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
• 负责人: Vince Calhoun
• 金额: $ 500万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112455&HistoricalAwards=false
• 关键词: CREST; Center; Dynamic; Multiscale; Multimodal

Center for dynamic multiscale and multimodal brain mapping over the lifespan The Centers of Research Excellence in Science and Technology (CREST) program supports the enhancement of research capabilities of minority-serving institutions through the establishment of centers that effectively integrate education and research. CREST promotes the development of new knowledge, enhancements of the research productivity of individual faculty, and an expanded presence of students historically underrepresented in science, technology, engineering, and mathematics disciplines. With National Science Foundation support, Georgia State University establishes the Center for dynamic multiscale and multimodal brain mapping over the lifespan [D-MAP] to study the links between brain development across the lifespan. The Center aims to understand brain structure and connectivity across multiple scales with three synergistic research studies. The proposed work will promote undergraduate development in preparation for STEM education and careers; create opportunities for graduate students to work in multidisciplinary environments; and develop education and training modules that can be integrated into existing graduate and undergraduate curricula.The study of links between early brain development, adulthood, and senescence throughout the lifespan is an important and understudied area. Subproject 1 (Unimodal Brain Dynamics) develops methods to advance understanding of time-varying brain connectivity and the evolution of whole brain connectivity patterns over time. New methods are needed that can incorporate explicitly spatial information into dynamics, estimate potential nonlinear relationships, and integrate dynamic information across scales. These methods will be applied to study the short and long-term dynamics of reading acquisition. Subproject 2 (Multimodal Data Fusion) develops novel methods to lead the field in multivariate approaches to model linked changes in multi-modal measures and their trajectories over the lifespan. Key contributions include the incorporation of network subspaces, flexible approaches to identify links between data with mismatched dimensionality, and the development of multimodal models that leverage deep learning to capture more complex relationships. Initial emphasis will be on multimodal MRI and EEG/MEG data. The focused application is to study the multimodal signatures of cognition and mood. Subproject 3 (Predictive Neuroimaging) focuses specifically on approaches to leverage lifespan data for individualized prediction. The subproject exploits large open data repositories to develop predictive fingerprints of development and aging along multiple dimensions. Anticipated contributions include novel predictive multimodal models that evolve both within and among individuals, advanced visualization approaches to enhance interpretability, and development and use of neuroinformatics infrastructure for reproducible large N brain imaging data analysis of various populations. The focused application will be to use neuroimaging to predict aspects of linguistic processing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在整个生命周期内进行动态多尺度和多模式大脑映射的中心科学和技术卓越研究中心（CREST）计划通过建立有效整合教育和研究的中心，支持增强为少数民族服务的机构的研究能力。 CREST促进新知识的发展，提高个别教师的研究生产力，并扩大历史上在科学，技术，工程和数学学科中代表性不足的学生的存在。 在美国国家科学基金会的支持下，格鲁吉亚州立大学建立了生命周期动态多尺度和多模式脑映射中心[D-MAP]，以研究整个生命周期中大脑发育之间的联系。 该中心旨在通过三项协同研究来了解多个尺度的大脑结构和连通性。 拟议的工作将促进本科生的发展，为STEM教育和职业做准备;为研究生创造在多学科环境中工作的机会;并开发可整合到现有研究生和本科生课程中的教育和培训模块。早期大脑发育，成年和整个生命周期衰老之间的联系的研究是一个重要但未充分研究的领域。 子项目1（单峰脑动力学）开发方法，以促进对随时间变化的大脑连接和全脑连接模式随时间的演变的理解。 需要新的方法，可以明确地将空间信息纳入动态，估计潜在的非线性关系，并整合跨尺度的动态信息。这些方法将被应用于研究阅读习得的短期和长期动态。 子项目2（多模态数据融合）开发了新的方法，在多变量方法领域领先，以模拟多模态测量及其生命周期轨迹的相关变化。 主要贡献包括网络子空间的整合，识别具有不匹配维度的数据之间的链接的灵活方法，以及利用深度学习来捕获更复杂关系的多模态模型的开发。 最初的重点将是多模态MRI和EEG/MEG数据。 重点应用是研究认知和情绪的多模态特征。 子项目3（预测神经影像学）特别关注利用寿命数据进行个性化预测的方法。 该子项目利用大型开放式数据库，沿沿着多个维度开发发育和衰老的预测指纹。 预期的贡献包括新的预测多模态模型，在个人内部和之间的发展，先进的可视化方法，以提高可解释性，以及开发和使用神经信息学基础设施，可重复的大N脑成像数据分析的各种人群。 该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Independent Vector Analysis Based Subgroup Identification from Multisubject fMRI Data

基于独立向量分析的多受试者 fMRI 数据亚组识别

• DOI: 10.1109/icassp43922.2022.9747224
• 发表时间: 2022
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Yang, H.;Akhonda, M. A.;Ghayem, F.;Long, Q.;Calhoun, V. D.;Adali, T.
• 通讯作者: Adali, T.

An Accelerated Rank-(L,L,1,1) Block Term Decomposition Of Multi-Subject Fmri Data Under Spatial Orthonormality Constraint

空间正交性约束下多主体Fmri数据的加速Rank-(L,L,1,1)块项分解

• DOI: 10.1109/icassp43922.2022.9746404
• 发表时间: 2022
• 期刊: & Signal Processing (ICASSP
• 作者: Kuang, Li-Dan;Wang, Biao;Lin, Qiu-Hua;Zhang, Hao-Peng;Zhang, Jianming;Li, Wenjun;Li, Feng;Calhoun, Vince D.
• 通讯作者: Calhoun, Vince D.

Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data.

• DOI: 10.3390/s22031224
• 发表时间: 2022-02-05
• 期刊: Sensors (Basel, Switzerland)
• 作者: Akhonda MABS;Levin-Schwartz Y;Calhoun VD;Adali T
• 通讯作者: Adali T

Trauma moderates the development of the oscillatory dynamics serving working memory in a sex-specific manner

创伤调节了以性别特异性方式服务于工作记忆的振荡动力学的发展

• DOI: 10.1093/cercor/bhac008
• 发表时间: 2022
• 期刊: Cerebral Cortex
• 影响因子: 3.7
• 作者: Killanin, Abraham D;Embury, Christine M;Picci, Giorgia;Heinrichs-Graham, Elizabeth;Wang, Yu-Ping;Calhoun, Vince D;Stephen, Julia M;Wilson, Tony W
• 通讯作者: Wilson, Tony W

An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.

• DOI: 10.1016/j.media.2022.102413
• 发表时间: 2022-05
• 期刊: MEDICAL IMAGE ANALYSIS
• 影响因子: 10.9
• 作者: Zhao, Min;Yan, Weizheng;Luo, Na;Zhi, Dongmei;Fu, Zening;Du, Yuhui;Yu, Shan;Jiang, Tianzi;Calhoun, Vince D;Sui, Jing
• 通讯作者: Sui, Jing