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BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis

机译:Brainiak教程:用于高级FMRI分析的用户友好的学习材料

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The analysis of brain activity, as measured using functional magnetic resonance imaging (fMRI), has led to significant discoveries about how the brain processes information and how this is affected by disease. However, exhaustive multivariate analyses in space and time, run across a large number of subjects, can be complex and computationally intensive, creating a high barrier for entry into this field. Furthermore, the materials available to learn these methods do not encompass all the methods used, work is often published with no publicly available code, and the analyses are often difficult to run on large datasets without cluster computing. We have created interactive software tutorials that make it easy to understand and execute advanced analyses on fMRI data using the BrainIAK package an open-source package built in Python. We have released these tutorials freely to the public and have significantly reduced computational roadblocks for users by making it possible to run the tutorials with a web browser and internet connection. We hope that this facilitated access and the usability of the underlying code a compendium for how to program and optimize the latest fMRI analyses will accelerate training, reproducibility, and discovery in cognitive neuroscience.
机译:使用功能磁共振成像(FMRI)测量的大脑活动的分析导致大脑如何处理信息以及如何受疾病影响的重要发现。然而,在空间和时间内彻底的多变量分析,跨越大量科目,可以复杂和计算密集,为进入该字段创造一个高障碍。此外,可用于学习这些方法的材料不包含所使用的所有方法,通常没有公开可用的代码发布的工作,并且在没有集群计算的情况下,分析通常难以在大型数据集上运行。我们创建了交互式软件教程,使得使用Brainiak软件包在Python中构建的开源包易于理解和执行FMRI数据的高级分析。我们已经向公众自由发布了这些教程,并通过使用户可以使用Web浏览器和Internet连接进行教程来显着降低用户的计算障碍。我们希望这一促进的访问和底层代码的可用性是如何编程和优化最新FMRI分析的纲要将加速认知神经科学中的培训,可重复性和发现。

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