e , differences (sample-by-sample in the time domain) between LFP

e., differences (sample-by-sample in the time domain) between LFPs from immediately neighboring electrodes. We refer to the bipolar derivatives as “sites.” Bipolar derivation further enhances spatial specificity of the signal and FRAX597 in vivo removes the common recording reference, which is important when analyzing synchronization between sites. Subsequently, per site and individual epoch, the mean was subtracted, and then, per site and session, the signal was normalized by its standard deviation. These normalized signals were pooled across sessions with identical stimulus and task, unless indicated otherwise. Spectral power, coherence, and GC influences were estimated by applying a fast Fourier

transform (FFT) after multitapering (Mitra and Pesaran, 1999) with seven tapers. selleck chemical Given epoch lengths of 0.5 s, this resulted in a spectral smoothing of ±7 Hz. The resulting spectra are shown from 8 Hz to 140 Hz. We performed a separate analysis of the lower frequencies (4 Hz to 28 Hz), in which the same 0.5 s data epochs were Hanning tapered. This did not reveal any consistent attentional effect. For the analysis of GC influences, we applied nonparametric spectral matrix factorization to the cross-spectral density (Dhamala et al., 2008). We performed this factorization separately for each pair of sites. GC influence spectra were first estimated with the same spectral concentration parameters as all spectra

and then smoothed with a two-frequency-bin boxcar window. If in a site pair one site has a higher SNR, then the analysis of GC influences has a bias

toward estimating a stronger influence from the high-SNR site to the low-SNR site (Nalatore et al., 2007). To control for this, we stratified for SNR. We defined SNR as the absolute power of the bipolar-derived, -demeaned, and SD-normalized signal in the frequency band for which the stratification was intended. There were two types of comparisons related to the Granger analysis and two corresponding types of stratification. (1) We compared bottom-up with top-down GC influences. In this case, we stratified SNR per site pair across the two areas. (2) We compared GC influences in a given direction between two attention conditions. In this case, we stratified SNR per site pair across the two attention conditions. In both cases, per site pair, trials were Thymidine kinase discarded until the mean SNR was essentially identical (and the SNR distribution across trials was as similar as possible) across sites (case 1) or across attention conditions (case 2). If for a given site pair this left fewer than 100 trials, the site pair was discarded from the stratified analysis. Statistical testing included two steps: we first tested across all frequencies for significances at a p < 0.05 level, while correcting for multiple comparisons across frequencies. We found significant differences in bands that are indicated as gray bars in the spectra and that fell almost entirely into the frequency band of 60–80 Hz.

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