Investigating the impact of electrical stimulation temporal distribution on cortical network responses

BackgroundThe brain is continuously targeted by a wealth of stimuli with complex spatio-temporal patterns and has presumably evolved in order to cope with those inputs in an optimal way. Previous studies investigating the response capabilities of either single neurons or intact sensory systems to external stimulation demonstrated that stimuli temporal distribution is an important, if often overlooked, parameter.ResultsIn this study we investigated how cortical networks plated over micro-electrode arrays respond to different stimulation sequences in which inter-pulse intervals followed a 1/fβ distribution, for different values of β ranging from 0 to ∞. Cross-correlation analysis revealed that network activity preferentially synchronizes with external input sequences featuring β closer to 1 and, in any case, never for regular (i.e. fixed-frequency) stimulation sequences. We then tested the interplay between different average stimulation frequencies (based on the intrinsic firing/bursting frequency of the network) for two selected values of β, i.e. 1 (scale free) and ∞ (regular). In general, we observed no preference for stimulation frequencies matching the endogenous rhythms of the network. Moreover, we found that in case of regular stimulation the capability of the network to follow the stimulation sequence was negatively correlated to the absolute stimulation frequency, whereas using scale-free stimulation cross-correlation between input and output sequences was independent from average input frequency.ConclusionsOur results point out that the preference for a scale-free distribution of the stimuli is observed also at network level and should be taken into account in designing more efficient protocols for neuromodulation purposes.

[1]  Luca Berdondini,et al.  Emergence of Bursting Activity in Connected Neuronal Sub-Populations , 2014, PloS one.

[2]  D. Botteldooren,et al.  1/f Noise in Rural and Urban Soundscapes , 2003 .

[3]  Steve M. Potter,et al.  Effective parameters for stimulation of dissociated cultures using multi-electrode arrays , 2004, Journal of Neuroscience Methods.

[4]  Timothy J Shafer,et al.  Multi-well microelectrode array recordings detect neuroactivity of ToxCast compounds. , 2014, Neurotoxicology.

[5]  P. S. Wolters,et al.  Longterm stability and developmental changes in spontaneous network burst firing patterns in dissociated rat cerebral cortex cell cultures on multielectrode arrays , 2004, Neuroscience Letters.

[6]  Luca Berdondini,et al.  Network Dynamics and Synchronous Activity in cultured Cortical Neurons , 2007, Int. J. Neural Syst..

[7]  Jan W. H. Schnupp,et al.  Emergence of Tuning to Natural Stimulus Statistics along the Central Auditory Pathway , 2011, PloS one.

[8]  Alessandro Vato,et al.  Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons , 2005, Neurocomputing.

[9]  Shimon Marom,et al.  Development, learning and memory in large random networks of cortical neurons: lessons beyond anatomy , 2002, Quarterly Reviews of Biophysics.

[10]  Sergio Martinoia,et al.  A self-adapting approach for the detection of bursts and network bursts in neuronal cultures , 2010, Journal of Computational Neuroscience.

[11]  Steve M. Potter,et al.  Controlling Bursting in Cortical Cultures with Closed-Loop Multi-Electrode Stimulation , 2005, The Journal of Neuroscience.

[12]  Michela Chiappalone,et al.  Cortical cultures coupled to micro-electrode arrays: a novel approach to perform in vitro excitotoxicity testing. , 2012, Neurotoxicology and teratology.

[13]  H. L. Bryant,et al.  Spike initiation by transmembrane current: a white‐noise analysis. , 1976, The Journal of physiology.

[14]  Steve M. Potter,et al.  An extremely rich repertoire of bursting patterns during the development of cortical cultures , 2006, BMC Neuroscience.

[15]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[16]  Andrea Hasenstaub,et al.  State Changes Rapidly Modulate Cortical Neuronal Responsiveness , 2007, The Journal of Neuroscience.

[17]  L. Pinneo.  On noise in the nervous system. , 1966, Psychological review.

[18]  A. Aertsen,et al.  A comparison of the Spectro-Temporal sensitivity of auditory neurons to tonal and natural stimuli , 1981, Biological Cybernetics.

[19]  Y. Ben-Ari.  Developing networks play a similar melody , 2001, Trends in Neurosciences.

[20]  L. L. Bologna,et al.  Low-frequency stimulation enhances burst activity in cortical cultures during development , 2010, Neuroscience.

[21]  David C. Tam.  An alternate burst analysis for detecting intra-burst firings based on inter-burst periods , 2002, Neurocomputing.

[22]  H. Markram,et al.  Interneurons of the neocortical inhibitory system , 2004, Nature Reviews Neuroscience.

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

[24]  Kikuro Fukushima,et al.  Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex , 2009, PLoS Comput. Biol..

[25]  Asaf Gal,et al.  Entrainment of the Intrinsic Dynamics of Single Isolated Neurons by Natural-Like Input , 2013, The Journal of Neuroscience.

[26]  P. Massobrio,et al.  Network plasticity in cortical assemblies , 2008, The European journal of neuroscience.

[27]  Gustavo Deco,et al.  Network Bursting Dynamics in Excitatory Cortical Neuron Cultures Results from the Combination of Different Adaptive Mechanism , 2013, PloS one.

[28]  Yuguo Yu,et al.  Preference of sensory neural coding for 1/f signals. , 2005, Physical review letters.

[29]  T. Sejnowski,et al.  Reliability of spike timing in neocortical neurons. , 1995, Science.

[30]  Idan Segev,et al.  Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing , 1998, Neural Computation.

[31]  H. Robinson,et al.  Spatio-temporal cholinergic modulation in cultured networks of rat cortical neurons: Spontaneous activity , 2005, Neuroscience.

[32]  U. Egert,et al.  Quantitative examination of stimulus-response relations in cortical networks in vitro. , 2013, Journal of neurophysiology.

[33]  Robert E Kass,et al.  Statistical issues in the analysis of neuronal data. , 2005, Journal of neurophysiology.

[34]  J. Schnupp,et al.  Tuning to Natural Stimulus Dynamics in Primary Auditory Cortex , 2006, Current Biology.

[35]  Salomon Z. Muller,et al.  Temporally-patterned deep brain stimulation in a mouse model of multiple traumatic brain injury , 2014, Behavioural Brain Research.

[36]  K. Harris,et al.  A Simple Model of Cortical Dynamics Explains Variability and State Dependence of Sensory Responses in Urethane-Anesthetized Auditory Cortex , 2009, The Journal of Neuroscience.

[37]  Eero P. Simoncelli.  Vision and the statistics of the visual environment , 2003, Current Opinion in Neurobiology.

[38]  L. Abbott,et al.  Responses of neurons in primary and inferior temporal visual cortices to natural scenes , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[39]  André Longtin,et al.  Noise in genetic and neural networks. , 2006, Chaos.

[40]  Shimon Marom,et al.  Neural timescales or lack thereof , 2010, Progress in Neurobiology.

[41]  H. Robinson,et al.  The mechanisms of generation and propagation of synchronized bursting in developing networks of cortical neurons , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[42]  Alessandro Vato,et al.  Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development , 2006, Brain Research.

[43]  Steve M. Potter,et al.  A new approach to neural cell culture for long-term studies , 2001, Journal of Neuroscience Methods.

[44]  J. Schiller,et al.  Dynamics of Excitability over Extended Timescales in Cultured Cortical Neurons , 2010, The Journal of Neuroscience.

[45]  Vincent Torre,et al.  Statistical properties of information processing in neuronal networks , 2005, The European journal of neuroscience.

[46]  G D Lewen,et al.  Reproducibility and Variability in Neural Spike Trains , 1997, Science.

[47]  Michela Chiappalone,et al.  Effects of antiepileptic drugs on hippocampal neurons coupled to micro-electrode arrays , 2013, Front. Neuroeng..

[48]  Ellese Cotterill,et al.  A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks , 2016, Journal of neurophysiology.

[49]  R. Voss,et al.  ‘1/fnoise’ in music and speech , 1975, Nature.

[50]  Paolo Massobrio,et al.  A novel algorithm for precise identification of spikes in extracellularly recorded neuronal signals , 2009, Journal of Neuroscience Methods.

[51]  Sergio Martinoia,et al.  Investigating neuronal activity by SPYCODE multi-channel data analyzer , 2010, Neural Networks.

[52]  Sergio Martinoia,et al.  Interaction of electrically evoked responses in networks of dissociated cortical neurons. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[53]  M Giugliano,et al.  Single-neuron discharge properties and network activity in dissociated cultures of neocortex. , 2004, Journal of neurophysiology.

[54]  G. Gross,et al.  A new fixed-array multi-microelectrode system designed for long-term monitoring of extracellular single unit neuronal activity in vitro , 1977, Neuroscience Letters.