Fifty-seven consecutive patients with myoclonus from various causes were studied by electrophysiological techniques. Giant somatosensory evoked potentials (SEPs) were observed almost exclusively in patients with progressive myoclonic epilepsy (PME) and diseases with similar clinical features that included lipidosis, neuronal ceroid lipofuscinosis and posthypoxic myoclonus. On the basis of combinations of the giant SEP and the myoclonus-related cortical spike demonstrated by jerk-locked averaging, myoclonus in these patients was classified into four types. In patients with 'cortical reflex' myoclonus (type I) who showed both the giant SEP and the myoclonus-related cortical spike, these two cortical activities were similar in terms of wave form, scalp topography, time relationship to either the long latency (C) reflex or myoclonus, the following cortical excitability, the effect of antimyoclonus drugs and alterations during slow wave sleep. It is therefore postulated that the giant SEP is generated, at least in part, by common physiological mechanisms to the myoclonus-related cortical spike, or that the latter may comprise a constituent of the former. In most patients with PME or allied diseases, both afferent and efferent components of the SEP are enhanced, but in some patients, only one of the two components seems to be predominantly enhanced.

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It has been revealed that spontaneous coherent brain activity during rest, measured by functional magnetic resonance imaging (fMRI), self-organizes a "small-world" network by which the human brain could sustain higher communication efficiency across global brain regions with lower energy consumption. However, the state-dependent dynamics of the network, especially the dependency on the conscious state, remain poorly understood. In this study, we conducted simultaneous electroencephalographic recording with resting-state fMRI to explore whether functional network organization reflects differences in the conscious state between an awake state and stage 1 sleep. We then evaluated whole-brain functional network properties with fine spatial resolution (3781 regions of interest) using graph theoretical analysis. We found that the efficiency of the functional network evaluated by path length decreased not only at the global level, but also in several specific regions depending on the conscious state. Furthermore, almost two-thirds of nodes that showed a significant decrease in nodal efficiency during stage 1 sleep were categorized as the default-mode network. These results suggest that brain functional network organizations are dynamically optimized for a higher level of information integration in the fully conscious awake state, and that the default-mode network plays a pivotal role in information integration for maintaining conscious awareness.