Abstract
In the first section of the Results, I will consider each of the connectivity methods that I developed and implemented during my Ph.D.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874
Ito S et al (2011) Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model. PLoS ONE 6:e27431. https://doi.org/10.1371/journal.pone.0027431
Poli D, Pastore VP, Martinoia S, Massobrio P (2016) From functional to structural connectivity using partial correlation in neuronal assemblies. J Neural Eng 13:026023
Pastore VP, Godjoski A, Martinoia S, Massobrio P (2017) SPICODYN: a toolbox for the analysis of neuronal network dynamics and connectivity from multi-site spike signal recordings. Neuroinformatics 16:15–30. https://doi.org/10.1007/s12021-017-9343-z
van den Heuvel MP, Sporns O (2013) Network hubs in the human brain. Trends Cogn Sci 17:683–696. https://doi.org/10.1016/j.tics.2013.09.012
Downes JH et al (2012) Emergence of a small-world functional network in cultured neurons. PLoS Comput Biol 8:e1002522. https://doi.org/10.1371/journal.pcbi.1002522
Langer N, Pedroni A, Jäncke L (2013) The problem of thresholding in small-world network analysis. PLoS ONE 8:e53199. https://doi.org/10.1371/journal.pone.0053199
Bonifazi P et al (2009) GABAergic hub neurons orchestrate synchrony in developing hippocampal networks. Science 326:1419–1424
Pastore VP, Massobrio P, Godjoski A, Martinoia S (2018) Excitatory-inhibitory links and network topology in large scale neuronal assemblies from multi-electrode recordings. PLoS Comput Biol 14(8):e1006381. https://doi.org/10.1371/journal.pcbi.100638
Kreitzer AC (2009) Physiology and pharmacology of striatal neurons. Annu Rev Neurosci 32:127–147. https://doi.org/10.1146/annurev.neuro.051508.135422
Kaufman AM et al (2012) Opposing roles of synaptic and extrasynaptic NMDA receptor signaling in cocultured striatal and cortical neurons. J Neurosci 32:3992–4003. https://doi.org/10.1523/jneurosci.4129-11.2012
Marom S, Shahaf G (2002) Development, learning and memory in large random networks of cortical neurons: lessons beyond anatomy. Q Rev Biophys 35:63–87
Schröter M, Paulsen O, Bullmore ET (2017) Micro-connectomics: probing the organization of neuronal networks at the cellular scale. Nat Rev Neurosci 18:131. https://doi.org/10.1038/nrn.2016.182
Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512
Poli D, Pastore VP, Massobrio P (2015) Functional connectivity in in vitro neuronal assemblies. Front Neural Circ 9:57. https://doi.org/10.3389/fncir.2015.00057
P Massobrio L Arcangelis de V Pasquale HJ Jensen D Plenz 2015 Criticality as a signature of healthy neural systems Front Syst Neurosci 9 22https://doi.org/10.3389/fnsys.2015.00022
Schroeter MS, Charlesworth P, Kitzbichler MG, Paulsen O, Bullmore ET (2015) Emergence of rich-club topology and coordinated dynamics in development of hippocampal functional networks in vitro. J Neurosci 35:5459–5470. https://doi.org/10.1523/JNEUROSCI.4259-14.2015
Maccione A et al (2010) Experimental investigation on spontaneously active hippocampal cultures recorded by means of high-density MEAs: analysis of the spatial resolution effects. Front Neuroeng 3:4. https://doi.org/10.3389/fneng.2010.00004
Gerstein GL, Perkel DH (1972) Mutual temporal relationships among neuronal spike trains. Biophys J 12:453–473. https://doi.org/10.1016/S0006-3495(72)86097-1
Rosenberg JR, Amjad AM, Breeze P, Brillinger DR, Halliday DM (1989) The Fourier approach to the identification of functional coupling between neuronal spike trains. Prog Biophys Mol Biol 53:1–31
Brosch M, Schreiner CE (1999) Correlations between neural discharges are related to receptive field properties in cat primary auditory cortex. Eur J Neurosci 11:3517–3530. https://doi.org/10.1046/j.1460-9568.1999.00770.x
Eytan D, Minerbi A, Ziv N, Marom S (2004) Dopamine-induced dispersion of correlations between action potentials in networks of cortical neurons. J Neurophysiol 92:1817–1824
Quian Quiroga R, Kreuz T, Grassberger P (2002) Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Phys Rev 66:041904
Bedenbaugh P, Gerstein GL (1997) Multiunit normalized cross correlation differs from the average single-unit normalized correlation. Neural Comput 9:1265–1275
Salinas E, Sejnowski TJ (2001) Correlated neuronal activity and the flow of neural information. Nat Rev Neurosci 2:539–550
Aertsen AMHJ, Gerstein GL (1985) Evaluation of neuronal connectivity: sensitivity of cross-correlation. Brain Res 340:341–354. https://doi.org/10.1016/0006-8993(85)90931-X
Melssen WJ, Epping WJ (1987) Detection and estimation of neural connectivity based on crosscorrelation analysis. Biol Cybern 57:403–414
Dunn B, Mørreaunet M, Roudi Y (2015) Correlations and functional connections in a population of grid cells. PLoS Comput Biol 11:e1004052. https://doi.org/10.1371/journal.pcbi.1004052
Aertsen A et al (1991) Neural interactions in the frontal cortex of a behaving monkey: signs of dependence on stimulus context and behavioral state. J Hirnforsch 32:735–743
Schneidman E, Berry MJ, Segev R, Bialek W (2006) Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440:1007–1012. http://www.nature.com/nature/journal/v440/n7087/suppinfo/nature04701_S1.html
Cutts CS, Eglen SJ (2014) Detecting pairwise correlations in spike trains: an objective comparison of methods and application to the study of retinal waves. J Neurosci 34:14288–14303. https://doi.org/10.1523/jneurosci.2767-14.2014
Salinas E, Sejnowski TJ (2000) Impact of correlated synaptic input on output firing rate and variability in simple neuronal models. J Neurosci 20:6193
Garofalo M, Nieus T, Massobrio P, Martinoia S (2009) Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks. PLoS ONE 4:e6482. https://doi.org/10.1371/journal.pone.0006482
Eichler M, Dahlhaus R, Sandkuhler J (2003) Partial correlation analysis for the identification of synaptic connections. Biol Cybern 89:289–302
Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103:8577–8582
Kanagasabapathi TT et al (2012) Functional connectivity and dynamics of cortical-thalamic networks co-cultured in a dual compartment device. J Neural Eng 9:036010. https://doi.org/10.1088/1741-2560/9/3/036010
Berdondini L et al (2009) Active pixel sensor array for high spatio-temporal resolution electrophysiological recordings from single cell to large scale neuronal networks. Lab Chip 9:2644–2651
Buchs P-A, Muller D (1996) Induction of long-term potentiation is associated with major ultrastructural changes of activated synapses. Proc Natl Acad Sci USA 93:8040–8045
Eversmann B et al (2003) A 128 x 128 CMOS biosensor array for extracellular recording of neural activity. IEEE J Solid-State Circ 38:2306–2317
Muller J et al (2015) High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels. Lab Chip 15:2767–2780. https://doi.org/10.1039/c5lc00133a
Viswam V, Dragas J, Muller J, Hierlemann A In: IEEE international solid-state circuits conference. IEEE, pp 394–396
Pastore VP, Poli D, Godjoski A, Martinoia S, Massobrio P (2016) ToolConnect: a functional connectivity toolbox for in vitro networks. Front Neuroinformatics 10:13. https://doi.org/10.3389/fninf.2016.00013
Overbey LA, Todd MD (2009) Dynamic system change detection using a modification of the transfer entropy. J Sound Vib 322:438–453. https://doi.org/10.1016/j.jsv.2008.11.025
HDFGroup (2013) Hierarchical data format
Bennett K, Robertson J (2011) In: Ionescu CM (ed) MATLAB—a ubiquitous tool for the practical engineer
Bologna LL et al (2010) Investigating neuronal activity by SPYCODE multi-channel data analyzer. Neural Netw 23:685–697. https://doi.org/10.1016/j.neunet.2010.05.002
Vato A et al (2004) Spike manager: a new tool for spontaneous and evoked neuronal networks activity characterization. Neurocomputing 58:1153–1161
Maccione A et al (2009) A novel algorithm for precise identification of spikes in extracellularly recorded neuronal signals. J Neurosci Methods 177:241–249
Tam DC (2002) An alternate burst analysis for detecting intra-burst firings based on inter-burst periods. Neurocomputing 44–46:1155–1159
Kapucu FE et al (2012) Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics. Front Comput Neurosci 6:38. https://doi.org/10.3389/fncom.2012.00038
Mazzoni A et al (2007) On the dynamics of the spontaneous activity in neuronal networks. PLoS ONE 2:e439. https://doi.org/10.1371/journal.pone.0000439
Pasquale V, Martinoia S, Chiappalone M (2009) A self-adapting approach for the detection of bursts and network bursts in neuronal cultures. J Comput Neurosci 29:213–229. https://doi.org/10.1007/s10827-009-0175-1
Chiappalone M et al (2005) Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons. Neurocomputing 65–66:653–662
Bal-Price AK et al (2010) In vitro developmental neurotoxicity (DNT) testing: relevant models and endpoints. NeuroToxicology 31:545–554. https://doi.org/10.1016/j.neuro.2009.11.006
Hogberg HT et al (2011) Application of micro-electrode arrays (MEAs) as an emerging technology for developmental neurotoxicity: evaluation of domoic acid-induced effects in primary cultures of rat cortical neurons. NeuroToxicology 32:158–168. https://doi.org/10.1016/j.neuro.2010.10.007
Grace AA, Bunney BS (1984) The control of firing pattern in nigral dopamine neurons: burst firing. J Neurosci 4:2877
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Pastore, V.P. (2021). Results. In: Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-030-59042-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-59042-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59041-3
Online ISBN: 978-3-030-59042-0
eBook Packages: EngineeringEngineering (R0)