Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems

Temporal structure in neuronal activity during working memory in macaque parietal cortex

Many cortical structures have elevated firing rates during working memory, but it is not known how the activity is maintained. To investigate whether reverberating activity is important, we studied the temporal structure of local field potential (LFP) activity and spiking from area LIP in two awake macaques during a memory-saccade task. Using spectral analysis, we found spatially tuned elevated power in the gamma band (25-90 Hz) in LFP and spiking activity during the memory period. Spiking and LFP activity were also coherent in the gamma band but not at lower frequencies. Finally, we decoded LFP activity on a single-trial basis and found that LFP activity in parietal cortex discriminated between preferred and anti-preferred direction with approximately the same accuracy as the spike rate and predicted the time of a planned movement with better accuracy than the spike rate. This finding could accelerate the development of a cortical neural prosthesis