Novel Continuous Structural and Functional Networks and Prediction of Individual Cognition

近年来，人类大脑网络受到了极大的关注，因为它们描述了与认知、神经精神和遗传学相关的大脑结构和功能连接的全面地图。现有的网络分析方法将大脑划分为几百个区域。然后将功能或结构信息覆盖在包裹的顶部以供进一步分析。然而，几百个区域的分割不能完全表征个体之间大脑解剖和功能的潜在差异。该项目将通过开发计算效率高的数学模型来构建连续的大脑网络来应对这一挑战。我们将展示新模型的各种用途，包括个人认知能力的预测。该项目将在网络模型、深度学习和伴随的代码中产生新的算法，并处理数据，这些数据将作为开发更先进方法的测试平台。该项目的影响超出了预期的应用，并将支持其他领域的更先进的方法。该项目有很大的潜力重塑网络如何构建和分析的研究。我们期望连续的脑网络表征个体大脑的基本性质，并提高对认知能力个体差异的预测能力。该项目还将提供一个通用的开源算法工具箱，用于连续地对大规模功能和结构脑网络进行建模和可视化。使用现有的脑分区来构建和分析脑网络模型的研究人员面临着以下几个挑战：1）使用预定的分区来理解多个空间尺度上的脑组织的固有局限性; 2）在选择分组时网络拓扑结构冲突; 3）对多模态集成的敏感性降低。这些已经被提出作为基于连接组的个体认知能力预测的主要挑战。如果脑区的边界与数据不匹配，则预测模型可能无法最佳地执行。此外，大脑包裹的具体选择可能会使预测结果产生偏差。考虑到这些限制，该项目的主要目标是开发计算效率高的数学模型，用于在不使用现有大脑包裹的情况下构建连续的功能和结构大脑网络。使用这些新的网络结构，我们将开发新的计算效率高的深度学习方法，这些方法将所提出的网络几何结构结合起来，并预测个人的认知能力，而不依赖于预定义的分组。我们将展示使用连续网络来理解多尺度水平的大脑组织，并预测个体认知能力，如智力，工作记忆，注意力和认知控制。该奖项由CISE信息和智能系统（IIS）通过CRCNA和BRAIN计划共同资助，MPS Division of Mathematical Sciences（DMS）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

In recent years, human brain networks have received great attention since they describe comprehensive maps of structural and functional connections in the brain in relation to cognition, neuropsychiatric and genetics. Existing methods for network analysis partition the brain into a few hundred regions. Functional or structural information is then overlaid on top of the parcellation for further analysis. However, the parcellation of a few hundred regions cannot fully characterize potential differences in the brain anatomy and function among individuals. This project will tackle the challenge by developing computationally efficient mathematical models for building continuous brain networks. We will demonstrate the various uses of the new models including the prediction of individual cognitive abilities. The project will produce new algorithms in network models, deep learning and accompanying codes and processed data that will serve as a testbed for the development of more advanced methods. The impact of the project goes beyond the intended applications and will support more advanced methods in other areas. The project has great potential to reshape the research on how networks are constructed and analyzed. We expect that the continuous brain networks characterize the fundamental nature of individual brains and improve the predictive power to individual differences in terms of cognitive abilities. The project will also provide versatile an open-source toolbox of algorithms for modeling and visualizing large-scale functional and structural brain networks continuously.Researchers who use existing brain parcellations for building and analyzing brain network models face several challenges: 1) the inherent limitations of using predetermined parcellations for understanding brain organizations in multiple spatial scales; 2) conflicting network topology over the choice of parcellation; 3) decreased sensitivity over multimodal integration. These have been raised as major challenges for the connectome-based prediction of individual cognitive abilities. The prediction models may not perform optimally if the boundary of the brain parcels does not fit the data well. Further, the specific choice of brain parcellations may bias prediction outcomes. Given these limitations, the main goal of the project is to develop computationally efficient mathematical models for building continuous functional and structural brain networks without using existing brain parcellations. Using these novel network constructions, we will develop new computationally efficient deep learning approaches that incorporate the proposed network geometry and predict individual cognitive abilities without relying on predefined parcellations. We will demonstrate the use of the continuous networks to understand brain organizations in multiscale levels and predict individual cognitive abilities such as intelligence, working memory, attention and cognitive controls.This award is being co-funded by the CISE Information and Intelligent Systems (IIS) through the CRCNA and BRAIN Programs, and the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program.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.