title: Quantification of pairwise neuronal interactions: going beyond the significance lines.
abstract: BACKGROUND
  Normal brain function depends on intact interactions between multiple neuronal ensembles. Interactions within and between local networks comprising multiple neuronal types may occur on a range of time scales thus affecting the estimation of interaction strength. A common technique to investigate functional interactions within neuronal ensembles is pairwise cross-correlation analysis. However, conventional cross-correlation methods address the question of whether an observed peak in the cross-correlation is statistically significant relative to the null hypothesis which assumes a lack of correlation. Ultimately, these methods were not designed to evaluate the strength of the observed interactions.
  
  
  METHODS
  We devised four complementary measures - Triplets, Bin crossing, Bin height and Entropy - for assessing the strength of neuronal interactions; each is sensitive to different features of the cross-correlogram peak such as height, width and smoothness.
  
  
  RESULTS
  First, a comparison of five prevalent methods for evaluating whether an observed peak in neuronal cross-correlogram is significant allowed their ranking from the most conservative to the more sensitive for purposes of selecting the appropriate method based on the data structure and preferred strategy. Second, the performance of the four measures we derived improved with interaction strength and the number of spikes in the cross-correlogram. The four measures also enabled the reconstruction of interaction parameters of simulated networks including the detection of time-dependent alterations.
  
  
  CONCLUSIONS
  We suggest that the combination of several measures of peak characteristics helps rectify the individual shortcomings of specific measures and can yield a broad coverage of interaction strengths and widths.
authors.affiliation: The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 52900, Israel.
authors.forename: Evi
authors.initials: E
authors.lastname: Kopelowitz
authors.affiliation: The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 52900, Israel.
authors.forename: Iddo
authors.initials: I
authors.lastname: Lev
authors.affiliation: The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 52900, Israel. Electronic address: danacoh@gmail.com.
authors.forename: Dana
authors.initials: D
authors.lastname: Cohen
mesh_terms: D000200:Action Potentials; D000818:Animals; D001921:Brain / Q000502:physiology; D002450:Cell Communication; D003198:Computer Simulation; D019277:Entropy; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D016012:Poisson Distribution; D025341:Principal Component Analysis; D013997:Time Factors
keywords: Confidence limits; Correlation; Cross-correlogram; Normal distribution; Poisson distribution