Maximally selective single cell target for circuit control in epilepsy

Abstract

Abstract Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. We studied epileptic circuits using a newly developed integrated computational pipeline applied to cellular resolution functional imaging data. Control and preseizure neural dynamics in larval zebrafish and in chronically epileptic mice were captured using large-scale cellular-resolution calcium imaging. Biologically constrained effective connectivity modeling extracted the underlying cell-cell communication network. Novel analysis of the higher-order network structure revealed the existence of ‘superhub’ cells that are unusually richly connected to the rest of the network through feedforward motifs. Instability in epileptic networks was causally linked to superhubs whose involvement in feedforward motifs critically enhanced downstream excitation. Disconnecting individual superhubs was significantly more effective in stabilizing epileptic networks compared to disconnecting hub cells defined traditionally by connection count. Collectively, these results predict a new, maximally selective and minimally invasive cellular target for seizure control. Highlights Higher-order connectivity patterns of large-scale neuronal communication networks were studied in zebrafish and mice Control and epileptic networks were modeled from in vivo cellular resolution calcium imaging data Rare ‘superhub’ cells unusually richly connected to the rest of the network through higher-order feedforward motifs were identified Disconnecting single superhub neurons more effectively stabilized epileptic networks than targeting conventional hub cells defined by high connection count. These data predict a maximally selective novel single cell target for minimally invasive seizure control