Computational Infrastructure for Brain Research: EAGER: Next-Generation Neural Data Analysis (NGNDA) Platform: Massive Parallel Analysis of Multi-Modal Brain Networks

脑研究计算基础设施：EAGER：下一代神经数据分析（NGNDA）平台：多模态脑网络的大规模并行分析

• 批准号: 1649865
• 负责人: Catherine Stamoulis
• 金额: $ 30万
• 依托单位: Children's Hospital Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649865&HistoricalAwards=false
• 关键词: Computational; Infrastructure; Brain; Research; EAGER

Unprecedented technological advances over the last decade have facilitated investigation of the brain at exquisite levels of spatial-temporal detail. Ambitious goals of large-scale efforts, including those supported by the BRAIN Initiative, include simultaneously measuring from thousands of neurons for long periods of time, and generating very high resolution images of the brain and its activity. However, enormous volumes of data will be produced by these technologies, and grand-scale analyses of these large datasets are virtually impossible to accomplish with currently available computational tools. This project is focused on addressing a number of these computational limitations by developing a novel and broadly accessible Next-Generation Neural Data Analysis (NGNDA) platform to analyze and integrate large volumes of heterogeneous brain data. This is a collaborative effort of neuroscience researchers, algorithm developers, and computing technology experts from Harvard Medical School Research Computing, the Laboratory of Cognitive Neuroscience at Boston Children's Hospital/Harvard Medical School, the Sleep and Inflammatory Systems Laboratory at Beth Israel Deaconess Medical Center/Harvard Medical School and the Epilepsy Divisions at Boston Children's Hospital and the Medical University of South Carolina. The ultimate goal is to understand not only the healthy brain but also complex diseases and disorders that are affecting progressively larger populations resulting in enormous socioeconomic costs. This project therefore aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare.This project is a pioneer effort to develop a new computational infrastructure, NGNDA, which will be specifically designed for collaborative research, to facilitate grand-scale analysis, simulation and modeling of brain connectomes across species. The overarching goal is to develop the means for intelligent parallel processing of big neural data, and efficient estimation of connectomes across scales of neural organization, via implementation of innovative algorithms and dynamic leveraging and integrating of shared institutional and national high performance computing (HPC) resources. The NGNDA platform will be implemented on the Orchestra HPC resource provided by Harvard Medical School Research Computing, and on resources of the Extreme Science and Engineering Discovery Environment (XSEDE) national computing consortium supported by the National Science Foundation. NGNDA will be validated with four very high-dimensional neural datasets, each posing a unique computational challenge. NGNDA aims to facilitate a "convergence" approach to Neuroscience research whereby expertise and insights from distinct disciplines are merged with cutting-edge resources and tools for a comprehensive investigation of the brain. The NGNDA computational infrastructure will be accessible to thousands of users and all its algorithms and validation data will be freely available to the community; and NGNDA may also eventually serve as a novel e-learning platform for multifaceted collaborative learning and education of next-generation neuroscientists. NGNDA will also facilitate testing the reproducibility and generalization of research findings.This Early-concept Grants for Exploratory Research (EAGER) award by the CISE Division of Advanced Cyberinfrastructure is jointly supported by the SBE Division of Behavioral and Cognitive Sciences, with funds associated with the NSF Understanding the Brain activity including for developing national research infrastructure for neuroscience, and alignment with NSF objectives under the National Strategic Computing Initiative.

在过去的十年里，前所未有的技术进步促进了对大脑时空细节的细致研究。大规模努力的雄心勃勃的目标，包括由BRAIN Initiative支持的那些，包括同时长时间测量数千个神经元，并生成非常高分辨率的大脑及其活动图像。然而，这些技术将产生大量的数据，而对这些大数据集的大规模分析实际上是不可能用现有的计算工具完成的。该项目致力于通过开发一种新颖且可广泛访问的下一代神经数据分析（NGNDA）平台来分析和集成大量异构大脑数据，从而解决许多这些计算限制。这是来自哈佛医学院研究计算、波士顿儿童医院/哈佛医学院认知神经科学实验室、贝斯以色列女执事医疗中心/哈佛医学院睡眠和炎症系统实验室以及波士顿儿童医院和南卡罗来纳医科大学癫痫科的神经科学研究人员、算法开发人员和计算技术专家的合作成果。最终目标是不仅要了解健康的大脑，还要了解影响越来越多的人口并造成巨大社会经济成本的复杂疾病和失调。因此，该项目符合美国国家科学基金会促进科学进步和促进国家健康、繁荣和福利的使命。该项目是开发一种新的计算基础设施NGNDA的先驱，它将专门为合作研究设计，以促进跨物种大脑连接体的大规模分析、模拟和建模。总体目标是通过实施创新算法和动态利用和整合共享的机构和国家高性能计算（HPC）资源，开发大神经数据的智能并行处理手段，以及跨神经组织尺度的连接体的有效估计。NGNDA平台将在哈佛医学院研究计算提供的Orchestra HPC资源以及由美国国家科学基金会支持的极限科学与工程发现环境（XSEDE）国家计算联盟的资源上实施。NGNDA将使用四个非常高维的神经数据集进行验证，每个数据集都提出了独特的计算挑战。NGNDA旨在促进神经科学研究的“融合”方法，即来自不同学科的专业知识和见解与尖端资源和工具相结合，以全面研究大脑。NGNDA计算基础设施将向成千上万的用户开放，其所有算法和验证数据将免费提供给社区；NGNDA也可能最终成为一个新的电子学习平台，为下一代神经科学家提供多方面的协作学习和教育。NGNDA还将有助于测试研究结果的可重复性和泛化性。这个由CISE高级网络基础设施部门颁发的探索性研究早期概念奖（EAGER）由SBE行为和认知科学部联合支持，与NSF理解大脑活动相关的资金，包括发展国家神经科学研究基础设施，并与国家战略计算计划下的NSF目标保持一致。

项目成果

Generative Models For Large-Scale Simulations Of Connectome Development

连接体发育大规模模拟的生成模型

• DOI: 10.1109/icasspw59220.2023.10193544
• 发表时间: 2023
• 期刊: and Signal Processing
• 作者: Brooks, Skylar J;Stamoulis, Catherine
• 通讯作者: Stamoulis, Catherine

Big Data-Driven Brain Parcellation from fMRI: Impact of Cohort Heterogeneity on Functional Connectivity Maps

来自功能磁共振成像的大数据驱动的大脑分区：队列异质性对功能连接图的影响

• DOI: 10.1109/embc46164.2021.9630267
• 发表时间: 2021
• 期刊: Proceedings 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Brooks, Skylar J;Parks, Sean M;Stamoulis, Catherine
• 通讯作者: Stamoulis, Catherine

A Spectral Clustering Approach for the Classification of Waveform Anomalies in High-Dimensional Brain Signals

• DOI: 10.1109/embc44109.2020.9176369
• 发表时间: 2020-07
• 期刊: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
• 作者: C. Stamoulis

Widespread Positive Direct and Indirect Effects of Regular Physical Activity on the Developing Functional Connectome in Early Adolescence

• DOI: 10.1093/cercor/bhab126
• 发表时间: 2021-05-14
• 期刊: CEREBRAL CORTEX
• 影响因子: 3.7
• 作者: Brooks, Skylar J.;Parks, Sean M.;Stamoulis, Catherine
• 通讯作者: Stamoulis, Catherine

SPARSE ANOMALY REPRESENTATIONS IN VERY HIGH-DIMENSIONAL BRAIN SIGNALS

• DOI: 10.1109/dsw.2018.8439924
• 发表时间: 2018-06
• 期刊: 2018 IEEE Data Science Workshop (DSW)
• 作者: C. Stamoulis