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基于语言网络功能连接模式的性别分类:休息状态fMRI研究

X Lajoie^1,2, C DeRoy^2,3, C Bedetti^1,2

¹Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada.

Human brain mapping

|January 10, 2026

概括

性差异存在于大脑的语言网络功能连接中. 机器学习精确地根据休息状态模式识别了性别,揭示了男性和女性的独特连接性.

科学领域:

神经科学是一个神经科学.
认知神经科学认知神经科学
性差异研究研究研究研究

背景情况:

了解大脑发育和衰老中的性别差异对于神经和精神疾病至关重要.
很少有研究探讨了功能性大脑网络,特别是语言网络的基于性别的变化.
以前的研究经常使用基于任务的fMRI,可能缺少微妙的静止状态功能差异.

研究的目的:

调查语言网络的静止状态功能连接中的与性别相关的差异.
应用机器学习来根据语言网络功能连接模式对性别进行分类.
确定在语言网络中区分性别的特定功能连接特征.

主要方法:

利用机器学习分类器来分析静态功能连接数据.
包括270名男性和288名女性 (22-36岁) 作为参与者.
在语言网络的功能连接中确定了关键的区分特征.

主要成果:

在基于语言网络连接的基础上对参与者的性别进行分类,获得了91.3%的准确性.
关键的区分特征涉及左下额头,左侧平面和左前中间.
与女性相比,男性在这些区域表现出更强的功能连接,尽管有些重叠.

结论:

与性别相关的差异存在于语言网络的静止状态功能连接中.
这些发现强调了在大脑和语言研究中将性别视为生物变量的重要性.
独特的连接模式突出了语言处理的潜在的性别特异的神经支.

关键词:

语言网络语言网络机器学习是机器学习.静止状态的功能连接性性别分类性别分类

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