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English(EN) Sex-based Network-Specific Differences in Connectomes: A Krakencoder-Based Analysis

Krakencoder 分析揭示大脑连接组中细微的性别差异

一项发表在 arXiv 上的新研究使用 Krakencoder 框架分析了大脑连接组中基于性别的差异。研究人员检查了来自人类连接组计划 702 名参与者的结构和功能连接组,评估了移除个体 Yeo-7 功能网络的影响。默认模式网络引起了最大的扰动,而体感运动网络的影响最小。虽然预测连接组中的性别特异性信息很微妙,但与单独的扰动衍生特征(66.09% 的准确率)相比,完整的预测连接组保留了显著更多的性别区分信息(准确率高达 84.76%)。 AI

排序理由 该集群包含一篇发表在 arXiv 上的研究论文,详细介绍了新颖的分析方法和研究结果。

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Krakencoder 分析揭示大脑连接组中细微的性别差异

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该集群包含一篇发表在 arXiv 上的研究论文,详细介绍了新颖的分析方法和研究结果。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Vibhashree S H, Debanjali Bhattacharya, Vamshi Krishna Kancharla, Neelam Sinha ·

    基于 Krakencoder 的连接组中基于性别的网络特异性差异分析

    arXiv:2606.16294v1 Announce Type: new Abstract: This study examines how deficiencies in one brain connectome modality propagate to the other, using the Krakencoder as a simulation framework. Structural and functional connectomes from 702 healthy participants in the Human Connecto…

  2. arXiv cs.CV TIER_1 English(EN) · Neelam Sinha ·

    基于Krakencoder的连接组中基于性别的网络特异性差异分析

    This study examines how deficiencies in one brain connectome modality propagate to the other, using the Krakencoder as a simulation framework. Structural and functional connectomes from 702 healthy participants in the Human Connectome Project were analyzed, with the impact of eac…