This use of the μECoG method is an innovative and potentially important approach, which raises a number of implications as well as underscoring important open questions. Methodologically, the paper showcases the strengths of μECoG in providing a wide-range

view of functional organization in a large cortical network including core auditory cortices, A1 and the rostral area (R), as well as more anterior regions. As pointed out by the authors, the view they provide is on a scale comparable to that provided by previous fMRI studies in both human and nonhuman primates. Critically, μECoG yields this view with high-temporal resolution, http://www.selleckchem.com/products/nutlin-3a.html utilizing amplitude fluctuations in a specific range of the neuronal activity spectrum, high gamma (60–250 Hz). The amplitude of neuronal activity in the high-gamma frequency range provides a relatively uncomplicated index of massed firing click here in neuronal ensembles underlying the electrodes, as well as a relatively direct linkage to studies using this exact measurement for studying brain activity in

surgical epilepsy patients (Canolty and Knight, 2010). Fukushima et al. (2012) were able to use μECoG to detail a relationship of spontaneous activity to functional architecture. Specifically, they verified that the high-gamma fraction of the stimulus-evoked response can be used to outline tonotopic maps within core and more rostral areas and the mirror-symmetric reversals at area boundaries as demonstrated by a host of earlier studies. They then cross-registered tonotopic maps with maps derived from spontaneous activity Org 27569 using the same high-gamma measure. This is a large step forward, as it begins to bridge the gap between a reasonably well-evolved understanding of how auditory cortex responds to stimulus input with the deeper issues surrounding ongoing activity and all the neuronal activities that compete and/or collaborate in this period, as discussed above. The fact that the rules governing ongoing neuronal activity are—at least to some extent—determined by the structural and functional organization

of a given brain region highlights the need for a better understanding of the underlying neuronal circuitry. Fukushima et al. (2012) relate their findings to several current questions in systems neuroscience, two of which we highlight here. One key issue that they discuss is the impact of ongoing activity on stimulus processing; a variety of findings indicate that ongoing fluctuations of activity have a large impact on the parameters of stimulus-evoked responses, stimulus detection, and the efficiency of behavioral responding. To be clear, these “activity fluctuations” usually reflect synchronous, rhythmic excitability variations (oscillations) in interconnected ensembles of local neurons (Jensen et al., 2012 and Schroeder and Lakatos, 2009). We will elaborate on this theme below.