BMES 2010 poster


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My poster for BMES 2010 held at Austin, TX

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BMES 2010 poster

  1. 1. Validation of Granger Causality using Unidirectional Neuronal Networks Sankaraleengam Alagapan, Liangbin Pan, Bruce Wheeler and Thomas DeMarse J Crayton Pruitt Family Department of Biomedical Engineering University of Florida Introduction Methods Results Results Materials • Neurons cultured on microelectrode arrays (MEAs) form an • MEAs (Commercial (MCS, Germany)) 8 x 8 array, 200µm 60 Analysis of Data From Microwells and Microtunnels (Fig • Average causal values from Input well to Output well is higher interesting system to study living (in vitro) neuronal network TiN electrodes on Glass substrate with SiN2 insulation 5) than Output well to Input well properties • Dissociated neuronal culture from E18 rats, Medium: • Causal Measures are Sensitive to Time Constant: • Microfluidic devices consisting of wells connected by tunnels Neurobasal/ B27/Glutamax – 1 ms bins, tau=4 ms: low causality within and between Bin size=10 ms Tau = 40ms enable control over the direction of connections in in vitro group activity in the wells, but high values for propagation networks Microtunnel Devices (Fig 2) by axons • Sequential plating of neurons in wells ideally leads to tunnels • PDMS Devices Tunnels - (3µm x 10µm x 400µm), Wells 1 mm – 10 ms bins, tau=40 ms: high causality within and filled with axons extending from the older to the younger high population and hence unidirectional propagation between wells, but little for axonal propagation • Well B cultured 10 days after culture in Well A • Spontaneous Activity recorded 10 days after culture in Well B Spike Train Bin size=1 ms Tau = 4ms Microwell B (Output Well) Row 4 Output well (B) Tunnels to Well B Well A to Well B Microtunnels Row 5 Smoothed Waveform Tunnels MEA Dissociated Neuronal Culture Multi Electrode Recordings Microwell A (Input Well) Well B to Tunnels Well A to Tunnels Arrow indicates direction 100 µm of growth of axons (a) (b) (a) (b) (c) Fig 6 Average Region wise Causal Values (Average values from Fig 5(a) bottom Fig 3. (a)Schematic of the device coupled MEA (b) Micrograph of device and Well B to Well A Tunnels to Well A Input well Microtunnel Device Microtunnel Device MEA (c) Preprocessing for Granger Analysis (A) Fig 1. Overview of technology used in this work Results Conclusion • Granger Causality(GC) is a statistical measure finding Analysis of Data From Microtunnels (Fig 4) Bin size=10 ms Tau = 40ms widespread use in Neuroscience especially in LFPs and EEGs • By design, action potentials should propagate from row 5 to • Granger analysis on the data from microtunnels shows that for assessing functional connectivity between brain structures row 4. This is supported by two measures. Granger Causality is effective in determining directionality • Here we use data from unidirectional microtunnel networks – Values of causal connection from row 5 to row 4 are higher from neuronal data. to validate Granger Causality’s ability to infer direction of Output well (B) Tunnels to Well B Well A to Well B than for row 4 to row 5 • Analysis of data from the entire system shows the functional connectivity Tunnels – Cross-correlograms show a significant peak at a delay at necessity to consider the time scale of interactions to Well B to Tunnels Methods Well A to Tunnels positive delay from row 4 to row 5 obtain the exact connectivity structure • However, in 15% of the cases, propagation was in the other Granger Causality direction as indicated by cross-correlogram and Granger • Suppose X and Y are 2 time 0.5 Illustration analysis. (Channels 84-85 in Fig 4) Acknowledgement 1 2 Well B to Well A Tunnels to Well A Input well series modeled as autoregressive 0.25 Conditional Granger Values Between Electrodes Under Microtunnels (A) processes, Y G-causes X if the 24 25 0.14 Funded by National Institute of Health (NIH) NS052233 including past of Y in modeling X 34 0.12 35 0.25 decreases the variability of 44 45 0.1 (a) References residuals in the model and vice Target 54 0.08 Bin size=1 ms Tau = 4ms Bin size=10 ms Tau = 40ms versa. 0. 5 55 64 0.06 1. Cadotte AJ, Demarse TB, He P, Ding M. Causal • The amount by which the 65 variability is reduced gives a 74 0.04 Measures of Structure and Plasticity in Simulated and Living Neural Networks. PLOS One. 2008;3(10). 75 measure of strength and direction Fig2 Illustration of Granger Causality Tunnels 0.02 Tunnels 84 is revealed in the relative strengths and representation 85 24 25 34 35 44 45 54 55 64 65 74 75 84 85 2. Dworak BJ, Wheeler BC. Novel MEA platform with • Conditional Granger Causality: Same idea as Granger, with both Source PDMS microtunnels enables the detection of action (a) X and Y conditioned on another variable Z which might influence Channel_55 to Channel_54 Channel_85 to Channel_84 potential propagation from isolated axons in culture. Lab the two time series (b) on a Chip. 2009:404-410. 3. Ding, M., Chen, Y., & Bressler, S.L. Granger causality: 400 200 Fig 5 (a) Granger Causal Values between different electrodes under the Counts/bin Counts/bin Pre-processing for Granger Analysis (Fig 3c) 100 200 microtunnels and microwells for different values of bin size and tau. Basic theory and application to neuroscience • Spikes detected – Threshold crossing – Threshold = 5σ (b) Connectivity Diagrams for different bin size and tau. Connectivity Diagram Winterhalder, N., & Timmer, J. Schelter. S. Handbook of • Spiketimes  Spiketrains (Bin size = 1ms or 10ms) 0 -2 -1 0 1 2 0 -2 -1 0 1 2 shows only stronger connections (GC values >0.025) Time Series Analysis. Wienheim : Wiley, 2006. • Spiketrains smoothed with exponential curve (τ = 4ms or 40ms) 4. Seth AK. A MATLAB toolbox for Granger causal Time (ms) Time (ms) to get a continuous waveform (b) Fig 4(a) Granger Causal Values from source(columns) to targets(rows) (b) connectivity analysis. Journal of neuroscience methods. • Conditional Granger analysis using GCCA Toolbox4 Cross Correlograms for 2 examples showing unidirectional (left) and 2010;186(2):262-73 bidirectional (right) propagationTEMPLATE DESIGN ©