MRI: Acquisition of High Performance Compute Cluster for Multivariate Real-time and Whole-brain Correlation Analysis of fMRI Data

MRI：获取高性能计算集群，用于功能磁共振成像数据的多变量实时和全脑相关分析

• 批准号: 1229597
• 负责人: Jonathan Cohen
• 金额: $ 52.8万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1229597&HistoricalAwards=false
• 关键词: MRI; Acquisition; Performance; Compute; Cluster

This Major Research Instrumentation award permits Dr. Jonathan Cohen and four co-investigators to purchase a high-performance computing instrumentation (3,584 cores; 2TB/core; 100TB flash storage) to be used by faculty, postdocs, graduate students and undergraduates within the Princeton Neuroscience Institute (PNI). The instrumentation will allow the analysis of human brain imaging data at a speed and scale not previously possible.The collaborating researchers are cognitive neuroscientists and computer scientists at Princeton with complementary expertise in human brain imaging and large scale computing. Two primary research objectives are proposed, building on recent progress in applying multivariate pattern analysis (MVPA) methods from machine learning to detect neural signals that correspond to internal mental states, such as perceptions, memories and intentions that are otherwise not accessible to direct observation. To date, use of MVPA has been restricted to the "offline" analyses" after data have been fully collected. However, a growing and powerful use of brain imaging is to give participants feedback about their brain states in real time, allowing them to use this information to better control brain function (e.g., providing feedback about pain areas as a way of learning to control chronic pain). Such real-time feedback methods could be greatly enhanced by adding MVPA. However, this has been computationally intractable until now. Objective 1 addresses this challenge, by inserting a high performance computing system into the brain scanning pipeline. This will be tested in an experiment that uses MVPA to detect patterns of brain activity associated with sustained attention, allowing us to provide real-time brain-based feedback to improve attentional abilities (with potential educational and health benefits).Objective 2 focuses on another major advance in brain imaging, in which correlations between areas of activity are analyzed, rather than areas of activity in isolation of one another. Such correlations - often referred to as "functional connectivity" - are likely to reveal more about how the brain actually functions, by providing critical information about the interactions between areas. At present, virtually all approaches to functional connectivity focus on the correlations among a limited set of brain areas of interest. However, a more powerful approach would be to examine the correlation of every area with all others. This requires computing the whole-brain correlation matrix. The analysis of such high dimensional data would be further enhanced by applying MVPA to patterns of correlation. However, doing this further increases computational demands. Applying this approach to a routine brain imaging dataset, using currently available instrumentation, would take 880 years to complete. The work under Objective 2 addresses this challenge, by coupling massively parallel computing with sophisticated software optimizations. Doing so can bring previously intractable problems into the range of practicality. These methods will be tested in an experiment that seeks to identify neural representations of intentions, and their influence on brain mechanisms responsible for executing these intentions.

这项主要研究仪器奖允许Jonathan Cohen博士和四名共同研究人员购买高性能计算仪器（3,584核，2TB/核，100TB闪存），供普林斯顿神经科学研究所（PNI）的教师，博士后，研究生和本科生使用。该仪器将允许以前所未有的速度和规模分析人脑成像数据。合作研究人员是普林斯顿大学的认知神经科学家和计算机科学家，他们在人脑成像和大规模计算方面具有互补的专业知识。本文提出了两个主要的研究目标，基于最近在应用机器学习的多元模式分析（MVPA）方法来检测与内部心理状态（如感知、记忆和意图）相对应的神经信号方面的进展，这些神经信号是无法直接观察到的。迄今为止，MVPA的使用仅限于“在数据完全收集之后”的“离线”分析。然而，脑成像的一个日益增长和强大的用途是实时给参与者反馈他们的大脑状态，允许他们使用这些信息来更好地控制大脑功能（例如，提供关于疼痛区域的反馈，作为学习控制慢性疼痛的一种方式）。这种实时反馈方法可以通过加入MVPA得到极大的增强。然而，到目前为止，这在计算上一直很棘手。目标1通过将高性能计算系统插入脑扫描管道来解决这一挑战。这将在一个实验中进行测试，该实验使用MVPA来检测与持续注意力相关的大脑活动模式，使我们能够提供实时的基于大脑的反馈，以提高注意力能力（具有潜在的教育和健康益处）。目标2侧重于脑成像的另一项重大进展，其中分析了活动区域之间的相关性，而不是彼此孤立的活动区域。这种相关性——通常被称为“功能连接”——通过提供有关大脑区域之间相互作用的关键信息，可能会揭示更多关于大脑实际功能的信息。目前，几乎所有的功能连接方法都集中在有限的大脑兴趣区域之间的相关性上。然而，更有效的方法是检查每个区域与所有其他区域的相关性。这需要计算全脑相关矩阵。通过将MVPA应用于相关模式，可以进一步加强对此类高维数据的分析。然而，这样做会进一步增加计算需求。将这种方法应用于常规的脑成像数据集，使用现有的仪器，将需要880年才能完成。目标2下的工作通过将大规模并行计算与复杂的软件优化相结合来解决这一挑战。这样做可以使以前难以解决的问题变得可行。这些方法将在一个实验中进行测试，该实验旨在识别意图的神经表征，以及它们对负责执行这些意图的大脑机制的影响。