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首页> 外文期刊>BMC Neuroscience >Fast reproducible identification and large-scale databasing of individual functional cognitive networks
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Fast reproducible identification and large-scale databasing of individual functional cognitive networks

机译:快速重现性识别和单个功能认知网络的大规模数据库建立

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Background Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level. Results 81 subjects were successfully scanned. Before describing inter-individual variability, we demonstrated in the present study the reliability of individual functional data obtained with this short protocol. Considering the anatomical variability, we then needed to correctly describe individual functional networks in a voxel-free space. We applied then non-voxel based methods that automatically extract main features of individual patterns of activation: group analyses performed on these individual data not only converge to those reported with a more conventional voxel-based random effect analysis, but also keep information concerning variance in location and degrees of activation across subjects. Conclusion This collection of individual fMRI data will help to describe the cerebral inter-subject variability of the correlates of some language, calculation and sensorimotor tasks. In association with demographic, anatomical, behavioral and genetic data, this protocol will serve as the cornerstone to establish a hybrid database of hundreds of subjects suitable to study the range and causes of variation in the cerebral bases of numerous mental processes.
机译:背景技术尽管诸如阅读和计算之类的认知过程与可重现的大脑网络有关,但个体间的差异却相当大。要了解这种变异性的根源,就需要建立大型的多模态数据库,以汇编数百个受试者的行为,解剖,遗传和功能性神经影像学数据。出于这个目标,我们基于5分钟的功能磁共振成像(fMRI)序列设计了一种简单而快速的采集程序,该程序可以像解剖扫描一样轻松,系统地运行,因此可用于接受检查的每个受试者功能磁共振成像在我们的实验室。该协议捕获了听觉和视觉感知,运动动作,阅读,语言理解和心理计算的大脑基础。结果成功扫描了81名受试者。在描述个体之间的变异性之前,我们在本研究中证明了使用此简短协议获得的单个功能数据的可靠性。考虑到解剖变异性,我们然后需要正确描述无体素空间中的各个功能网络。然后,我们应用了基于非体素的方法,该方法会自动提取单个激活模式的主要特征:对这些单个数据进行的分组分析不仅可以汇聚到更常规的基于体素的随机效应分析所报告的数据,而且还能保留有关变异的信息。跨学科的位置和激活程度。结论收集的这些fMRI个体数据将有助于描述某些语言,计算和感觉运动任务相关性的受试者间脑部变异性。结合人口统计学,解剖学,行为学和遗传学数据,该协议将成为建立数百个对象的混合数据库的基石,这些对象适用于研究众多心理过程的大脑基础的变化范围和原因。

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