Paradigm shift required for translational research on the brain - Yoon, Jong Hyuk; Lee, Dongha; Lee, Chany; Cho, Eunji; Lee, Seulah; Cazenave-Gassiot, Amaury; Kim, Kipom; Chae, Sehyun; Dennis, Edward A.; Suh, Pann-Ghill (Experimental & molecular medicine, 2024)
Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain. Unlocking brain mysteries: high-throughput tools transform brain biomedical research Biomedical research, which is the study of health and disease, has greatly improved our ability to manage diseases and has advanced global healthcare. However, understanding the causes of most brain disorders is still a challenge due to the limitations of traditional research methods. This study introduces a new translational research method that combines high-throughput analysis tools—which are techniques that can analyze a large amount of data quickly - like neuroimaging, multi-omics, and artificial intelligence (AI). The study suggests that combining neuroimaging, multi-omics, and AI can offer valuable insights, such as new disease markers, subtypes, and treatments. The authors believe this new method could overcome the limitations of traditional methods, leading to a better understanding of brain disorders and the development of new diagnostic tools and treatments. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.