Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy ( P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

中文翻译：

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青年交往网络功能拓扑的性别差异


先前的研究表明，大脑皮层功能网络的空间分布或功能拓扑存在很大的个体差异。然而，目前尚不清楚青少年个体化网络的拓扑结构是否存在性别差异。在这里，我们利用先进的机器学习方法（稀疏正则化非负矩阵分解）来定义 693 名青少年（8 至 23 岁）的个性化功能网络，这些青少年作为费城神经发育队列的一部分接受了功能 MRI。使用支持向量机进行多变量模式分析，根据功能地形对参与者性别进行分类，准确度为 82.9%（磷< 0.0001)。对参与者性别进行分类最有效的大脑区域属于关联网络，包括腹侧注意力、默认模式和额顶网络。使用带有惩罚样条的广义加性模型进行的大规模单变量分析提供了收敛结果。此外，来自艾伦人脑图谱的转录组数据显示，功能拓扑多变量模式的性别差异与 X 染色体上的基因表达在空间上相关。这些结果强调了性别作为塑造功能地形的生物变量的作用。