Collaborative Research: NCS-FO: Flexible Large-Scale Brain Imaging Analysis: Diversity, Individuality and Scalability

合作研究:NCS-FO:灵活的大规模脑成像分析:多样性、个性化和可扩展性

基本信息

项目摘要

This project is designed to develop important analysis methods for brain imaging data, provide new educational and outreach activities to help promote the workforce, and create a software tool to foster big data analysis of the human brain. Functional magnetic resonance imaging (fMRI) enables noninvasive study of brain function, typically through the estimation of functional networks of connectivity. These networks are relatively stable, but it is also clear that there is a wide degree of differences across individuals. Given that now large-scale multi-subject data have now become available across multiple repositories, there is a pressing need for the development of a flexible analysis framework for large-scale fMRI data that can capture the global traits in brain activity, while not losing the individual aspects of a given brain. Such an accurate estimation of each subject's functional connectivity maps enables the leveraging of large and distributed fMRI repositories. It also promises effective comparisons across different conditions, groups, and time points, thus further increasing the usefulness of fMRI in human brain research. The project provides rich educational experience necessary for student training and workforce development in this fast growing field. The benefits are permeated even further via undergraduate research projects and public outreach programs such as brain awareness weeks, in addition to scholarly dissemination through publications, presentations, and workshop organization. The software toolbox developed as part of the project is freely distributed and enables wider adoption and reuse of the methods by the academia and the practitioners to move forward the brain research collectively. Ultimately, the project outcomes contribute to the NSF's mission of promoting the progress of science and advancing the national health, prosperity and welfare.Data-driven methods based on latent variable models such as independent component analysis (ICA) have been increasingly adopted in fMRI data analysis. Recently, there have been lively debates as to whether ICA leverages source independence, exploits sparsity, or both, igniting active research in sparse matrix models such as dictionary learning (DL) for fMRI analysis. Indeed, synergistically balancing multiple notions of diversity remains an important challenge. In this context, it is first recognized that jointly leveraging both independence and sparsity enables a powerful and flexible framework for analyzing large-scale fMRI data, by capturing the common traits as well as individual details in a data-driven manner. Therefore, the complementary strengths of the already widely used blind source separation approaches such as ICA, and the more recent, sparse matrix factorization models such as DL are advantageously integrated. Essential practical aspects for large-scale data integration studies, such as decentralized computation and privacy-aware sharing of the datasets across multiple repositories are also addressed by leveraging the complementary expertise of the team.
该项目旨在开发脑成像数据的重要分析方法,提供新的教育和推广活动,以帮助促进劳动力,并创建一个软件工具,以促进人类大脑的大数据分析。功能磁共振成像(fMRI)能够对大脑功能进行无创研究,通常是通过对连接功能网络的估计。这些网络相对稳定,但也很明显,个体之间存在很大程度的差异。鉴于现在大规模的多主题数据已经可以在多个存储库中获得,因此迫切需要开发一种灵活的大规模fMRI数据分析框架,以捕获大脑活动的全局特征,同时不丢失给定大脑的单个方面。这种对每个受试者的功能连接图的准确估计使得利用大型和分布式的fMRI存储库成为可能。它还承诺在不同条件、群体和时间点之间进行有效的比较,从而进一步提高功能磁共振成像在人类大脑研究中的实用性。该项目为这个快速发展的领域的学生培训和劳动力发展提供了丰富的教育经验。除了通过出版物、演讲和研讨会组织的学术传播之外,这些好处还通过本科生研究项目和公众宣传计划(如大脑意识周)进一步渗透。作为项目的一部分开发的软件工具箱是免费分发的,使学术界和实践者能够更广泛地采用和重用这些方法,共同推进大脑研究。最终,项目成果有助于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。基于潜在变量模型的数据驱动方法如独立分量分析(ICA)在功能磁共振成像数据分析中得到越来越多的应用。最近,关于ICA是否利用源独立性,利用稀疏性,或两者兼有,引发了对稀疏矩阵模型(如用于fMRI分析的字典学习(DL))的积极研究。的确,协同平衡多种多样性概念仍然是一项重要挑战。在此背景下,我们首先认识到,通过以数据驱动的方式捕获共同特征和个体细节,联合利用独立性和稀疏性,可以为分析大规模fMRI数据提供强大而灵活的框架。因此,已经广泛使用的盲源分离方法(如ICA)和最近的稀疏矩阵分解模型(如DL)的互补优势得到了有利的整合。大规模数据集成研究的基本实践方面,如分散计算和跨多个存储库的数据集的隐私意识共享,也通过利用团队的互补专业知识来解决。

项目成果

期刊论文数量(22)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury.
  • DOI:
    10.1002/brb3.809
  • 发表时间:
    2017-10
  • 期刊:
  • 影响因子:
    3.1
  • 作者:
    Vergara VM;Mayer AR;Damaraju E;Calhoun VD
  • 通讯作者:
    Calhoun VD
COINSTAC: Decentralizing the future of brain imaging analysis.
  • DOI:
    10.12688/f1000research.12353.1
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ming J;Verner E;Sarwate A;Kelly R;Reed C;Kahleck T;Silva R;Panta S;Turner J;Plis S;Calhoun V
  • 通讯作者:
    Calhoun V
IVA using complex multivariate GGD: application to fMRI analysis
  • DOI:
    10.1007/s11045-019-00685-0
  • 发表时间:
    2019-10
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    Rami Mowakeaa;Zois Boukouvalas;Qunfang Long;T. Adalı
  • 通讯作者:
    Rami Mowakeaa;Zois Boukouvalas;Qunfang Long;T. Adalı
A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy
  • DOI:
    10.1016/j.neuroimage.2019.06.021
  • 发表时间:
    2019-10-15
  • 期刊:
  • 影响因子:
    5.7
  • 作者:
    von Luehmann, Alexander;Boukouvalas, Zois;Adali, Tulay
  • 通讯作者:
    Adali, Tulay
Capturing Common and Individual Components in fMRI Data by Discriminative Dictionary Learning
通过判别字典学习捕获 fMRI 数据中的常见和单独成分
  • DOI:
    10.1109/acssc.2018.8645300
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Dontaraju, Krishna;Kim, Seung-Jun;Akhonda, Mohammad;Adali, Tulay
  • 通讯作者:
    Adali, Tulay
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Vince Calhoun其他文献

Unsupervised feature extraction by time-contrastive learning from resting-state fMRI data
通过静息态 fMRI 数据的时间对比学习进行无监督特征提取
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Hiroshi Morioka;Vince Calhoun;Aapo Hyvarinen;Aapo Hyvarinen and Hiroshi Morioka;Hiroshi Morioka and Aapo Hyvarinen
  • 通讯作者:
    Hiroshi Morioka and Aapo Hyvarinen
Dissemination in time and space in presymptomatic granulin mutation carriers: A GENFI dynamic functional network connectivity study
  • DOI:
    10.1016/j.jns.2021.117779
  • 发表时间:
    2021-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Marcello Giunta;Enrico Premi;Armin Iraji;Alberto Benussi;Stefano Gazzina;Vince Calhoun;Alessandro Padovani;Barbara Borroni; Genetic Frontotemporal Dementia Initiative Genfi
  • 通讯作者:
    Genetic Frontotemporal Dementia Initiative Genfi
Age-Related Prefrontal Network Connectivity Pattern Changes are Associated With Risk for Psychosis
  • DOI:
    10.1016/j.biopsych.2021.02.878
  • 发表时间:
    2021-05-01
  • 期刊:
  • 影响因子:
  • 作者:
    Roberta Passiatore;Linda Antonucci;Thomas DeRamus;Leonardo Fazio;Giuseppe Stolfa;Ileana Andriola;Marina Sangiuliano;Mario Altamura;Alessandro Saponaro;Flora Brudaglio;Angela Carofiglio;Teresa Popolizio;Paolo Taurisano;Fabio Sambataro;Giuseppe Blasi;Alessandro Bertolino;Vince Calhoun;Giulio Pergola
  • 通讯作者:
    Giulio Pergola
21. Associations of Physical Frailty With Health Outcomes and Brain Structure in 483,033 Adults From the UK Biobank
  • DOI:
    10.1016/j.biopsych.2023.02.204
  • 发表时间:
    2023-05-01
  • 期刊:
  • 影响因子:
  • 作者:
    Rongtao Jiang;Stephanie Noble;Jing Sui;Vince Calhoun;Dustin Scheinost
  • 通讯作者:
    Dustin Scheinost
P437. High-Resolution Structural MRI Suggests Protective Effects of Amygdala and Hippocampal Subregional Volume Following Traumatic Experiences
  • DOI:
    10.1016/j.biopsych.2022.02.673
  • 发表时间:
    2022-05-01
  • 期刊:
  • 影响因子:
  • 作者:
    Giorgia Picci;Nicholas Christopher-Hayes;Nathan Petro;Brittany Taylor;Jacob Eastman;Michaela Frenzel;Yu-Ping Wang;Julia Stephen;Vince Calhoun;Tony Wilson
  • 通讯作者:
    Tony Wilson

Vince Calhoun的其他文献

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{{ truncateString('Vince Calhoun', 18)}}的其他基金

Collaborative Research:CISE-ANR:CIF:Small:Learning from Large Datasets - Application to Multi-Subject fMRI Analysis
合作研究:CISE-ANR:CIF:Small:从大数据集中学习 - 多对象 fMRI 分析的应用
  • 批准号:
    2316421
  • 财政年份:
    2023
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
CREST Center for Dynamic Multiscale and Multimodal Brain Mapping Over The Lifespan [D-MAP]
CREST 生命周期动态多尺度和多模式脑图谱中心 [D-MAP]
  • 批准号:
    2112455
  • 财政年份:
    2021
  • 资助金额:
    $ 10万
  • 项目类别:
    Continuing Grant
Collaborative Research: NCS-FO: Flexible Large-Scale Brain Imaging Analysis: Diversity, Individuality and Scalability
合作研究:NCS-FO:灵活的大规模脑成像分析:多样性、个性化和可扩展性
  • 批准号:
    1631819
  • 财政年份:
    2016
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
CIF: Small: Collaborative Research: Entropy Rate for Source Separation and Model Selection: Applications in fMRI and EEG Analysis
CIF:小型:合作研究:源分离和模型选择的熵率:在功能磁共振成像和脑电图分析中的应用
  • 批准号:
    1116944
  • 财政年份:
    2011
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
III: Small: Collaborative Research: Canonical Dependence Analysis for Multi-modal Data Fusion and Source Separation
III:小:协作研究:多模态数据融合和源分离的典型依赖分析
  • 批准号:
    1016619
  • 财政年份:
    2010
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
Complex-Valued Signal Processing and its Application to Analysis of Brain Imaging Data
复值信号处理及其在脑成像数据分析中的应用
  • 批准号:
    0840895
  • 财政年份:
    2008
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
Collaborative Research: SEI: Independent Component Analysis of Complex-Valued Brain Imaging Data
合作研究:SEI:复值脑成像数据的独立成分分析
  • 批准号:
    0715022
  • 财政年份:
    2006
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
Collaborative Research: SEI: Independent Component Analysis of Complex-Valued Brain Imaging Data
合作研究:SEI:复值脑成像数据的独立成分分析
  • 批准号:
    0612104
  • 财政年份:
    2006
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant

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  • 批准号:
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  • 批准年份:
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Collaborative Research: NCS-FR: Individual variability in auditory learning characterized using multi-scale and multi-modal physiology and neuromodulation
合作研究:NCS-FR:利用多尺度、多模式生理学和神经调节表征听觉学习的个体差异
  • 批准号:
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Collaborative Research: NCS-FO: Modified two-photon microscope with high-speed electrowetting array for imaging voltage transients in cerebellar molecular layer interneurons
合作研究:NCS-FO:带有高速电润湿阵列的改良双光子显微镜,用于对小脑分子层中间神经元的电压瞬变进行成像
  • 批准号:
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