为了保持原始功能磁共振成像(function Magnetic Resonance Imaging, fMRI)数据的空间结构实现噪声体素移除,提高聚类的效果,提出了一种基于遗传算法的简易优化算法.以广义线性模型(Generalize Linear Model, GLM)方法获取的数据作为大...为了保持原始功能磁共振成像(function Magnetic Resonance Imaging, fMRI)数据的空间结构实现噪声体素移除,提高聚类的效果,提出了一种基于遗传算法的简易优化算法.以广义线性模型(Generalize Linear Model, GLM)方法获取的数据作为大脑视觉刺激的真实激活模板,基于遗传算法对5位被测者的fMRI数据图像进行分析.取不同阈值(0.1~0.9)时交叉率和变异率为0且具有唯一的最优值为模糊C均值聚类算法(Fuzzy C-Means, FCM)结果,依据真实模板验证聚类结果的准确度.结果表明,相比原始的FCM方法,改变阈值的大小可以使FCM聚类结果的准确度得到有效提高.通过简易优化遗传算法,可以确定最佳阈值为0.6.展开更多
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
文摘为了保持原始功能磁共振成像(function Magnetic Resonance Imaging, fMRI)数据的空间结构实现噪声体素移除,提高聚类的效果,提出了一种基于遗传算法的简易优化算法.以广义线性模型(Generalize Linear Model, GLM)方法获取的数据作为大脑视觉刺激的真实激活模板,基于遗传算法对5位被测者的fMRI数据图像进行分析.取不同阈值(0.1~0.9)时交叉率和变异率为0且具有唯一的最优值为模糊C均值聚类算法(Fuzzy C-Means, FCM)结果,依据真实模板验证聚类结果的准确度.结果表明,相比原始的FCM方法,改变阈值的大小可以使FCM聚类结果的准确度得到有效提高.通过简易优化遗传算法,可以确定最佳阈值为0.6.
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.
