Complex Propagation Patterns Characterize Human Cortical Activity during Slow-Wave Sleep

被引:27
|
作者
Hangya, Balazs [1 ]
Tihanyi, Benedek T. [1 ]
Entz, Laszlo [2 ,3 ]
Fabo, Daniel [2 ,3 ]
Eross, Lorand [3 ]
Wittner, Lucia [2 ,3 ]
Jakus, Rita [3 ]
Varga, Viktor [1 ]
Freund, Tamas F. [1 ]
Ulbert, Istvan [2 ,3 ,4 ]
机构
[1] Hungarian Acad Sci, Inst Expt Med, Dept Cellular & Network Neurobiol, Budapest, Hungary
[2] Hungarian Acad Sci, Inst Psychol, Budapest, Hungary
[3] Natl Inst Neurosci, Budapest, Hungary
[4] Peter Pazmany Catholic Univ, Fac Informat Technol, Budapest, Hungary
来源
JOURNAL OF NEUROSCIENCE | 2011年 / 31卷 / 24期
基金
美国国家卫生研究院;
关键词
NEOCORTICAL NEURONS; OSCILLATIONS; EEG; NETWORKS; RIPPLES; STATE; HIPPOCAMPUS; PLASTICITY; DYNAMICS; SPINDLES;
D O I
10.1523/JNEUROSCI.1498-11.2011
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Cortical electrical activity during nonrapid eye movement (non-REM) sleep is dominated by slow-wave activity (SWA). At larger spatial scales (similar to 2-30 cm), investigated by scalp EEG recordings, SWA has been shown to propagate globally over wide cortical regions as traveling waves, which has been proposed to serve as a temporal framework for neural plasticity. However, whether SWA dynamics at finer spatial scales also reflects the orderly propagation has not previously been investigated in humans. To reveal the local, finer spatial scale (similar to 1-6 cm) patterns of SWA propagation during non-REM sleep, electrocorticographic (ECoG) recordings were conducted from subdurally implanted electrode grids and a nonlinear correlation technique [mutual information (MI)] was implemented. MI analysis revealed spatial maps of correlations between cortical areas demonstrating SWA propagation directions, speed, and association strength. Highest correlations, indicating significant coupling, were detected during the initial positive-going deflection of slow waves. SWA propagated predominantly between adjacent cortical areas, albeit spatial noncontinuities were also frequently observed. MI analysis further uncovered significant convergence and divergence patterns. Areas receiving the most convergent activity were similar to those with high divergence rate, while reciprocal and circular propagation of SWA was also frequent. We hypothesize that SWA is characterized by distinct attributes depending on the spatial scale observed. At larger spatial scales, the orderly SWA propagation dominates; at the finer scale of the ECoG recordings, non-REM sleep is characterized by complex SWA propagation patterns.
引用
收藏
页码:8770 / 8779
页数:10
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