Event Abstract

The initial motivation, history and recent results for using MEG to Understand Sleep and its implication in specific health conditions

  • 1 AAI Scientific Cultural Services Ltd, Cyprus
  • 2 King Fahad Medical City, MEG Unit, Saudi Arabia
  • 3 Universidad Nacional Autónoma de México, Facultad de Psicología, Mexico
  • 4 University of Patras, Patras, Department of Physiology, Medical School,, Greece

Some 20 years ago, Michel Jouvet wrote in the concluding chapter of his book The paradox of sleep - the story of dreaming “… the majority of researchers are waiting with bated breath for the results of studies combining PET scanning, ‘functional’ magnetic resonance imaging (fMRI), magnetoencephalography and tomographic electroencephalography.” The developments in the last two decades have fully vindicated this statement [1-3]. We will summarize results from the analysis of the first whole night MEG sleep measurements performed around the turn of the millennium at the Brain Science Institute, RIKEN in Japan which have initiated the new wave of sleep neuroimaging. The analysis is described in two papers; they reveal a far richer picture than what even Jouvet would have anticipated [3,4]; the first paper focused on changes in regional brain activations and connectivity between areas related to eye movements [4]. The second described regional changes in spectral power in the quiet (core) states of each sleep stage [5]. The first paper demonstrated that even a “simple” eye movement in one direction cannot be understood by an orderly sequence of activations progressing from “command” brain areas to “slave” action-executing areas. It was difficult to decipher the basis of the organization when the analysis was confined to the simultaneously identified activations in different brain areas and their interactions. It was necessary to include the EOG traces which most people considered noise: the linked activity between each brain area and the eye muscle electrical activity (EOG) was quantified using time-delayed mutual information revealing the basis of the organization, around which the wider web of interactions between different brain areas also made sense: a cascade of events, governed by the natural brain rhythms (especially the delta rhythm) guided influences diverging from and converging onto each key brain area from many other areas, with a dense convergence reaching the oculomotor nuclei (OMN). None of the actual brain activations within the last 200 milliseconds before the eyes start to move, not even the ones in the OMN, relate to eye muscle activity that takes place before the eye moves and hence can be considered as the eye movement initiators; The earliest brain activity linked to EOG before saccade onset was 300 (from the OMN) and 400 (from the frontal lobes) milliseconds before saccade onset. The activity in all brain areas, including the cerebellum and brainstem, in the last 200 milliseconds before saccade onset was linked to EOG activity after the onset of the saccade [4]. The conclusion is that although saccades are rapid and ballistic in nature they are the planned for a long time and they are guided by anticipatory corrective muscle actions that are computed before, during and after the saccade onset! The work leading to the second paper focused on the search of an orderly change in some aspect of brain activity as awake state gives way to light sleep (NREM1 and NREM2), slow wave sleep (NREM3 and NREM 4) and finally REM. Each sleep stage is defined by the presence of characteristic patterns in the EEG that are either high amplitude and/or very rhythmic events; for example the hallmarks of NREM2 sleep stage are spindles (bursts of oscillatory activity in the 11 to 14 Hz range) and K complexes the highest amplitude EEG event encountered in a healthy EEG. Attempting to discern a continuity in the pattern of brain activation through the sleep stages by studying the tomographic estimates of activity at the time of the key graphoelements of each sleep produced no breakthrough, confirming the many reports before and many earlier reports (and even more recent ones) that concluded that regional activations corresponding to the EEG/MEG hallmark signatures of each stage have little consistency, appearing as random excursions of activity spread across wide brain areas. Progress was made when we focused on the quiet, or core periods that are in between periods when the hallmark events are evident. For nearly half a century, the heuristic rule of scoring a quiet period (without the hallmarks of a sleep stage) as sleep stage X, if it was preceded and followed by sleep stages X (marked so by the presence of the hallmarks of sleep stage X). The hunch was that there was a characteristic pattern of activity corresponding to sleep stage X even in the quiet, or “core”, periods. Indeed, highly consistent activity patterns were identified in these core periods. Critically, an almost monotonic change in activity was identified as sleep stages change from NREM1,2,3, and 4 to REM in a few areas. The most striking consistent changes were observed in the left medial prefrontal cortex and in the posterior midline parietal area; in these two areas the activity in the gamma band seem to increase steadily from light to slow wave sleep reaching its highest gamma spectral power during REM [5]. Demonstrating that regional brain activations during core periods of each sleep stage maintain a distinct identity turns sleep research methodology on its head. It suggests that rather than seeking to understand sleep on the basis of the hallmark events of each sleep stage, it may be more profitable to focus on the quiet periods between the large events. Recently we went a step further, attempting to understand the nature of the hallmark events of each sleep stage in terms of the fundamental properties of the brain as these are revealed through the patterns of brain activity of the core states and the changes in the few seconds before the hallmark events. Specifically, as part of the theoretical work of the project ARMOR [6], we explore the way light sleep progresses leading every evening to the highly rhythmic spindles and high amplitude K-complexes. We found common precursor activity to both events in the core periods that differentiates before spindles and K complexes suggesting distinct roles for each graphoelement: sentinel for K-complex [7] and memory consolidation for spindles. The methodology promises a better way of describing sleep with implications for both its normal function and pathology [8, 9].


The whole night MEG data were recorded at the lab. for Human Brain Dynamics (1998 - 2009) at BSI, RIKEN, Japan and analyzed at BSI and at the Lab. for Human Brain Dynamics (2008 - now) at AAI Scientific Cultural Services in Nicosia Cyprus (AAISCS). Partial support for recent work from grants SmokeFreeBrain under Horizon 2020 of the EU (grant No. 681120) and the project TopSleep of AAISCS, submitted to the Cyprus Ministry of Energy, Commerce Industry and Tourism (currently under evaluation).


[1] Nir, Y., Tononi, G. 2010 Dreaming and the brain: from phenomenology to neurophysiology. Trends in Cognitive Sciences 14(2): 88 – 100

[2] Duyn, J.H. 2012 EEG-fMRI Methods for the Study of Brain Networks during sleep. Front. Neurol. doi: 10.3389/fneur.2012.00100

[3] Dehghani, N., Cash, S.S., Chen, C.C., Hagler, D.J., Huangm, M., Dale, A.M., Halgren, E., (2010) Divergent Cortical Generators of MEG and EEG during Human Sleep Spindles Suggested by Distributed Source Modeling. PLoSone, 5(7): e11454. doi:10.1371/journal.pone.0011454

[4] Ioannides, A.A., Corsi-Cabrera, M., Fenwick, P.B.C., del Rio Portilla, Y., Laskaris, N.A., Khurshudyan, A., Theofilou, D., Shibata, T., Uchida, S., Nakabayashi, T., Kostopoulos, G.K., 2004. MEG tomography of human cortex and brainstem activity in waking and REM sleep saccades. Cereb. Cortex 14, 56–72.

[5] Ioannides, A.A., Kostopoulos, G.K., Liu, L., Fenwick, P.B.C., 2009. MEG identifies dorsal medial brain activations during sleep. Neuroimage 44, 455–468.

[6] Project ARMOR (Advanced multi-paRametric Monitoring and analysis for diagnosis and Optimal management of epilepsy and Related brain disorder), Program FP7/2007-2013, grant agreement number 287720

[7] Kostopoulos GKK, MEG and EEG polysomnography studies explore the role of K-Complexes and related EEG rhythms in maintaining a sound and safe sleep, this volume

[8] Project SmokeFreeBrain, Program Horizon 2020, grant agreement number 681120

[9] Project TopSleep, supported by AAISCS and submitted to the Cyprus Ministry of Energy, Commerce, Industry and Tourism.

Keywords: Sleep, MEG, Tomogrphic analysis of MEG data, mutual information, Eye movement in awake state and sleep

Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016.

Presentation Type: Oral Presentation in the Symposium Epilepsy and Sleep

Topic: Symposium Epilepsy and Sleep

Citation: Ioannides AA, Liu L, Poghosyan V, Fenwick PB, Corsi-Cabrera M, Del Río-Portilla Y and Kostopoulos GK (2016). The initial motivation, history and recent results for using MEG to Understand Sleep and its implication in specific health conditions. Front. Hum. Neurosci. Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00110

Received: 22 Jul 2016; Published Online: 01 Aug 2016.

* Correspondence: Prof. Andreas A Ioannides, AAI Scientific Cultural Services Ltd, Nicosia, 1065, Cyprus, a.ioannides@aaiscs.com

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