No staining corresponding to specific labeling was observed when primary antisera were omitted. In multiple labeling experiments using primary antibodies from different species, the lack of cross-reactivity of the secondary antibodies was consistently checked.

Images were obtained with a Zeiss AxioImager Z2 microscope coupled to a camera (Zeiss AxioCam MR3). Immunofluorescence images were acquired using a halogene HBO lamp associated with (470/40, 525/50), (545/25, 605/70) filter cubes for detection of Al488, Cy3 or DL549, Cy5, or DL649. Counts were performed manually. All selleck chemical results are given as percentages of total number of cells (Figure 1C) or as means of percentages ± SEM (Figures 3L and 3M), n being the number of mice per group. Forty-three neurons were reconstructed with a computer assisted system attached to a microscope (Neurolucida, MicroBrightfield). Out of those, 31 were included in the morphometric analysis. Morphological variables included: dendritic and axonal lengths, dendritic and axonal surfaces, and number of dendritic and axonal terminals. We also performed a Sholl analysis in order to determine the distribution of the number of axonal intersections with circles of increasing radius (20 μm steps) centered at the cell’s soma. Cluster analysis

for morphological data was performed using Statistica software. Our analysis was performed with Euclidean distances using Ward’s method. According to Ward’s method, cases are assigned to clusters so that the variance (sum of squared deviations from the mean) within each cluster is minimized. AZD6244 We used custom designed MATLAB software (Bonifazi et al., 2009) that allowed: (1) automatic identification of loaded cells; (2) measuring the average fluorescence transients from each cell as a function of time; (3) detecting the onsets and offsets of calcium signals; and (4) reconstructing the functional connectivity of the imaged network. Network synchronizations (GDPs) were detected as synchronous onsets peaks including more neurons

than expected by chance, Selleckchem CHIR 99021 as previously described (Bonifazi et al., 2009). In order to identify cells in the network responding to phasic stimulations, for each cell we first calculated the average fluorescence change across trials in a time window between −1 and +1 s centered on the time of the stimulus. Cross-correlation between the average calcium signal of the cell and the calcium signal of the stimulated cell was calculated at time lags varying between −1 and +1 s. If the maximum of the cross-correlation exceeded 0.5 and occurred at positive times, indicating that the activation of the cell followed the stimulation, the cell was considered as responding to the stimulation. In order to color-code the effective connectivity map, we built a matrix from the calcium image of the slice and we assigned to each cell its maximal cross-correlation value. The image was then convolved with a Gaussian of unitary amplitude and 8 μm radius.