The desire to understand the human brain has been the main interests of
cognitive neuroscientists throughout the past centuries. More recently, the neuroscience technology has experienced a rapid growth, which makes a map of the human brain even possible. The human brain mapping, along the same scope and scale of the human genome mapping, would be another huge step toward the personalized medicine dreamland. The key breakthroughs in the development of effective treatment of neurological and psychiatric disease will require innovative techniques to enable scientists to record and analyze signals from 100 billion neurons in brain in much larger numbers and at even faster speeds. These neuroscience methods usually generate huge volumes of data with often many terabytes involving various high resolution imaging modalities (fMRI, PET, DTI, EEG, etc.) that measure different perspectives on brain functions or structures. Since the human brain operates at multiple spatial and temporal scales, all of these data sources are potentially of use, and a fully understanding of ``how the brain works" is only possible by synthesizing all of those information. Thus, the efficient analysis of big data and the development of high-performance computing tools become critical to neuroscience.
Statistical methods for ultra-high dimensional data have been a dramatic success in many applications such as computer science, machine learning and genetics, however, there is limited literature in neuroscience, which motivates the needs of this Frontiers Research Topic. The goal of this topic is to provide a forum for researchers to contribute their investigations on statistical challenges and approaches to exploring the mystery of the human brain using big data. Contributors with diverse backgrounds will be welcomed. The established researchers in computational neurosciences will be encouraged to demonstrate their newly developed methods and provide insights on the statistical modeling in neurosciences. The call for participation will also go to the leading statisticians and computer scientists who will bring statistical and computational innovations that can be employed to address the important questions in neurosciences. In addition, this topic will potentially attract much attentions from neuroscientists, psychologists, physicists and mathematician.
The desire to understand the human brain has been the main interests of
cognitive neuroscientists throughout the past centuries. More recently, the neuroscience technology has experienced a rapid growth, which makes a map of the human brain even possible. The human brain mapping, along the same scope and scale of the human genome mapping, would be another huge step toward the personalized medicine dreamland. The key breakthroughs in the development of effective treatment of neurological and psychiatric disease will require innovative techniques to enable scientists to record and analyze signals from 100 billion neurons in brain in much larger numbers and at even faster speeds. These neuroscience methods usually generate huge volumes of data with often many terabytes involving various high resolution imaging modalities (fMRI, PET, DTI, EEG, etc.) that measure different perspectives on brain functions or structures. Since the human brain operates at multiple spatial and temporal scales, all of these data sources are potentially of use, and a fully understanding of ``how the brain works" is only possible by synthesizing all of those information. Thus, the efficient analysis of big data and the development of high-performance computing tools become critical to neuroscience.
Statistical methods for ultra-high dimensional data have been a dramatic success in many applications such as computer science, machine learning and genetics, however, there is limited literature in neuroscience, which motivates the needs of this Frontiers Research Topic. The goal of this topic is to provide a forum for researchers to contribute their investigations on statistical challenges and approaches to exploring the mystery of the human brain using big data. Contributors with diverse backgrounds will be welcomed. The established researchers in computational neurosciences will be encouraged to demonstrate their newly developed methods and provide insights on the statistical modeling in neurosciences. The call for participation will also go to the leading statisticians and computer scientists who will bring statistical and computational innovations that can be employed to address the important questions in neurosciences. In addition, this topic will potentially attract much attentions from neuroscientists, psychologists, physicists and mathematician.