![]() These data can be obtained with invasive or non-invasive recording techniques, and in a context that involves an experimental manipulation or in a context that is task-free. The hypothesis that neuronal oscillations in general, and inter-areal synchronization of these oscillations in particular, are instrumental for normal brain function has resulted in widespread application of quantitative methods to evaluate neuronal synchrony in electrophysiological data. The brain could dynamically coordinate the flow of information by changing the strength, pattern, or the frequency with which different brain areas engage in oscillatory synchrony. These bursts may occur during oscillations and may further enhance the reliability of the information transmission ( Lisman, 1997) or contribute to the establishment of long-range synchronization ( Wang, 2010). The neural information transmitted from one region to another is reflected by the action potentials, where the action potentials themselves may be temporally organized in bursts. ![]() These oscillations likely reflect synchronized rhythmic excitability fluctuations of local neuronal ensembles ( Buzsáki and Wang, 2012), and may facilitate the flow of neural information between nodes in the network when the oscillations are synchronized between those nodes ( Womelsdorf et al., 2007). It has been argued that neuronal oscillations provide a mechanism underlying dynamic coordination in the brain ( Singer, 1999 Varela et al., 2001 Fries, 2005, 2015 Siegel et al., 2012). We discuss how these issues can be addressed using current methods.ĭifferent cognitive or perceptual tasks require a coordinated flow of information within networks of functionally specialized brain areas. These pitfalls will be illustrated by presenting a set of MATLAB-scripts, which can be executed by the reader to simulate each of these potential problems. ![]() Next, we highlight a number of interpretational caveats and common pitfalls that can arise when performing functional connectivity analysis, including the common reference problem, the signal to noise ratio problem, the volume conduction problem, the common input problem, and the sample size bias problem. First, we review metrics for functional connectivity, including coherence, phase synchronization, phase-slope index, and Granger causality, with the specific aim to provide an intuition for how these metrics work, as well as their quantitative definition. This tutorial will review and summarize current analysis methods used in the field of invasive and non-invasive electrophysiology to study the dynamic connections between neuronal populations. Rhythmic neuronal interactions can be quantified using multiple metrics, each with their own advantages and disadvantages. Oscillatory neuronal activity may provide a mechanism for dynamic network coordination. 3Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands.2Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands. ![]() 1Department of Brain and Cognitive Sciences, The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA. ![]()
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