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

这项重大研究仪器奖允许Jonathan Cohen博士和四名合作研究人员购买高性能计算仪器（3，584个核心; 2 TB/核心; 100 TB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。合作的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。提出了两个主要的研究目标，建立在应用机器学习的多变量模式分析（MVPA）方法来检测与内部心理状态相对应的神经信号（如感知，记忆和意图）的最新进展的基础上，否则无法直接观察。 迄今为止，MVPA的使用仅限于”数据完全收集后“的“离线”分析。 然而，大脑成像的一个日益增长和强大的用途是给参与者真实的时间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方式）。 这种实时反馈方法可以通过添加MVPA来大大增强。 然而，直到现在，这在计算上一直是难以处理的。 目标1通过将高性能计算系统插入大脑扫描管道来解决这一挑战。 这将在一项实验中进行测试，该实验使用MVPA来检测与持续注意力相关的大脑活动模式，使我们能够提供基于大脑的实时反馈，以提高注意力能力（具有潜在的教育和健康益处）。目标2关注大脑成像的另一项重大进展，其中分析活动区域之间的相关性，而不是相互孤立的活动区域。 这种相关性--通常被称为“功能连接性”--通过提供有关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一组感兴趣的大脑区域之间的相关性。然而，一个更有效的方法是检查每个领域与所有其他领域的相关性。 这需要计算全脑相关矩阵。通过将MVPA应用于相关模式，将进一步增强对这种高维数据的分析。 然而，这样做进一步增加了计算需求。将这种方法应用于常规的脑成像数据集，使用目前可用的仪器，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。这样做可以将以前难以解决的问题纳入实用范围。 这些方法将在一项旨在识别意图的神经表征及其对负责执行这些意图的大脑机制的影响的实验中进行测试。

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.