How Can I Analyze Connectivity in iEEG Data?

Understanding how brain regions influence one another is foundational to modern neuroscience, which places a heavy emphasis on how behavior emerges from networks of inter-regional activity. Extracting, analyzing, and interpreting these brain networks from intracranial EEG remains an ill-defined process, with little by way of standardized protocols or guidance on navigating a vast suite of analytic tools. This chapter begins by reviewing methods for computing iEEG-based connectivity, including common spectral approaches such as phase locking, phase consistency, and coherence—as well as popular adaptations to account for volume conduction and several alternative metrics. Statistical frameworks for analyzing these data are presented in a cognitive neuroscience context, with a focus on how variability in connectivity relates to human cognition. This chapter also discusses challenges in the interpretation of iEEG connectivity, particularly in light of current debates over the causal-versus-correlative nature of functional connectivity. As intracranial data is becoming increasingly widespread and accessible, the chapter closes with a note on the need to standardize analytic approaches to iEEG-based connectivity.