Theoretical Framework \& Background Cortical spreading depressions (CSD) and seizures, are crucial in the development of delayed cerebral ischemia and poor functional outcome in patients suffering from acute brain injuries such as subarachnoid hemorrhage. Multimodal neuromonitoring (MMNM) provides the unique possibility in the sedated and mechanically ventilated patients to record these electrophysiological phenomena and relate them to measures of cerebral ischemia and malperfusion. MMNM combines invasive (e.g. electrocorticography, cerebral microdialysis, brain tissue oxygenation) and noninvasive (e.g. neuroimaging, continuous EEG) techniques. Additionally, cerebral microdialysis can measure the unbound extracellular drug concentrations of sedatives, which potentially inhibit CSD and seizures in various degrees, beyond the blood-brain barrier without further interventions. Hypotheses 1. Online multimodal neuromonitoring can accurately detect changes in neuronal metabolic demand and pathological neuronal bioelectrical changes in highly vulnerable brain tissue. 2. Online multimodal neuromonitoring can accurately detect the impact of pathological neuronal bioelectrical changes on metabolic demand in highly vulnerable brain tissue. 3. The occurrence and duration of pathological neuronal bioelectrical changes are dependent on sedatives and antiepileptic drug concentrations 4. The occurrence and duration of pathological neuronal bioelectrical changes have a negative impact on functional and neurological long-term patient outcome. 5. Simultaneous invasive and non-invasive multimodal neuromonitoring can identify a clear relationship of both methods regarding pathological neuronal bioelectrical changes and metabolic brain status. Methods Systematic analysis of MMNM measurements following standardized criteria and correlation of electrophysiological phenomena with cerebral metabolic changes in all included patients. In a second step neuroimaging, cerebral extracellular sedative drug concentrations and neurological functional outcome, will be correlated with both electrophysiologic and metabolic changes. Due to numerous high-resolution parameters, machine learning algorithms will be used to correlate comprehensive data on group and individual levels following a holistic approach. Level of originality Extensive, cutting edge diagnostic methods are used to get a better insight into the pathophysiology of electrophysiological and metabolic changes during the development of secondary brain damage. Due to the immense amount of high-resolution data, a computer-assisted evaluation will be applied to identify relationships in the development of secondary brain injury. For the first time systematic testing of several drug concentrations beyond the blood-brain barrier will be performed. With these combined methods, we will be able to develop new cerebroprotective treatment concepts on an individual basis.