibitory neurons are recruited during network oscillations in the healthy brain, and during pathological activity such as seizures in the epileptic brain.

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The hippocampal circuits that store and recall spatial information are comprised of diverse cell types, each exhibiting distinct dynamics and complex patterns of synaptic connectivity. Thus, even highly specific experimental perturbations of a single component of these neuronal circuits can have counterintuitive effects on their internal dynamics and output. Computational modeling offers experimentalists a framework to integrate their knowledge, make explicit the assumptions of their conceptual models, and quantitatively predict how each element of a neuronal network is expected to respond to cell type- or projection-specific perturbations. In the Soltesz lab we build computational models in close collaboration with experimentalists, both in the lab, across Stanford, and at other institutions through a multi-site NIH BRAIN Initiative collaboration. Our large-scale network models of the hippocampus are continuously refined to incorporate newly obtained experimental constraints, and numerical simulations are carried out to test hypotheses, compare candidate biophysical and network mechanisms for memory storage and recall, and aide in the interpretation of physiological and behavioral experimental data. The ultimate goal of these efforts is to obtain a deep conceptual understanding of the cellular and network mechanisms that mediate “memory replay” events called sharp-wave ripples by simulating a large-scale model of the hippocampus that reproduces for the observed firing properties of all cell types during sharp-waves.

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