The number Roxadustat nmr of positive pixels and positive clusters (groups of adjacent positive pixels) within the outline was counted using ImageJ. To normalize

for variation in size of neurons, we divided the numbers of pixels and clusters by the outline perimeter. Data are presented as means ± SEM and were analyzed using ANOVAs repeated-measures and two-tailed t test (unpaired or paired) for normally distributed variables to evaluate statistical significance with p < 0.05 as level of statistical significance. See Table S2 for the average number of analyzed cells per mouse for each perisomatic marker and Table S3 for detailed statistical results. We thank K. Kan and M. Parakala for technical assistance and M. Mayford for providing

TetTag mice. We thank J. Aggleton, J. Ainsley, L. Drane, L. Feig, M. Jacob, K. GDC-0199 research buy Mackie, E. Perisse, and S. Waddell for critical reading of the manuscript. This work was supported by an NIH Director’s New Innovator Award (L.G.R.; DP2 OD006446), a Fyssen Foundation Postdoctoral Fellowship, a Bettencourt-Schueller award, and a Philippe Foundation Award (S.T.), a Sackler Dean’s Graduate Fellowship (J.S.), the Synapse Neurobiology Training Program (J.S.; T32 NS061764; PI: K. Dunlap and M. Jacob), the Tufts Center for Neuroscience Research (P30 NS047243; PI: R. Jackson), and DA011322 (PI: K. Mackie). “
“Neurons communicate with each other in dynamically modulated circuits. Functional connectivity, a measure of interactions between neurons in these circuits, can change gradually during learning (McIntosh and Gonzalez-Lima, 1998) and formation of long-term memories, or it can change rapidly, depending on behavioral context and cognitive demands. While the mechanisms underlying long-term network plasticity have been extensively documented, those underlying rapid modulation of functional connectivity remain largely unknown. At the network level, functional connectivity is affected by up-down and oscillatory states of the neural network (Gray et al., 1989). Cortical inhibition plays a key role in this process

(Cardin et al., 2009, Sohal et al., 2009 and Womelsdorf et al., 2007). Parvalbumin-positive (PV+) interneurons, which make up more than half of the inhibitory neurons in the click here cortex (Celio, 1986), are particularly important as they provide strong feedforward and feedback inhibition that can synchronize the cortical network (Cardin et al., 2009, Fuchs et al., 2007, Isaacson and Scanziani, 2011 and Sohal et al., 2009). Their precise influence on cortical networks during sensory processing, however, remains unclear. In particular, it is unknown how PV+ neurons may differentially modulate responses in different layers of the neocortex and how the anatomical organization of the cortex affects the flow of information through these networks.