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Functional Brain Network Estimation with Human-Guided Modularity Representation

机译：具有人为模块化表示的功能性脑网络估计

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摘要

Functional brain network (FBN) has been demonstrated with remarkable advancements in understanding the human brain organization architectures and diagnosis disorders. Thus, it is crucial to accurately estimate both biologically meaningful and discriminative FBNs. Although several FBN estimation approaches have been proposed, the accurate estimation of FBN is still an open field due to the high complexity of human brains and the poor quality of the observed data. Moreover, most existing works fail in incorporating domain expert knowledge. In this paper, we stress the importance of both modular topology prior and domain expert knowledge for FBN estimation, and a human-guided modular representation (MR) FBN estimation framework is proposed. Specifically, we depict the intra- and intermodular structures of FBNs under domain expert knowledge guidance and characterize them with an adversarial low-rank constraint. An efficient ConCave-Convex Procedure (CCCP) is applied to estimate FBN, which is then verified on the Chronic Tinnitus Identification task. The proposed methods achieves a 92.11% classification accuracy, significantly outperformed the state-of-the-art methods. Our method also tends to provide more biologically meaningful functional connections, which benefit for both basic and clinical neuroscience studies.
机译：在理解人脑组织架构和诊断障碍方面，已经证明了功能性大脑网络（FBN）。因此，至关重要，可以准确估计生物学意义和辨别性FBN。尽管已经提出了几种FBN估计方法，但由于人体大脑的高复杂性和观察数据的质量差，因此FBN的准确估计仍然是开放场。此外，大多数现有工程都在纳入域专家知识中失败。在本文中，我们强调了模块化拓扑以前和域专家知识对FBN估计的重要性，提出了人类引导的模块化表示（MR）FBN估计框架。具体而言，我们描绘了域专家知识指导下FBN的内部和谐结构，并用对抗的低级约束表征它们。应用有效的凹凸过程（CCCP）来估计FBN，然后在慢性耳鸣识别任务上验证。所提出的方法实现了92.11％的分类准确性，显着优于最先进的方法。我们的方法还倾向于提供更具生物学上有意义的功能连接，这有利于基础和临床神经科学研究。

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