- Poster presentation
- Open Access
State-dependent network reconstruction from calcium imaging signals
© Stetter et al; licensee BioMed Central Ltd. 2011
- Published: 18 July 2011
- Degree Distribution
- Gene Regulatory Network
- Cluster Coefficient
- Structural Connectivity
- Directed Network
Calcium imaging has become a standard technique for the measurement of the activity of a population of cultured neurons. Typically these recordings are slow compared to the cell dynamics and display a low signal-to-noise ratio, but they allow for the simultaneous recording of hundreds of neurons.
We are interested in reconstructing an approximation of the structural connectivity of a culture of neurons. This would allow for characterization of the bulk properties of these networks, such as the dependence of connection probability of two nodes on the distance between them, the degree distribution or the clustering coefficient, which are currently inaccessible with single-cell or even typical multi-electrode techniques. In order to benchmark our connectivity inference methods, we first study simulations of fluorescence signals and examine established methods of inferring the topology. It turns out that we can improve on these methods if we turn to measures from information theory, which do not rely on a linearity assumption.
We demonstrate post-processing improvements of the reconstruction using the Data Processing Inequality that are only possible in the case of information theoretical measures. These methods, already applied with success in the reconstruction of gene regulatory networks , help to discriminate indirect from direct interactions.
We then apply our algorithm to real data from large cultures of hippocampal neurons in vitro stained with Fluo-4 AM dye. We probe and quantify the distance-dependent probability of connection and other topological properties of the reconstructed network, finding deviations from a random topology.
Finally we point out and quantify which experimental parameters would be most relevant for an improved reconstruction using our method.
- Schreiber T: Measuring Information Transfer. Phys Rev Lett. 2000, 85: 461-464. 10.1103/PhysRevLett.85.461.View ArticlePubMedGoogle Scholar
- Gourevitch B, Eggermont J: Evaluating Information Transfer Between Auditory Cortical Neurons. J Neurophysiol. 2007, 97 (3): 2533-2543. 10.1152/jn.01106.2006.View ArticlePubMedGoogle Scholar
- Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera R, Califano A, ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinformatics. 2006, 7 (Suppl. 1): S7-10.1186/1471-2105-7-S1-S7.PubMed CentralView ArticlePubMedGoogle Scholar
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