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Chemogenetic silencing of hippocampal neurons suppresses epileptic neural circuits.

ABSTRACT: We investigated how pathological changes in newborn hippocampal dentate granule cells (DGCs) lead to epilepsy. Using a rabies virus-mediated retrograde tracing system and a designer receptors exclusively activated by designer drugs (DREADD) chemogenetic method, we demonstrated that newborn hippocampal DGCs are required for the formation of epileptic neural circuits and the induction of spontaneous recurrent seizures (SRS). A rabies virus-mediated mapping study revealed that aberrant circuit integration of hippocampal newborn DGCs formed excessive de novo excitatory connections as well as recurrent excitatory loops, allowing the hippocampus to produce, amplify, and propagate excessive recurrent excitatory signals. In epileptic mice, DREADD-mediated-specific suppression of hippocampal newborn DGCs dramatically reduced epileptic spikes and SRS in an inducible and reversible manner. Conversely, specific activation of hippocampal newborn DGCs increased both epileptic spikes and SRS. Our study reveals an essential role for hippocampal newborn DGCs in the formation and function of epileptic neural circuits, providing critical insights into DGCs as a potential therapeutic target for treating epilepsy.

SUBMITTER: Zhou QG

PROVIDER: S-EPMC6307945 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Pattern separation is a central concept in current theories of episodic memory: this computation is thought to support our ability to avoid confusion between similar memories by transforming similar cortical input patterns of neural activity into dissimilar output patterns before their long-term storage in the hippocampus. Because there are many ways one can define patterns of neuronal activity and the similarity between them, pattern separation could in theory be achieved through multiple coding strategies. Using our recently developed assay that evaluates pattern separation in isolated tissue by controlling and recording the input and output spike trains of single hippocampal neurons, we explored neural codes through which pattern separation is performed by systematic testing of different similarity metrics and various time resolutions. We discovered that granule cells, the projection neurons of the dentate gyrus, can exhibit both pattern separation and its opposite computation, pattern convergence, depending on the neural code considered and the statistical structure of the input patterns. Pattern separation is favored when inputs are highly similar, and is achieved through spike time reorganization at short time scales (< 100 ms) as well as through variations in firing rate and burstiness at longer time scales. These multiplexed forms of pattern separation are network phenomena, notably controlled by GABAergic inhibition, that involve many celltypes with input-output transformations that participate in pattern separation to different extents and with complementary neural codes: a rate code for dentate fast-spiking interneurons, a burstiness code for hilar mossy cells and a synchrony code at long time scales for CA3 pyramidal cells. Therefore, the isolated hippocampal circuit itself is capable of performing temporal pattern separation using multiplexed coding strategies that might be essential to optimally disambiguate multimodal mnemonic representations.

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Chemogenetic silencing of hippocampal neurons suppresses epileptic neural circuits.

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