Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

NER 2013 Poster


Published on

  • Be the first to comment

  • Be the first to like this

NER 2013 Poster

  1. 1. SPATIAL DISTRIBUTION OF FUNCTIONAL CONNECTION STRENGTHS IN PATTERNED NETWORKS OF VARYING CONVERGENCE Sankar Alagapan1, Eric W. Franca1, Liangbin Pan1, Thomas B. DeMarse1, Gregory J. Brewer2 and Bruce C. Wheeler1 1 J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 2 Department of Biomedical Engineering, University of California, Irvine, CA Introduction Results • We used Conditional Granger Causality (CGC) [4],[5] as a measure of functional connectivity and Victor – Purpura’s (VP) spike train similarity metric [6] as a measure of fidelity of information propagation. Methods • Poly-d-lysine (PDL) was patterned onto each microelectrode array’s (MEA) surface (60 electrodes in 6 x 10 arrangement, 30µm diameter and 500 µm inter electrode distance) and treated with 3-glycidoxypropyltrimethoxysilane (3-GPS) by microcontact printing. 4 Connect • E18 rat cortical neurons were dissociated and plated at a density of 700 cells/mm2 Axonal Bundles • Patterns consisted of circular nodes (50µm diameter) connected to neighboring nodes by straight lines (20µm width). Convergence was varied by varying number of nearest neighboring nodes each node was connected to and the patterns are referred according to this number as shown in Fig 1 (2 Connect – 2 nearest neighbors along horizontal axis, 4 Connect – 4 nearest neighbors in both horizontal and vertical axis and 8 Connect – 8 nearest neighbors as in 4 connect but including diagonal). 2 Connect Neurons (Cell Body Cluster) Electrode 8 Connect Fig 1.Three different convergence patterns based on a serial chain (2 connection), city-block (4 connection), and 8 connection network topologies. • CGC was calculated from smoothed spike trains of the spontaneous activity recorded from these networks and VP dissimilarity metric (Dv) was calculated from the spike trains of spontaneous activity and the cost parameter varied to account for multiple lengths of time segments. Results Effect of Convergence on Functional Connection Strengths Fig 2. Overall Distribution of CGC Strengths. •We hypothesized that higher convergence may lead to higher functional connection strengths between nodes in the network. •However, the distribution of CGC strengths among patterned networks were not significantly different. (Random networks were significantly different than patterned) Effect of Convergence on Fidelity of Information Propagation Dissimilarity • Patterned networks interfaced with planar multi electrode arrays (MEAs) [1] provide a living model system to study the effect a network’s structure on its function [2],[3]. • We varied the convergence of structural connections (i.e. number of nearest connecting neighbor nodes) to study the influence of convergence on functional connectivity and fidelity of information transmission. A B Fig 4 A. Fidelity of Information Propagation vs. Convergence •Higher convergence should lead to better fidelity of information propagation between nodes in the network. Spike trains increased in similarity (decreased dissimilarity) increasing convergence but were most similar in random cultures. •Dissimilarity decreased with longer time windows supporting a strong role for a rate modulation as neural code during transmission. •Random networks had the least dissimilarity (highest fidelity) in transmission and 2 Connect networks had high dissimilarity (low fidelity) Fig 4 B. Fidelity of Information propagation vs. Distance •No significant trend was observed in the fidelity of information propagation with respect to distance between nodes Fig 5. Fidelity of information propagation vs. Path Length •Fidelity of propagating spike trains was affected by the number of mediating nodes (path length) •The effect of convergence is pronounced at shorter path lengths (path lengths of 1 and 2). •At longer path lengths effect was not significant Conclusion • Fidelity of information was high at longer time windows suggesting rate based code during propagation. • Convergence did affect the fidelity of information propagation but depended more upon path length (number of intermediate connections) than physical distance. • Convergence does not affect the functional connection strengths between nodes in a network. • Functional connectivity is affected by physical distance. This effect depends on the levels of convergence. Higher the convergence, lesser the effect distance had on connection strength. Acknowledgement This work was partly supported by NIH grant NS052233 2 Connect Fig 3. CGC strengths vs. distance. •2 Connect Networks showed a steeper decline in mean CGC strengths compared to 4 and 8 Connect Networks which in turn were steeper than Random Networks. Slope = -0.0449 Mean Normalized CGC Values •The strength of any functional connectivity decreased with distance from each node. 4 Connect References 1. Wheeler, B., Corey, J., Brewer, G. & Branch, D. (1999). Microcontact printing for precise control of nerve cell growth in culture. Journal of biomechanical engineering. 2. Boehler, M. D., Leondopulos, S. S., Wheeler, B. C. & Brewer, G. J. (2011). Hippocampal networks on reliable patterned substrates. Journal of Neuroscience Methods. Elsevier. 8 Connect Random Slope = -0.0205 Slope = -0.0064 3. Marconi, E., Nieus, T., Maccione, A., Valente, P., Simi, A., Messa, M., Dante, S., et al. (2012). Emergent Functional Properties of Neuronal Networks with Controlled Topology. PloS one. Public Library of Science 4. Ding, M., Chen, Y. & Bressler, S. L. (2006). Granger causality: basic theory and application to neuroscience. Handbook of time series analysis. Wiley Online Library. 5. Seth, A. K. (2010). A MATLAB toolbox for Granger causal connectivity analysis. Journal of neuroscience methods. Elsevier. Distance in µm TEMPLATE DESIGN © 2008 Slope = -0.0207 6. Victor, J. D. & Purpura, K. P. (1996). Nature and precision of temporal coding in visual cortex: a metric-space analysis. Journal of Neurophysiology. Am Physiological Soc.