A thorough understanding of sex differences that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit phenotypic differences between males and females. Here we evaluate sex differences in regional temporal dependence of resting‐state brain activity in 195 adult male–female pairs strictly matched for total grey matter volume from the Human Connectome Project. We find that males have more persistent temporal dependence in regions within temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional temporal dependence measures achieve sex classification accuracies up to 81%. Regions with the strongest feature importance in the sex classification task included cerebellum, amygdala, and frontal and occipital cortices. Secondarily, we show that even after strict matching of total gray matter volume, significant volumetric sex differences persist; males have larger absolute cerebella, hippocampi, parahippocampi, thalami, caudates, and amygdalae while females have larger absolute cingulates, precunei, and frontal and parietal cortices. Sex classification based on regional volume achieves accuracies up to 85%, highlighting the importance of strict volume‐matching when studying brain‐based sex differences. Differential patterns in regional temporal dependence between the sexes identifies a potential neurobiological substrate or environmental effect underlying sex differences in functional brain activation patterns.

中文翻译：

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使用静息态功能 MRI 时间序列的长期时间依赖性进行性别分类。

彻底了解健康个体大脑中存在的性别差异对于研究男性和女性之间表现出表型差异的神经系统疾病至关重要。在这里，我们评估了 195 对成年男女静息态大脑活动的区域时间依赖性的性别差异，这些配对与人类连接组计划的总灰质体积严格匹配。我们发现男性在颞叶、顶叶和枕叶皮质内的区域具有更持久的时间依赖性。经过区域时间依赖性测量训练的机器学习算法可实现高达 81% 的性别分类准确率。在性别分类任务中特征重要性最强的区域包括小脑、杏仁核、额叶和枕叶皮质。其次，我们表明，即使在总灰质体积严格匹配之后，显着的体积性别差异仍然存在；男性有较大的绝对小脑、海马、海马旁、丘脑、尾状核和杏仁核，而女性有较大的绝对扣带回、楔前叶、额叶和顶叶皮质。基于区域体积的性别分类准确率高达 85%，凸显了在研究基于大脑的性别差异时严格体积匹配的重要性。性别之间区域时间依赖性的差异模式识别出功能性大脑激活模式中性别差异的潜在神经生物学底物或环境影响。