School of Psychology, The University of Queensland, Saint Lucia, Brisbane, QLD 4072, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.
Neuroimage. 2021 Dec 1;244:118624. doi: 10.1016/j.neuroimage.2021.118624. Epub 2021 Oct 1.
Whether it be in a single neuron or a more complex biological system like the human brain, form and function are often directly related. The functional organization of human visual cortex, for instance, is tightly coupled with the underlying anatomy with cortical shape having been shown to be a useful predictor of the retinotopic organization in early visual cortex. Although the current state-of-the-art in predicting retinotopic maps is able to account for gross individual differences, such models are unable to account for any idiosyncratic differences in the structure-function relationship from anatomical information alone due to their initial assumption of a template. Here we developed a geometric deep learning model capable of exploiting the actual structure of the cortex to learn the complex relationship between brain function and anatomy in human visual cortex such that more realistic and idiosyncratic maps could be predicted. We show that our neural network was not only able to predict the functional organization throughout the visual cortical hierarchy, but that it was also able to predict nuanced variations across individuals. Although we demonstrate its utility for modeling the relationship between structure and function in human visual cortex, our approach is flexible and well-suited for a range of other applications involving data structured in non-Euclidean spaces.
无论是在单个神经元还是更复杂的生物系统（如人类大脑）中，形态和功能通常都是直接相关的。例如，人类视觉皮层的功能组织与底层解剖结构紧密结合，皮层形状已被证明是早期视觉皮层视网膜组织的有用预测指标。尽管目前预测视网膜图的最先进技术能够解释总体的个体差异，但由于这些模型最初基于模板的假设，它们无法仅从解剖学信息来解释结构-功能关系中的任何特殊差异。在这里，我们开发了一种几何深度学习模型，能够利用皮层的实际结构来学习人类视觉皮层中大脑功能和解剖结构之间的复杂关系，从而可以预测更真实和特殊的图谱。我们表明，我们的神经网络不仅能够预测整个视觉皮层层次结构中的功能组织，而且还能够预测个体之间的细微变化。尽管我们证明了它在模拟人类视觉皮层中结构和功能关系方面的实用性，但我们的方法灵活且非常适合涉及非欧几里得空间数据的各种其他应用。
