Skip to main content

Part of the book series: Springer Theses ((Springer Theses))

  • 156 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. Bonifazi P et al (2009) GABAergic hub neurons orchestrate synchrony in developing hippocampal networks. Science 326:1419–1424

    Article  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. Kreitzer AC (2009) Physiology and pharmacology of striatal neurons. Annu Rev Neurosci 32:127–147. https://doi.org/10.1146/annurev.neuro.051508.135422

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  MathSciNet  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. 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

  17. 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

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. 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

    Article  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. 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

    MathSciNet  Google Scholar 

  24. Bedenbaugh P, Gerstein GL (1997) Multiunit normalized cross correlation differs from the average single-unit normalized correlation. Neural Comput 9:1265–1275

    Article  Google Scholar 

  25. Salinas E, Sejnowski TJ (2001) Correlated neuronal activity and the flow of neural information. Nat Rev Neurosci 2:539–550

    Article  Google Scholar 

  26. 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

    Article  Google Scholar 

  27. Melssen WJ, Epping WJ (1987) Detection and estimation of neural connectivity based on crosscorrelation analysis. Biol Cybern 57:403–414

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. 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

    Google Scholar 

  30. 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

  31. 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

    Article  Google Scholar 

  32. Salinas E, Sejnowski TJ (2000) Impact of correlated synaptic input on output firing rate and variability in simple neuronal models. J Neurosci 20:6193

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. Eichler M, Dahlhaus R, Sandkuhler J (2003) Partial correlation analysis for the identification of synaptic connections. Biol Cybern 89:289–302

    Article  Google Scholar 

  35. Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103:8577–8582

    Article  Google Scholar 

  36. 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

    Article  Google Scholar 

  37. 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

    Article  Google Scholar 

  38. 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

    Article  Google Scholar 

  39. 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

    Article  Google Scholar 

  40. 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

    Article  Google Scholar 

  41. Viswam V, Dragas J, Muller J, Hierlemann A In: IEEE international solid-state circuits conference. IEEE, pp 394–396

    Google Scholar 

  42. 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

    Article  Google Scholar 

  43. 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

    Article  Google Scholar 

  44. HDFGroup (2013) Hierarchical data format

    Google Scholar 

  45. Bennett K, Robertson J (2011) In: Ionescu CM (ed) MATLAB—a ubiquitous tool for the practical engineer

    Google Scholar 

  46. 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

    Article  Google Scholar 

  47. Vato A et al (2004) Spike manager: a new tool for spontaneous and evoked neuronal networks activity characterization. Neurocomputing 58:1153–1161

    Article  Google Scholar 

  48. Maccione A et al (2009) A novel algorithm for precise identification of spikes in extracellularly recorded neuronal signals. J Neurosci Methods 177:241–249

    Article  Google Scholar 

  49. Tam DC (2002) An alternate burst analysis for detecting intra-burst firings based on inter-burst periods. Neurocomputing 44–46:1155–1159

    Article  Google Scholar 

  50. 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

    Article  Google Scholar 

  51. 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

    Article  Google Scholar 

  52. 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

    Article  Google Scholar 

  53. 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

    Article  Google Scholar 

  54. 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

    Article  Google Scholar 

  55. 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

    Article  Google Scholar 

  56. Grace AA, Bunney BS (1984) The control of firing pattern in nigral dopamine neurons: burst firing. J Neurosci 4:2877

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints 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

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics