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Computational processing of optical measurements of neuronal and synaptic activity in networks.

ABSTRACT: Imaging of optical reporters of neural activity across large populations of neurones is a widely used approach for investigating the function of neural circuits in slices and in vivo. Major challenges in analysing such experiments include the automatic identification of neurones and synapses, extraction of dynamic signals, and assessing the temporal and spatial relationships between active units in relation to the gross structure of the circuit. We have developed an integrated set of software tools, named SARFIA, by which these aspects of dynamic imaging experiments can be analysed semi-automatically. Key features are image-based detection of structures of interest using the Laplace operator, determining the positions of units in a layered network, clustering algorithms to classify units with similar functional responses, and a database to store, exchange and analyse results across experiments. We demonstrate the use of these tools to analyse synaptic activity in the retina of live zebrafish by multi-photon imaging of SyGCaMP2, a genetically encoded synaptically localised calcium reporter. By simultaneously recording activity across tens of bipolar cell terminals distributed throughout the IPL we made a functional map of the ON and OFF signalling channels and found that these were only partially separated. The automated detection of signals across many neurones in the retina allowed the reliable detection of small populations of neurones generating "ectopic" signals in the "ON" and "OFF" sublaminae. This software should be generally applicable for the analysis of dynamic imaging experiments across hundreds of responding units.

SUBMITTER: Dorostkar MM

PROVIDER: S-EPMC2849931 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology.

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Computational processing of optical measurements of neuronal and synaptic activity in networks.

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