标题： 量化人脑的可压缩性 显示英文标题

标题： Quantifying the compressibility of the human brain

摘要： 在人类大脑中，活动的允许模式受到脑区之间相关性的约束。然而，尚不清楚需要哪些相关性以及需要多少相关性才能预测大尺度神经活动。在这里，我们提出了一种信息论框架，以识别最重要的相关性，这些相关性能提供最准确的神经状态预测。将我们的框架应用于人类皮层活动，我们发现活动中的大部分方差是由少量相关性解释的。这意味着大脑具有高度可压缩性：只需一个稀疏的相关性网络即可预测大尺度活动。我们发现这种可压缩性在不同个体和认知任务中表现出显著的一致性，并且出人意料的是，最重要的相关性不一定是最强的相关性。综上所述，这些结果表明几乎不需要所有相关性来预测神经活动，而我们提供了揭示关键相关性的工具。 显示英文摘要

摘要： In the human brain, the allowed patterns of activity are constrained by the correlations between brain regions. Yet it remains unclear which correlations -- and how many -- are needed to predict large-scale neural activity. Here, we present an information-theoretic framework to identify the most important correlations, which provide the most accurate predictions of neural states. Applying our framework to cortical activity in humans, we discover that the vast majority of variance in activity is explained by a small number of correlations. This means that the brain is highly compressible: only a sparse network of correlations is needed to predict large-scale activity. We find that this compressibility is strikingly consistent across different individuals and cognitive tasks, and that, counterintuitively, the most important correlations are not necessarily the strongest. Together, these results suggest that nearly all correlations are not needed to predict neural activity, and we provide the tools to uncover the key correlations that are.