By contrast, slow wave-triggered averaging of spiking activity in mPFC and hippocampus once again revealed that slow

waves in prefrontal cortex preceded those found in hippocampus (Figure S7F). On the whole, our results suggest that signal propagation in slow wave sleep primarily follows a cortical-hippocampal direction. What determines whether and when a given region transitions into an active state? We hypothesized that this process is not entirely stochastic and is determined by what proportion of its afferents have just transitioned to an active state. To test this possibility, we focused on the amygdala and its afferents. This choice was guided by the fact that projections to the amygdala in primates are mostly ipsilateral and arrive from diverse sources, with a notable contribution from other limbic structures such as entorhinal cortex, cingulate cortex, and hippocampus, as well as medial prefrontal www.selleckchem.com/products/Bosutinib.html this website and orbitofrontal cortices serving mainly as input sources (Amaral et al., 1992 and McDonald,

1998). We capitalized on this anatomical organization and examined whether we could predict the occurrence and timing of individual slow waves in the amygdala. Crucially, if transitions into ON periods indeed reflect cumulative drive of anatomical afferents, we expected that we could better predict the occurrence of events on the basis of ipsilateral limbic afferents than on the basis of equivalent contralateral information. To examine this possibility, we inspected data in 17 hemispheres whatever of nine individuals in which signals from amygdala and several other limbic structures were recorded. A linear classifier was trained with a subset of slow waves to utilize information about the occurrence, amplitude, and timing of transitions into population ON periods (positive peaks in depth EEG) in ipsilateral (or contralateral) limbic regions to predict the occurrence and timing of individual slow waves in the amygdala (Experimental Procedures). Its performance was then tested with a separate subset of waves (Figure 8). In all nine individuals, the information from ipsilateral afferent regions led to significantly greater accuracy

in predicting the occurrence of slow waves in the amygdala (p < 1 × 10−39 for all nine individuals). Moreover, ipsilateral prediction accuracy monotonically increased as a function of the number of afferent regions that were made available for classifier training (Figure 8A, red; slope = 0.04% ± 0.005% correct per neighbor). By contrast, contralateral prediction was sometimes at near-chance levels and did not depend as strongly on the number of regions (Figure 8A, blue; significantly smaller slopes; p < 5.4 × 10−4 via paired t test). Along the same line, the timing of individual slow waves in the amygdala (whether they occurred before or after the parietal scalp electrode) could be more accurately predicted with information from ipsilateral afferent regions (p < 1.