Cerebral blood flow hemodynamics and cerebral spinal fluid dynamics monitoring are necessary for many patients with acute brain or spinal cord injury in the neural intensive care unit (NICU) because accurate monitoring of the brain allows the clinicians to manage systemic complications, recognise and treat clinical deterioration, and investigate the cause of brain disorders. Multimodality monitoring (MMM) techniques are essential for optimising cerebral and systemic physiology, which have been extensively applied in NICU, including electroencephalograph (EEG), arterial blood pressure (ABP), intracranial pressure (ICP), respiratory monitoring, microdialysis, brain oxygen, cerebral blood flow, temperature, electrocardiogram (EKG), brain imaging, etc. The accurate assessment of brain metabolism, structure, perfusion and oxygenation status via integration of physiologic and biochemical variables from different types of equipment is vital to avoid secondary injury to the brain. Therefore, integrating MMM information to analyse the physiopathology of brain diseases further and develop personalised treatment strategies to improve patient outcomes is in great demand. Moreover, it is almost impossible for clinicians to aggregate all bedside data to grab the most crucial information and make a quick diagnosis. Thus, artificial intelligence has become popular, enabling immediate detection and prevention of acute neurologic injury and appropriate intervention based on the patient's disease state in the NICU.
Although MMM for brain monitoring has rapidly developed in the past several decades, we still face significant challenges in providing optimal care to critically ill patients in NICU:
• We still lack a better understanding of how the disorders of cerebral blood flow hemodynamics and cerebral spinal fluid dynamics influence different brain diseases.
• Developing feasible personalised treatment techniques or precise medicine techniques at the bedside in NICU to improve patient outcomes needs further research.
• We still need methods for continuous, accurate monitoring of brain state or brain hemodynamics to enable early detection of brain deterioration.
• We have limited devices for efficient data communication and aggregation due to various data output ports of different bedside equipment at the NICU.
Therefore, we need tools to collect high-resolution data that contains rich information about a patient's physiological state with electronic hospital data. This Research Topic aims to provide the most recent update about brain diseases and advanced techniques for MMM in NICU. In addition, we wish to facilitate the development of personalised treatment strategies for patient management in the NICU and improve patient outcomes.
In this Research Topic, we welcome manuscripts addressing the following topics but are not limited to:
1) Basic science about physiopathology or cerebral blood flow hemodynamics and cerebral spinal fluid dynamics of brain diseases, such as hydrocephalus, stroke, hypoxia, traumatic brain injury, subarachnoid hemorrhage, etc.;
2) New techniques for brain state monitoring or brain diseases diagnosis;
3) Research about personalised treatment or precise medicine in NICU;
4) Artificial intelligent techniques for patient outcome prediction in NICU.
Original manuscripts, as well as review papers, are welcome for submission.
Cerebral blood flow hemodynamics and cerebral spinal fluid dynamics monitoring are necessary for many patients with acute brain or spinal cord injury in the neural intensive care unit (NICU) because accurate monitoring of the brain allows the clinicians to manage systemic complications, recognise and treat clinical deterioration, and investigate the cause of brain disorders. Multimodality monitoring (MMM) techniques are essential for optimising cerebral and systemic physiology, which have been extensively applied in NICU, including electroencephalograph (EEG), arterial blood pressure (ABP), intracranial pressure (ICP), respiratory monitoring, microdialysis, brain oxygen, cerebral blood flow, temperature, electrocardiogram (EKG), brain imaging, etc. The accurate assessment of brain metabolism, structure, perfusion and oxygenation status via integration of physiologic and biochemical variables from different types of equipment is vital to avoid secondary injury to the brain. Therefore, integrating MMM information to analyse the physiopathology of brain diseases further and develop personalised treatment strategies to improve patient outcomes is in great demand. Moreover, it is almost impossible for clinicians to aggregate all bedside data to grab the most crucial information and make a quick diagnosis. Thus, artificial intelligence has become popular, enabling immediate detection and prevention of acute neurologic injury and appropriate intervention based on the patient's disease state in the NICU.
Although MMM for brain monitoring has rapidly developed in the past several decades, we still face significant challenges in providing optimal care to critically ill patients in NICU:
• We still lack a better understanding of how the disorders of cerebral blood flow hemodynamics and cerebral spinal fluid dynamics influence different brain diseases.
• Developing feasible personalised treatment techniques or precise medicine techniques at the bedside in NICU to improve patient outcomes needs further research.
• We still need methods for continuous, accurate monitoring of brain state or brain hemodynamics to enable early detection of brain deterioration.
• We have limited devices for efficient data communication and aggregation due to various data output ports of different bedside equipment at the NICU.
Therefore, we need tools to collect high-resolution data that contains rich information about a patient's physiological state with electronic hospital data. This Research Topic aims to provide the most recent update about brain diseases and advanced techniques for MMM in NICU. In addition, we wish to facilitate the development of personalised treatment strategies for patient management in the NICU and improve patient outcomes.
In this Research Topic, we welcome manuscripts addressing the following topics but are not limited to:
1) Basic science about physiopathology or cerebral blood flow hemodynamics and cerebral spinal fluid dynamics of brain diseases, such as hydrocephalus, stroke, hypoxia, traumatic brain injury, subarachnoid hemorrhage, etc.;
2) New techniques for brain state monitoring or brain diseases diagnosis;
3) Research about personalised treatment or precise medicine in NICU;
4) Artificial intelligent techniques for patient outcome prediction in NICU.
Original manuscripts, as well as review papers, are welcome for submission.