CRII: RI: Using Large-Scale Neuroanatomy Datasets to Quantify the Mesoscale Architecture of the Brain

CRII：RI：使用大规模神经解剖学数据集来量化大脑的中尺度结构

• 批准号: 1755871
• 负责人: Eva Dyer
• 金额: $ 17.5万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755871&HistoricalAwards=false
• 关键词: CRII; RI; Using; Large; Scale

Methods for revealing the global connections of the brain typically start by tracing a small number of neurons at a time. It is through performing many experiments, in different brain areas, and across many brains, that information can be aggregated and consolidated to produce detailed maps of the brain's global networks and architecture. The aim of this project is to develop new computational approaches for modeling the connectivity of the mouse brain, in order to reveal principles of wiring and information routing. The project will leverage whole-brain imaging datasets from the Allen Institute for Brain Science that each provide a small piece of the puzzle but when combined, can yield a picture of whole-brain connectivity. The outcomes of this research will be new maps of the global connectivity of the mouse brain, and a framework for studying the impact of disease and aging on whole-brain networks.This project will develop a novel framework for analyzing whole-brain connectomics datasets to model high-level (mesoscopic) principles of wiring and architecture. To do this, tools from matrix factorization will be used to decompose large datasets from many brains into a collection of learned neural pathways or "parts" that, when combined, describe large volumes of data as succinctly as possible. To address the size of the datasets, the use of subsampling-based approaches and randomized methods will be explored for massive-scale machine learning applications. Through intelligent and adaptive subsampling of the data, in combination with online methods for factorization, methods will be developed to process and learn from the entire Allen Institute Mouse Connectivity Atlas at less than 10-micron resolution. The outcomes of this project will be new tools for large-scale matrix factorization, models of the whole brain connectome, and discovery of wiring principles that could be useful in the development of next generation architectures for machine intelligence.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.

揭示大脑全局连接的方法通常从一次追踪少量神经元开始。通过在不同的大脑区域和许多大脑中进行许多实验，可以将信息聚合和整合，以产生大脑全球网络和架构的详细地图。该项目的目的是开发新的计算方法来模拟小鼠大脑的连接性，以揭示布线和信息路由的原理。该项目将利用来自艾伦脑科学研究所的全脑成像数据集，每个数据集都提供了一小块拼图，但当结合起来时，可以产生全脑连接的图片。该研究的成果将是小鼠大脑全球连接的新地图，以及研究疾病和衰老对全脑网络影响的框架。该项目将开发一个新的框架，用于分析全脑连接组学数据集，以模拟高级（介观）布线和架构原理。为了做到这一点，来自矩阵分解的工具将被用来将来自许多大脑的大型数据集分解成一组学习的神经通路或“部分”，当这些神经通路或“部分”组合在一起时，将尽可能简洁地描述大量数据。为了解决数据集的大小问题，将探索使用基于子采样的方法和随机方法来进行大规模机器学习应用。通过对数据进行智能和自适应的二次采样，结合在线因子分解方法，将开发出以低于10微米的分辨率处理和学习整个艾伦研究所小鼠连接图谱的方法。该项目的成果将是大规模矩阵分解的新工具、全脑连接体的模型，以及发现可能有助于开发下一代机器智能架构的布线原理。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Modeling Variability in Brain Architecture with Deep Feature Learning

通过深度特征学习对大脑结构的变异性进行建模

• DOI: 10.1109/ieeeconf44664.2019.9048805
• 发表时间: 2019
• 期刊: and Computers
• 作者: Balwani, Aishwarya H.;Dyer, Eva L.
• 通讯作者: Dyer, Eva L.

Hierarchical Optimal Transport for Multimodal Distribution Alignment

• 发表时间: 2019-06
• 期刊: ArXiv
• 作者: John Lee;M. Dabagia;Eva L. Dyer;C. Rozell

A Generative Modeling Approach for Interpreting Population-Level Variability in Brain Structure

解释大脑结构群体水平变异性的生成模型方法

• DOI: 10.1007/978-3-030-59722-1_25
• 发表时间: 2020
• 期刊: Medical Image Computing and Computer Assisted Intervention
• 作者: Liu, Ran;Subakan, Cem;Balwani, Aishwarya H.;Whitesell, Jennifer;Harris, Julie;Koyejo, Sanmi;Dyer, Eva L.
• 通讯作者: Dyer, Eva L.

A three-dimensional thalamocortical dataset for characterizing brain heterogeneity.

• DOI: 10.1038/s41597-020-00692-y
• 发表时间: 2020-10-20
• 期刊: Scientific data
• 影响因子: 9.8
• 作者: Prasad JA;Balwani AH;Johnson EC;Miano JD;Sampathkumar V;De Andrade V;Fezzaa K;Du M;Vescovi R;Jacobsen C;Kording KP;Gürsoy D;Gray Roncal W;Kasthuri N;Dyer EL
• 通讯作者: Dyer EL

Brain mapping at high resolutions: Challenges and opportunities

高分辨率大脑图谱：挑战与机遇

• DOI: 10.1016/j.cobme.2019.10.009
• 发表时间: 2019
• 期刊: Current Opinion in Biomedical Engineering
• 影响因子: 3.9
• 作者: Milligan, Kyle;Balwani, Aishwarya;Dyer, Eva
• 通讯作者: Dyer, Eva