Exploring Granger Causality As A Tool For Understanding                                                            Connect...
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MEA2010 Poster

  1. 1. Exploring Granger Causality As A Tool For Understanding Connectivity In Patterned Networks Sankaraleengam Alagapan, Liangbin Pan, Eric Franca, Bruce Wheeler, and Thomas DeMarse J Crayton Pruitt Department of Biomedical Engineering, University of Florida, USA Introduction Results Analysis of Data From Microwells and Microtunnels • Granger Causality(GC) is a statistical measure finding widespread use in Neuroscience especially in LFPs and EEGs for assessing functional connectivity Bin size=1 ms Tau = 4ms between brain structures • Ordered living neural networks, with controlled connectivity, provide a test of measures of functional connectivity • Sequential plating of neurons in microwells ideally leads to tunnels filled with axons Output well (B) Tunnels to Well B Well A to Well B extending from the older to the younger population and hence unidirectional Tunnels propagation. Tunnels Well B to Tunnels • Here we use data from unidirectional microtunnel networks to validate Granger Well A to Tunnels Causality’s ability to infer direction of functional connectivity • Here we have used data from predominantly unidirectional networks that are grown in microtunnel devices to validate GC ability to infer direction of functional connectivity Well B to Well A Tunnels to Well A Input well (A) Methods Microtunnel Devices (Fig 1) • PDMS Devices Tunnels - (3µm x 10µm x 400µm), Wells 1 mm high • Well B cultured 10 days after culture in Well A • Spontaneous Activity recorded 10 days after culture in Well B Input Well Well to Well Bin size=10 ms Tau = 40ms Pre-processing for Granger Analysis • Spikes detected – Threshold crossing – Threshold = 5σ • Spiketimes  Spiketrains (Bin size = 1ms or 10ms) • Spiketrains smoothed with exponential curve (τ = 4ms or 40ms) to get a continuous Output well (B) Tunnels to Well B Well A to Well B waveform (Fig 2) Tunnels • Conditional Granger analysis using GCCA Toolbox4 Spike Train Tunnels Well B to Tunnels Well A to Tunnels Microwell B (Output Well) Row 4 Microtunnels Well B to Well A Tunnels to Well A Input well (A) Well to Well Row 5 Smoothed Waveform Microwell A (Input Well) Arrow indicates direction 100 µm of growth of axons (a) (b) Fig 1. (Left) Micrograph showing tunnels and wells on an MEA Fig 4. Connectivity Diagrams (Left) and Granger Causal Values between different electrodes under the microtunnels and (Right) Schematic showing design of the microtunnel device microwells (Right) for different values of bin size and tau. Connectivity Diagram shows only stronger connections (GC values >0.025) Results Conclusion Conditional Granger Values Between Electrodes Under Microtunnels Analysis of Data From 24 25 0.14 Microtunnels (Fig 3) 34 0.12 35 • Granger analysis on the data from microtunnels shows that GC is effective in • By design, action potentials should 44 0.1 45 determining directionality from neuronal data. propagate from row 5 to row 4. This is Target 54 0.08 55 • Analysis of data from the entire system shows the necessity to consider the time supported by two measures. 64 0.06 65 scale of interactions to obtain the exact connectivity structure – Values of causal connection from 74 75 0.04 row 5 to row 4 are higher than for 0.02 84 85 row 4 to row 5 24 25 34 35 44 45 54 55 64 65 74 75 84 85 Source Acknowledgement and References – Cross-correlograms show a (a) significant peak at a delay at Channel_85 to Channel_84 Channel_55 to Channel_54 This work was partly supported by NIH grant NS052233 positive delay from row 4 to row 5 400 200 • However, in 15% of the cases, Counts/bin Counts/bin 200 100 1. Cadotte AJ, Demarse TB, He P, Ding M. Causal Measures of Structure and propagation was in the other direction Plasticity in Simulated and Living Neural Networks. PLOS One. 2008;3(10). as indicated by cross-correlogram 0 -2 -1 0 Time (ms) 1 2 0 -2 -1 0 Time (ms) 1 2 2. Dworak BJ, Wheeler BC. Novel MEA platform with PDMS microtunnels enables the and Granger analysis. (Channels 84- (b) detection of action potential propagation from isolated axons in culture. Lab on a 85 in Fig 3) Fig 3 (a) Granger Causal Values from source(columns) to targets(rows) Chip. 2009:404-410. (b) Cross Correlograms for 2 examples showing 3. Ding, M., Chen, Y., & Bressler, S.L. Granger causality: Basic theory and application unidirectional(right) and bidirectional(left) propagation to neuroscience Winterhalder, N., & Timmer, J. Schelter. S. Handbook of Time Series Analysis of Data From Microwells and Microtunnels (Fig 4) Analysis. Wienheim : Wiley, 2006. • Causal Measures are Sensitive to Time Constant: see graphs at top of next column 4. Seth AK. A MATLAB toolbox for Granger causal connectivity analysis. Journal of – 1 ms bins, tau=4 ms: low causality within and between group activity in the wells, neuroscience methods. 2010;186(2):262-73 but high values for propagation by axons – 10 ms bins, tau=40 ms: high causality within and between wells, but little for axonal propagationTEMPLATE DESIGN © 2008www.PosterPresentations.com