Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics.

BACKGROUND: Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain. METHOD: We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors. RESULTS: The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted. COMPARISON WITH EXISTING METHODS: Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales. CONCLUSIONS: This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.

Overnight verbal memory retention correlates with the number of sleep spindles.

Despite strong evidence supporting a role for sleep in the consolidation of newly acquired declarative memories, the contribution of specific sleep stages remains controversial. Based on electrophysiological studies in animals, synchronous sleep oscillations have been long proposed as possible origins of sleep-related memory improvement. Nevertheless, no studies to date have directly investigated the impact of sleep oscillations on overnight memory retention in humans. In the present study we provide evidence that overnight verbal memory retention is highly correlated with the number of sleep spindles detected by an automatic algorithm over left frontocentral areas. At the same time, overnight retention of newly learned faces was found to be independent of spindle activity but correlated with non-rapid-eye-movement sleep time. The data strongly support theories suggesting a link between sleep spindle activity and verbal memory consolidation.