Altered Network Communication Following a Neuroprotective Drug Treatment

Preconditioning is defined as a range of stimuli that allow cells to withstand subsequent anaerobic and other deleterious conditions. While cell protection under preconditioning is well established, this paper investigates the influence of neuroprotective preconditioning drugs, 4-aminopyridine and bicuculline (4-AP/bic), on synaptic communication across a broad network of in vitro rat cortical neurons. Using a permutation test, we evaluated cross-correlations of extracellular spiking activity across all pairs of recording electrodes on a 64-channel multielectrode array. The resulting functional connectivity maps were analyzed in terms of their graph-theoretic properties. A small-world effect was found, characterized by a functional network with high clustering coefficient and short average path length. Twenty-four hours after exposure to 4-AP/bic, small-world properties were comparable to control cultures that were not treated with the drug. Four hours following drug washout, however, the density of functional connections increased, while path length decreased and clustering coefficient increased. These alterations in functional connectivity were maintained at four days post-washout, suggesting that 4-AP/bic preconditioning leads to long-term effects on functional networks of cortical neurons. Because of their influence on communication efficiency in neuronal networks, alterations in small-world properties hold implications for information processing in brain systems. The observed relationship between density, path length, and clustering coefficient is captured by a phenomenological model where connections are added randomly within a spatially-embedded network. Taken together, results provide information regarding functional consequences of drug therapies that are overlooked in traditional viability studies and present the first investigation of functional networks under neuroprotective preconditioning.

[1]  Olaf Sporns,et al.  Can structure predict function in the human brain? , 2010, NeuroImage.

[2]  A. Koulakov,et al.  Correlated Connectivity and the Distribution of Firing Rates in the Neocortex , 2008, The Journal of Neuroscience.

[3]  G. Mealing,et al.  Elevated Synaptic Activity Preconditions Neurons against an in Vitro Model of Ischemia* , 2008, Journal of Biological Chemistry.

[4]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[5]  Wulfram Gerstner,et al.  Extraction of Network Topology From Multi-Electrode Recordings: Is there a Small-World Effect? , 2011, Front. Comput. Neurosci..

[6]  M. Ding,et al.  Causal Measures of Structure and Plasticity in Simulated and Living Neural Networks , 2008, PloS one.

[7]  S. Nelson,et al.  Homeostatic plasticity in the developing nervous system , 2004, Nature Reviews Neuroscience.

[8]  H. Robinson,et al.  Simultaneous induction of pathway-specific potentiation and depression in networks of cortical neurons. , 1999, Biophysical journal.

[9]  A. Aertsen,et al.  Evaluation of neuronal connectivity: Sensitivity of cross-correlation , 1985, Brain Research.

[10]  S. Shen-Orr,et al.  Networks Network Motifs : Simple Building Blocks of Complex , 2002 .

[11]  Jon M. Kleinberg,et al.  The small-world phenomenon: an algorithmic perspective , 2000, STOC '00.

[12]  Antonio Novellino,et al.  Recurrence Quantification Analysis of Spontaneous Electrophysiological Activity during Development: Characterization of In Vitro Neuronal Networks Cultured on Multi Electrode Array Chips , 2010, Adv. Artif. Intell..

[13]  Sujit K Sikdar,et al.  Small‐world network topology of hippocampal neuronal network is lost, in an in vitro glutamate injury model of epilepsy , 2007, The European journal of neuroscience.

[14]  G. Marcus,et al.  The topographic brain: from neural connectivity to cognition , 2007, Trends in Neurosciences.

[15]  J. P. Morgan,et al.  Design and Analysis: A Researcher's Handbook , 2005, Technometrics.

[16]  M. Chiappalone,et al.  Networks of neurons coupled to microelectrode arrays: a neuronal sensory system for pharmacological applications. , 2003, Biosensors & bioelectronics.

[17]  Jaime de la Rocha,et al.  Supplementary Information for the article ‘ Correlation between neural spike trains increases with firing rate ’ , 2007 .

[18]  Jean-Philippe Thivierge,et al.  Extracting functionally feedforward networks from a population of spiking neurons , 2012, Front. Comput. Neurosci..

[19]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[20]  Michael I. Ham,et al.  Functional structure of cortical neuronal networks grown in vitro. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  G. Buzsáki,et al.  Interneuron Diversity series: Circuit complexity and axon wiring economy of cortical interneurons , 2004, Trends in Neurosciences.

[22]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[23]  T. Sick,et al.  Anoxic preconditioning in hippocampal slices: role of adenosine , 1996, Neuroscience.

[24]  Sen Song,et al.  Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits , 2005, PLoS biology.

[25]  H. Berendse,et al.  The application of graph theoretical analysis to complex networks in the brain , 2007, Clinical Neurophysiology.

[26]  K. Gurney,et al.  Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence , 2008, PloS one.

[27]  R. Plackett,et al.  Karl Pearson and the Chi-squared Test , 1983 .

[28]  K. Kapinya,et al.  Ischemic tolerance in the brain. , 2005, Acta physiologica Hungarica.

[29]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[30]  Peter Dayan,et al.  The Effect of Correlated Variability on the Accuracy of a Population Code , 1999, Neural Computation.

[31]  M. Corner,et al.  Dynamics and plasticity in developing neuronal networks in vitro. , 2005, Progress in brain research.

[32]  Abbes Amira,et al.  3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches , 2010, Adv. Artif. Intell..