[Outpatient long-term video EEG as new diagnostic approach in Germany: results of a feasibility study]. / Ambulantes Langzeit-Video-EEG als neuer diagnostischer Ansatz in Deutschland: Ergebnisse einer Machbarkeitsstudie.

In Germany, long-term video EEG as the gold standard for the diagnostics of epilepsy and other seizure disorders, is currently only available for inpatient monitoring in a limited number of specialized centers. These limited monitoring capacities and the large amount of associated time and work resources lead to a significant waiting time for this important diagnostic procedure nationwide. New portable sensor technology and automated data analysis methods are creating opportunities for gold standard long-term video EEG assessments in outpatient settings, which may help to resolve this barrier. Here, we report the results of a single-center feasibility study by implementing outpatient long-term video EEG (ALVEEG) as a diagnostic pathway in Germany. In the new diagnostic pathway, the use of innovative, portable video EEG monitoring systems along with artificial intelligence-assisted data analysis are intended to provide those patients affected by seizure disorders with a more rapid, efficient, and cross-sectoral access to gold standard diagnostics in the home environment. The diagnostics were well accepted by patients and clinicians and may represent a complementary option to inpatient monitoring to eliminate current bottlenecks in diagnostics and care.

Desynchronization Transitions in Adaptive Networks.

Adaptive networks change their connectivity with time, depending on their dynamical state. While synchronization in structurally static networks has been studied extensively, this problem is much more challenging for adaptive networks. In this Letter, we develop the master stability approach for a large class of adaptive networks. This approach allows for reducing the synchronization problem for adaptive networks to a low-dimensional system, by decoupling topological and dynamical properties. We show how the interplay between adaptivity and network structure gives rise to the formation of stability islands. Moreover, we report a desynchronization transition and the emergence of complex partial synchronization patterns induced by an increasing overall coupling strength. We illustrate our findings using adaptive networks of coupled phase oscillators and FitzHugh-Nagumo neurons with synaptic plasticity.