title: Detecting neuromagnetic synchrony in the presence of noise.
abstract: BACKGROUND
  Synchrony between neuroelectric oscillations in distant brain areas is currently used as an indicator of functional connectivity between the involved neural substrates. Coherence measures, which quantify synchrony, are affected by concurrent brain activities, commonly subsumed as noise.
  
  
  METHODS
  Using Monte-Carlo simulation, we analysed the properties of circular statistics and how those are affected by noise. We considered three different models of neuroelectric signal generation, which are an additive model, phase-reset, and reciprocal phase-interaction. Using the receiver-operating characteristic method, we compared the performances of currently implemented algorithms for coherence detection such as phase-coherence or phase-locking factor, magnitude-squared coherence, and phase-lagging index, all based on circular statistics, and a more general approach to synchrony, using measures of mutual information. We compared inter-trial coherence as a method for signal detection with coherence between multiple sources as measure of source interaction and connectivity.
  
  
  RESULTS
  Charts of performance characteristics showed that the choice of methods depend on the underlying signal generation model. Detection of coherence requires in general a higher signal-to-noise ratio than detection of the signal itself, and again, the difference in performance depends strongly on the underlying model of signal generation.
  
  
  METHODS
  Previous comparisons of the performances of different algorithms for signal detection and coherence have not considered systematically the underlying neural generation mechanisms.
  
  
  CONCLUSIONS
  Detection of coherence generated by additive signals or a phase-reset requires largely higher signal-to-noise ratio compared to signal detection. Only in case of true phase interaction, signal detection and coherence measures are similarly sensitive.
authors.affiliation: Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada M6A 2E1; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada M5G 2M9. Electronic address: ewianda@research.baycrest.org.
authors.forename: Elvis
authors.initials: E
authors.lastname: Wianda
authors.affiliation: Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada M6A 2E1; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada M5G 2M9. Electronic address: bross@research.baycrest.org.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Ross
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001303:Auditory Cortex / Q000502:physiology*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D015225:Magnetoencephalography; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009010:Monte Carlo Method; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D055815:Young Adult
keywords: Circular statistics; Coherence; Electroencephalography; Inter-trial coherence; Magnetoencephalography; Monte-Carlo simulation; Phase-coherence; Phase-locking value; Phase-reset; Receiver-operating characteristics; Synchrony