arXiv 2505.226852025-06-11eess.IVcs.AIcs.CV

DeepMultiConnectome：直接基于弥散MRI纤维束成像的结构连接组深度多任务预测

DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography

Eindhoven University of Technology（埃因霍温理工大学）；Brigham and Women’s Hospital（布莱尔妇女医院）；Harvard Medical School（哈佛医学院）；The University of Sydney（悉尼大学）；University of Electronic Science and Technology of China（电子科技大学）；Harvard-MIT Health Sciences and Technology（哈佛-麻省理工健康科学与技术）

Marcus J. Vroemen, Yuqian Chen, Yui Lo, Tengfei Xue, Weidong Cai, Fan Zhang, Josien P. W. Pluim, Lauren J. O'Donnell

备注：: 15 pages, 5 figures

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AI总结：

本文提出DeepMultiConnectome，利用点云神经网络与多任务学习直接从dMRI纤维束成像预测多种分割方案下的结构连接组，实现快速、可扩展且高保真的连接组生成。

摘要：

弥散MRI（dMRI）纤维束成像能够在体绘制大脑结构连接，但传统的连接组生成过程耗时，且需要进行灰质分割，这给大规模研究带来了挑战。我们提出DeepMultiConnectome，这是一种直接从纤维束成像预测结构连接组的深度学习模型，无需进行灰质分割，同时支持多种分割方案。该模型采用基于点云的神经网络和多任务学习，根据两种分割方案中纤维束所连接的区域对其进行分类，并共享学习到的表示。我们在Human Connectome Project Young Adult数据集（n=1000）的纤维束成像上训练并验证DeepMultiConnectome，这些数据采用84个和164个区域的灰质分割方案进行标注。DeepMultiConnectome可在约40秒内从包含300万条纤维束的全脑纤维束图中预测多个结构连接组。通过将预测连接组与使用灰质分割标记纤维束的传统方法生成的连接组进行比较，我们对DeepMultiConnectome进行了评估。预测连接组与传统生成连接组高度相关（84区域方案的r=0.992；164区域方案的r=0.986），并在很大程度上保留了网络属性。对DeepMultiConnectome的重测分析表明，其可重复性与传统生成的连接组相当。在预测年龄和认知功能方面，预测连接组的表现与传统生成连接组相似。总体而言，DeepMultiConnectome提供了一种可扩展、快速的模型，可跨多种分割方案生成受试者特异性连接组。

英文摘要：

Diffusion MRI (dMRI) tractography enables in vivo mapping of brain structural connections, but traditional connectome generation is time-consuming and requires gray matter parcellation, posing challenges for large-scale studies. We introduce DeepMultiConnectome, a deep-learning model that predicts structural connectomes directly from tractography, bypassing the need for gray matter parcellation while supporting multiple parcellation schemes. Using a point-cloud-based neural network with multi-task learning, the model classifies streamlines according to their connected regions across two parcellation schemes, sharing a learned representation. We train and validate DeepMultiConnectome on tractography from the Human Connectome Project Young Adult dataset ($n = 1000$), labeled with an 84 and 164 region gray matter parcellation scheme. DeepMultiConnectome predicts multiple structural connectomes from a whole-brain tractogram containing 3 million streamlines in approximately 40 seconds. DeepMultiConnectome is evaluated by comparing predicted connectomes with traditional connectomes generated using the conventional method of labeling streamlines using a gray matter parcellation. The predicted connectomes are highly correlated with traditionally generated connectomes ($r = 0.992$ for an 84-region scheme; $r = 0.986$ for a 164-region scheme) and largely preserve network properties. A test-retest analysis of DeepMultiConnectome demonstrates reproducibility comparable to traditionally generated connectomes. The predicted connectomes perform similarly to traditionally generated connectomes in predicting age and cognitive function. Overall, DeepMultiConnectome provides a scalable, fast model for generating subject-specific connectomes across multiple parcellation schemes.

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