标题： 面向脑数据的预测时空表示 显示英文标题

标题： Towards a predictive spatio-temporal representation of brain data

摘要： 将大脑表征为“连接组”，其中连接通过时间序列之间的相关性值以及从图论分析中得出的总结性度量来表示，在过去几年中非常流行。 然而，尽管这种表示方法已经促进了我们对大脑功能的理解，但它可能是一个过于简化的模型。 这是因为典型的fMRI数据集由复杂且高度异质的时间序列组成，这些时间序列在空间上（即大脑区域的位置）存在变化。 我们比较了深度学习和几何深度学习的各种建模技术，以开创未来研究的有效方法，充分利用典型fMRI数据集以及其他类似数据集中的丰富空间和时间领域。 作为概念验证，我们在监督二分类任务中，将我们的方法与同质且公开可用的人类连接组计划（HCP）数据集进行比较。 我们希望，相对于之前的“连接组”度量，我们的方法论进展最终能够在临床和计算方面具有相关性，从而更细致地理解健康和疾病状态下的大脑动态。 对大脑的这种理解可以从根本上减少对专门临床专业知识的依赖，以准确理解大脑的变异性。 显示英文摘要

摘要： The characterisation of the brain as a "connectome", in which the connections are represented by correlational values across timeseries and as summary measures derived from graph theory analyses, has been very popular in the last years. However, although this representation has advanced our understanding of the brain function, it may represent an oversimplified model. This is because the typical fMRI datasets are constituted by complex and highly heterogeneous timeseries that vary across space (i.e., location of brain regions). We compare various modelling techniques from deep learning and geometric deep learning to pave the way for future research in effectively leveraging the rich spatial and temporal domains of typical fMRI datasets, as well as of other similar datasets. As a proof-of-concept, we compare our approaches in the homogeneous and publicly available Human Connectome Project (HCP) dataset on a supervised binary classification task. We hope that our methodological advances relative to previous "connectomic" measures can ultimately be clinically and computationally relevant by leading to a more nuanced understanding of the brain dynamics in health and disease. Such understanding of the brain can fundamentally reduce the constant specialised clinical expertise in order to accurately understand brain variability.