Geometric deep learning on brain shape predicts sex and age
Geometric deep learning on brain shape predicts sex and age
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DOI：
10.1016/j.compmedimag.2021.101939
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发表时间：
2021-06-01
影响因子：
5.7
通讯作者：
Bandt, S. Kathleen
中科院分区：
文献类型：
--
作者：
Besson, Pierre;Parrish, Todd;Bandt, S. Kathleen
尽管对皮质折叠进行了几十年的广泛研究，但人类大脑的形状和功能之间的复杂关系仍然难以捉摸。皮质回旋的分析提供了一个机会来推进我们对这种关系的认识，并更好地理解涉及不同程度皮质折叠异常的各种病理的病因。假设驱动的基于表面的方法已被证明在准确描述皮质带折叠薄片拓扑结构的独特特征方面特别有效。然而，这些方法的实用性由于依赖于人工定义的特征而被削弱，这些特征旨在捕捉皮层折叠的相关几何特性。在本文中，我们提出了一种全新的、基于数据驱动的深度学习方法来分析大脑的形状，从而消除了对手动特征定义的依赖。该方法建立在新兴的几何深度学习领域的基础上，并使用传统的卷积神经网络架构，该架构独特地适应于皮质带的表面表示。该方法与之前的脑MRI CNN研究完全不同，之前的研究都依赖于三维MRI数据和MRI信号的解释特征进行预测。6410名健康受试者的MRI数据来自11个公开可用的数据存储库，用于分析。年龄从6岁到89岁不等。利用Freesurfer提取皮层内外表面，并在MNI空间中进行配准。为了方法开发的目的，分别为包括性别和年龄预测在内的网络学习引入了分类和回归挑战。训练了两个独立的图形卷积神经网络（gcnn），其中第一个用于预测受试者自我识别的性别，第二个用于预测受试者的年龄。分别构建类激活图（CAM）和回归激活图（RAM），绘制每个gCNN决策过程中最具影响力的脑区域的地形分布。使用这种方法，gCNN能够预测受试者的性别，平均准确率为87.99%，在预测受试者年龄时，Person相关系数为0.93，平均绝对误差为4.58年。我们相信这种基于形状的卷积分类器提供了一种新颖的、数据驱动的方法，可以在人群和单个受试者水平上定义大脑的生物医学相关特征，因此为未来的精准医学应用奠定了重要基础。
The complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about this relationship, and better understand the etiology of a variety of pathologies involving diverse degrees of cortical folding abnormalities. Hypothesis-driven surface-based approaches have been shown to be particularly efficient in their ability to accurately describe unique features of the folded sheet topology of the cortical ribbon. However, the utility of these approaches has been blunted by their reliance on manually defined features aiming to capture the relevant geometric properties of cortical folding. In this paper, we propose an entirely novel, data-driven deep-learning based method to analyze the brain's shape that eliminates this reliance on manual feature definition. This method builds on the emerging field of geometric deeplearning and uses traditional convolutional neural network architecture uniquely adapted to the surface representation of the cortical ribbon. This method is a complete departure from prior brain MRI CNN investigations, all of which have relied on three dimensional MRI data and interpreted features of the MRI signal for prediction. MRI data from 6410 healthy subjects obtained from 11 publicly available data repositories were used for analysis. Ages ranged from 6 to 89 years. Both inner and outer cortical surfaces were extracted using Freesurfer and then registered into MNI space. For purposes of method development, both a classification and regression challenge were introduced for network learning including sex and age prediction, respectively. Two independent graph convolutional neural networks (gCNNs) were trained, the first of which to predict subject's self-identified sex, the second of which to predict subject's age. Class Activation Maps (CAM) and Regression Activation Maps (RAM) were constructed respectively to map the topographic distribution of the most influential brain regions involved in the decision process for each gCNN. Using this approach, the gCNN was able to predict a subject's sex with an average accuracy of 87.99 % and achieved a Person's coefficient of correlation of 0.93 with an average absolute error 4.58 years when predicting a subject's age. We believe this shape-based convolutional classifier offers a novel, data-driven approach to define biomedically relevant features from the brain at both the population and single subject levels and therefore lays a critical foundation for future precision medicine applications.