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Krakencoder:一种统一的脑连接组翻译与融合工具。

Krakencoder: a unified brain connectome translation and fusion tool.

作者信息

Jamison Keith W, Gu Zijin, Wang Qinxin, Tozlu Ceren, Sabuncu Mert R, Kuceyeski Amy

机构信息

Department of Computational Biology, Cornell University, Ithaca, NY, USA.

Department of Radiology, Weill Cornell Medicine, New York, NY, USA.

出版信息

Nat Methods. 2025 Jun 5. doi: 10.1038/s41592-025-02706-2.

Abstract

Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here, we present the Krakencoder, a joint connectome mapping tool that simultaneously bidirectionally translates between structural and functional connectivity, and between different atlases and processing choices via a common latent representation. These mappings demonstrate exceptional accuracy and individual-level identifiability; the mapping between structural and functional connectivity has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This fusion representation better reflects familial relatedness, preserves age- and sex-relevant information, and enhances cognition-relevant information. The Krakencoder can be applied, without retraining, to new out-of-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a notable leap forward in capturing the relationship between multimodal brain connectomes in an individualized, behaviorally and demographically relevant way.

摘要

脑连接性可以通过多种方式进行估计,这取决于模态和处理策略。在此,我们展示了Krakencoder,这是一种联合连接组映射工具,它通过一个共同的潜在表示,在结构和功能连接性之间,以及在不同图谱和处理选择之间同时进行双向转换。这些映射展示了卓越的准确性和个体水平的可识别性;结构和功能连接性之间的映射比现有模型的可识别性高出42%-54%。Krakencoder通过一个共享的低维潜在空间来整合所有连接组类型。这种融合表示能更好地反映家族相关性,保留与年龄和性别相关的信息,并增强与认知相关的信息。Krakencoder无需重新训练,就可以应用于新的分布外数据,同时仍能保留连接组预测中的个体差异以及潜在表示中的家族关系。Krakencoder在以个性化、行为和人口统计学相关的方式捕捉多模态脑连接组之间的关系方面向前迈出了显著的一步。

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