Abstract
Sleep spindles are brief oscillatory events during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Here, using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the contrasting properties of the core and matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, which projects to middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. This property was sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Furthermore, consistent with human recordings, in the model, spindles occurred independently in the core system but the matrix system spindles commonly co-occurred with core spindles. We also found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of intra- and inter-cortical connectivity can potentially be a mechanism for increased spindle density as has been observed during learning.
Highlights
Sleep marks a profound change of brain state as manifested by the spontaneous emergence of characteristic oscillatory activities
Our study predicts that differences in thalamocortical connectivity, known from anatomical studies, are sufficient to explain the differences in the spindle properties between EEG and MEG which are observed in human recordings
Our model predicts that intracortical connectivity between cortical layers, a property influenced by sleep preceding learning, increases spindle density
Summary
Sleep marks a profound change of brain state as manifested by the spontaneous emergence of characteristic oscillatory activities. In in-vivo conditions, the cortex has been shown to be actively involved in the initiation and termination of spindles [3] as well as the long-range synchronization of spindles [4] [5]. Spindle density is known to increase following training in hippocampaldependent [6] as well as procedural memory [7] memory tasks. Spindle density correlates with better memory retention following sleep in verbal tasks [8, 9]. It was shown that pharmacologically increasing spindle density leads to better post-sleep performance in hippocampal-dependent learning tasks [10]. Spindle activity metrics, including amplitude and duration, were predictive of learning performance [11,12,13], suggesting that spindle event occurrence, amplitude, and duration influence memory consolidation
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