While the function of most biological systems is tightly constrained by their structure, current evidence suggests that coupling between the structure and function of brain networks is relatively modest. We aimed to investigate whether the modest coupling between connectome structure and function is a fundamental property of nervous systems or a limitation of current brain network models. We developed a new deep learning framework to predict an individual's brain function from their structural connectome, achieving prediction accuracies that substantially exceeded state-of-the-art biophysical models (group: R=0.9±0.1=0.9±0.1, individual: R=0.55±0.1). Crucially, brain function predicted from an individual's structural connectome explained significant inter-individual variation in cognitive performance. Our results suggest that structure-function coupling in human brain networks is substantially tighter than previously suggested. We establish the margin by which current brain network models can be improved and demonstrate how deep learning can facilitate investigation of fundamental relations between brain function and behavior.

中文翻译：

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人体连接组中的结构-功能耦合：一种机器学习方法

虽然大多数生物系统的功能受到其结构的严格限制，但目前的证据表明，大脑网络的结构和功能之间的耦合相对较小。我们旨在研究连接组结构和功能之间的适度耦合是神经系统的基本特性还是当前大脑网络模型的局限性。我们开发了一个新的深度学习框架，从结构连接组预测个体的大脑功能，实现的预测精度大大超过了最先进的生物物理模型（组：R=0.9±0.1=0.9±0.1，个体：R= 0.55±0.1)。至关重要的是，从个体的结构连接组预测的大脑功能解释了认知表现的显着个体间差异。我们的研究结果表明，人脑网络中的结构-功能耦合比以前建议的要紧密得多。我们建立了可以改进当前大脑网络模型的边际，并展示了深度学习如何促进对大脑功能和行为之间的基本关系的研究。