Brain aging and neurodegeneration diseases are affect more than 720 million people over the age of 65 in the world, accounting for 9.3% of the total population, and the number is expected to reach 1.6 billion by 2050. Brain aging and brain diseases are often accompanied by changes in brain. Structural and functional changes occur in the brain during aging that may succumb to neurodegenerative cascades that result in disorders. Early detection of these changes is helpful for early diagnosis and recovery.
Neuromodulation could improve brain function through physical stimulations such as ultrasound, light, electricity, and magnetism. It has been applied to the enhancement of working memory in the elders, regulation of Alzheimer's disease, insomnia, and other diseases. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning/deep learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. Engineering methods and advanced artificial intelligence algorithms could help revealing the mechanisms of brain aging and brain diseases. And it will uncover the neuroplasticity of the brain, explain how cognitive functions arise and develop, and serve cognitive enhancement in the elderly and brain disease rehabilitation in patients.
The research focus of this topic is on the mechanism of brain aging and neuroplasticity, diagnosis, and rehabilitation of brain diseases. This Research Topic will seek contributions on brain aging and brain diseases that focus on the following topics: develop new medical detection tools and algorithms to improve the accuracy of brain activity monitoring; analyze brain information data such as EEG, MRI, and fNIRS based on new algorithms such as artificial intelligence to promote understanding of brain mechanisms and diagnosis of brain diseases; combined with noninvasive neuromodulation technology, such as tDCS, tACS, TMS, tFUS, to treat brain diseases, or enhancement of brain function.
Potential topics include, but are not limited to:
- Diagnosis, treatment, or prevention of brain disorder and disease
- Mechanisms and applications of noninvasive neuromodulation technologies
- Machine learning/deep learning techniques for medical image analysis
- Aging and brain plasticity
- Detection technology and data processing of medical information
Brain aging and neurodegeneration diseases are affect more than 720 million people over the age of 65 in the world, accounting for 9.3% of the total population, and the number is expected to reach 1.6 billion by 2050. Brain aging and brain diseases are often accompanied by changes in brain. Structural and functional changes occur in the brain during aging that may succumb to neurodegenerative cascades that result in disorders. Early detection of these changes is helpful for early diagnosis and recovery.
Neuromodulation could improve brain function through physical stimulations such as ultrasound, light, electricity, and magnetism. It has been applied to the enhancement of working memory in the elders, regulation of Alzheimer's disease, insomnia, and other diseases. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning/deep learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. Engineering methods and advanced artificial intelligence algorithms could help revealing the mechanisms of brain aging and brain diseases. And it will uncover the neuroplasticity of the brain, explain how cognitive functions arise and develop, and serve cognitive enhancement in the elderly and brain disease rehabilitation in patients.
The research focus of this topic is on the mechanism of brain aging and neuroplasticity, diagnosis, and rehabilitation of brain diseases. This Research Topic will seek contributions on brain aging and brain diseases that focus on the following topics: develop new medical detection tools and algorithms to improve the accuracy of brain activity monitoring; analyze brain information data such as EEG, MRI, and fNIRS based on new algorithms such as artificial intelligence to promote understanding of brain mechanisms and diagnosis of brain diseases; combined with noninvasive neuromodulation technology, such as tDCS, tACS, TMS, tFUS, to treat brain diseases, or enhancement of brain function.
Potential topics include, but are not limited to:
- Diagnosis, treatment, or prevention of brain disorder and disease
- Mechanisms and applications of noninvasive neuromodulation technologies
- Machine learning/deep learning techniques for medical image analysis
- Aging and brain plasticity
- Detection technology and data processing of medical information