Shlizerman, E. | bioRxiv Science

bioRxiv2017

Neural Interactome: Interactive Simulation of a Neuronal System

Both connectivity and biophysical processes determine the functionality of neuronal networks. We, therefore, develop a real-time framework, called Neural Interactome1, to simultaneously visualize and interact with the structure and dynamics of such networks. Neural Interactome is a cross-platform framework, which combines graph visualization with the simulation of neural dynamics, or experimentally recorded multi neural time series, to allow application of stimuli to neurons to examine network responses. In addition, Neural Interactome supports structural changes, such as disconnection of neurons from the network (ablation feature), as typically done in experiments. Neural dynamics can be explored on a single neuron level (using a zoom feature), back in time (using a review feature) and recorded (using presets feature). We implement the framework using a model of the nervous system of Caenorhabditis elegans (C. elegans) nematode, a model organism for which full connectome and neural dynamics have been resolved. We show that Neural Interactome assists in studying neural response patterns associated with locomotion and other stimuli. In particular, we demonstrate how stimulation and ablation help in identifying neurons that shape particular dynamics. We examine scenarios that were experimentally studied, such as touch response circuit, and explore new scenarios that did not undergo elaborate experimental studies. The development of the Neural Interactome was guided by generic concepts to be applicable to neuronal networks with different neural connectivity and dynamics.\n\nAuthor SummaryEmerging neuroimaging techniques and novel optical interfaces which record and control neural dynamics enable detailed computational connectivity and dynamics models for neurobiological systems. An open question stemming from these advances is how to validate, simulate and apply these models to predict network functionality. Supervised empirical exploration to identify functional stimulations is an elaborate process, and direct computational approach of sequential stimulation is also formidable since produces large amounts of data without clarity on how it can be used to steer toward meaningful functionalities. We therefore develop a platform to inspect network dynamics in real time while preserving structural connectivity properties, displaying the dynamics on a graph, with possibilities to identify functional sub circuits and review the simulated dynamics. The platform allows for real time interactions with the network such as variation of stimuli and performing connectivity changes as neural ablation. We apply the platform to Caenorhabditis elegans nematode nervous system model. We revisit experimentally known scenarios of stimulations and show how our platform helps to detect associated neural dynamic patterns within seconds through few interactions. In addition, we show how the platform could provide novel hypotheses for scenarios that were not yet explored empirically. By implementing the platform with flexibility for changes in connectivity and dynamic models, this work sets forth a generic methodology applicable to various neurobiological systems.

neuroscience↗

bioRxiv2017

Functional Connectomics from Data: Probabilistic Graphical Models for Neuronal Network of C. elegans

We propose a data-driven approach to represent neuronal network dynamics as a Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by employing dimension reduction to network response dynamics evoked by stimuli applied to each neuron separately. The outcome model captures how stimuli propagate through the network and thus represents functional dependencies between neurons, i.e., functional connectome. The benefit of using a PGM as the functional connectome is that posterior inference can be done efficiently and circumvent the complexities in direct inference of response pathways in dynamic neuronal networks. In particular, posterior inference reveals the relations between known stimuli and downstream neurons or allows to query which stimuli are associated with downstream neurons. For validation and as an example for our approach we apply our methodology to a model of Caenorhabiditis elegans nervous system which structure and dynamics are well-studied. From its dynamical model we collect time series of the network response and use singular value decomposition to obtain a low-dimensional projection of the time series data. We then extract dominant patterns in each data matrix to get pairwise dependency information and create a graphical model for the full somatic nervous system. The PGM enables us to obtain and verify underlying neuronal pathways dominant for known behavioral scenarios and to detect possible pathways for novel scenarios.

bioinformatics↗