Biology subjects

Raikov, I.

Publications and source records attributed to Raikov, I..

3 recordsLinked to original sources

bioRxiv2020

Higher-order hub cells involved in feedforward motifs as critical factors in epileptic network instability

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. HighlightsO_LIHigher-order connectivity patterns of large-scale neuronal communication networks were studied in zebrafish and mice C_LIO_LIControl and epileptic networks were modeled from in vivo cellular resolution calcium imaging data C_LIO_LIRare superhub cells unusually richly connected to the rest of the network through higher-order feedforward motifs were identified C_LIO_LIDisconnecting single superhub neurons more effectively stabilized epileptic networks than targeting conventional hub cells defined by high connection count. C_LIO_LIThese data predict a maximally selective novel single cell target for minimally invasive seizure control C_LI

neuroscience↗

bioRxiv2016

Interneuronal mechanisms of hippocampal theta oscillation in a full-scale model of the rodent CA1 circuit

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 CA1 area and studied its interneurons during theta oscillations. Theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct in terneuronal 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.

neuroscience↗

bioRxiv2016

SimTracker tool and code template to design, manage and analyze neural network model simulations in parallel NEURON

Advances in technical computing enable larger and more detailed neural models that can incorporate ever more of the rapidly expanding body of quantitative neuroscience data. In principle, such complex network models that are strongly constrained by experimental data could direct experimental research and provide novel insights into experimental observations. However, as network models grow in complexity and scale, the necessary tasks of development and organization become unwieldy. Further, the models risk becoming inaccessible to experimentalists and other modelers, and their results may then be seen as less relevant to experimental work. To address these obstacles, we developed a tool for managing simulations called SimTracker. It supports users at each step of the modeling process, including execution of large scale parallel models on supercomputers. SimTracker is suitable for users with a range of modeling experience. SimTracker can be a valuable modeling resource that promotes iterative progress between experiment and model.

neuroscience↗