The human brain is a huge,complex system generating brain activity.The exploration of human brain function using functional magnetic resonance imaging(f MRI) is a promising method to understand brain activity.However,...The human brain is a huge,complex system generating brain activity.The exploration of human brain function using functional magnetic resonance imaging(f MRI) is a promising method to understand brain activity.However,the complexity of the big data generated by f MRI facilitates the analysis of various levels of human brain activity,such as the distribution of neural representations,the interaction between different regions,and the dynamic interaction over time.These different levels can depict distinct prospects of the human brain activity,and considerable progress has been achieved.In the future,more big data analysis methods combining advances in computer science,including larger-scale computing,machine learning,and graph theory,will promote the understanding of the human brain.展开更多
The field of functional neuroimaging has substantially advanced as a big data science in the past decade,thanks to international collaborative projects and community efforts.Here we conducted a literature review on fu...The field of functional neuroimaging has substantially advanced as a big data science in the past decade,thanks to international collaborative projects and community efforts.Here we conducted a literature review on functional neuroimaging,with focus on three general challenges in big data tasks:data collection and sharing,data infrastructure construction,and data analysis methods.The review covers a wide range of literature types including perspectives,database descriptions,methodology developments,and technical details.We show how each of the challenges was proposed and addressed,and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community.Furthermore,based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries,we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure,methodology development toward improved learning capability,and multi-discipline translational research framework for this new era of big data.展开更多
基金supported by the Key Program of the National Natural Science Foundation of China(91320201)the Funds for International Cooperation and Exchange of the NationalNatural Science Foundation of China(61210001)+1 种基金the Excellent Young Scientist Program of China(61222113)the Program for New Century Excellent Talents in University(NCET-12-0056)
文摘The human brain is a huge,complex system generating brain activity.The exploration of human brain function using functional magnetic resonance imaging(f MRI) is a promising method to understand brain activity.However,the complexity of the big data generated by f MRI facilitates the analysis of various levels of human brain activity,such as the distribution of neural representations,the interaction between different regions,and the dynamic interaction over time.These different levels can depict distinct prospects of the human brain activity,and considerable progress has been achieved.In the future,more big data analysis methods combining advances in computer science,including larger-scale computing,machine learning,and graph theory,will promote the understanding of the human brain.
基金supported by the National Institutes of Health,United States(Grant No.RF1AG052653)
文摘The field of functional neuroimaging has substantially advanced as a big data science in the past decade,thanks to international collaborative projects and community efforts.Here we conducted a literature review on functional neuroimaging,with focus on three general challenges in big data tasks:data collection and sharing,data infrastructure construction,and data analysis methods.The review covers a wide range of literature types including perspectives,database descriptions,methodology developments,and technical details.We show how each of the challenges was proposed and addressed,and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community.Furthermore,based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries,we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure,methodology development toward improved learning capability,and multi-discipline translational research framework for this new era of big data.
