Oscillatory neuronal synchronization has been hypothesized to provide a mechanism for dynamic network coordination. Rhythmic neuronal interactions can be quantified using multiple metrics, each with their own advantages and disadvantages. This tutorial reviews current analysis methods used in the field of invasive and non-invasive electrophysiology to study the dynamic connections between neuronal populations. The tutorial first presents metrics for functional connectivity, including coherence, phase synchronization, phase-slope index, and Granger causality, to provide an intuition for how these metrics work, as well as their quantitative definition. A number of interpretational caveats and common pitfalls 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. These pitfalls are summarized and then illustrated through a series of MATLAB scripts, and ways to address these issues with current methods are then presented.
Taught by: Andre M. Bastos, MIT
Video:
- A tutorial review of functional connectivity analysis methods and their interpretational pitfalls (1:46:41)
Additional Resources:
- Bastos, A. M. & Schoffelen, J. (2016) A tutorial review of functional connectivity analysis methods and their interpretational pitfalls, Frontiers in Systems Neuroscience 9(175):1-23.
- FieldTrip MATLAB toolbox for MEG and EEG analysis
- MATLAB scripts to simulate pitfalls for functional connectivity analysis