Generation of theta rhythm in medial entorhinal cortex of freely moving rats

A regular slow wave theta rhythm can be recorded in the medial entorhinal cortex (MEC) of freely moving rats during voluntary behaviors and paradoxical sleep. Electrode penetrations normal to the cortical layers proceeding from the deeper to the more superficial layers reveal a continuous theta rhythm in layers IV-III (deep MEC theta rhythm) with an amplitude maximum in layer III, a null between the outer one-third of layer III and the inner one-half of layer I, and a continuous phase-reversed theta rhythm in layers II-I (superficial MEC theta rhythm) with an amplitude maximum there. Deep MEC theta rhythm is similar in phase and wave shape to CA1 theta rhythm; superficial MEC theta rhythm is similar in phase to DG theta rhythm. Laminar profiles throughout MEC show that the theta rhythm is generated there; it is not volume conducted from hippocampus.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/23244/1/0000177.pd

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