BrainNetGAN：使用生成对抗网络进行痴呆分类的大脑连接数据增强
BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification
摘要：
阿尔茨海默病（AD）是最常见的年龄相关性痴呆。它仍然是一个挑战，以确定个人在痴呆症的风险进行精确的管理。脑MRI提供了一种非侵入性的生物标志物来检测脑老化。先前的证据表明，弥散MRI检测到的脑结构改变与痴呆有关。越来越多的研究将大脑概念化为一个复杂的网络，这表明这种方法在表征各种神经和精神疾病方面的实用性。因此，结构连接性在痴呆分类中显示出希望。拟议的BrainNetGAN是一个生成对抗网络变体，用于增强大脑结构连接矩阵以进行二进制痴呆分类任务。分离的大脑区域之间的结构连接矩阵构建使用纤维束成像扩散MRI数据。BrainNetGAN模型经过训练，生成假的大脑连接矩阵，这些矩阵预计将反映真实大脑网络数据的潜在分布。最后，提出了一个卷积神经网络分类器用于二值痴呆分类。数值结果表明，使用BrainNetGAN增强数据集，测试集中的二进制分类性能得到了改善。所提出的方法允许快速合成任意数量的增广连接矩阵，可以很容易地转移到类似的分类任务。
英文摘要：
Alzheimer's disease (AD) is the most common age-related dementia. It remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a noninvasive biomarker to detect brain aging. Previous evidence shows that the brain structural change detected by diffusion MRI is associated with dementia. Mounting studies has conceptualised the brain as a complex network, which has shown the utility of this approach in characterising various neurological and psychiatric disorders. Therefore, the structural connectivity shows promise in dementia classification. The proposed BrainNetGAN is a generative adversarial network variant to augment the brain structural connectivity matrices for binary dementia classification tasks. Structural connectivity matrices between separated brain regions are constructed using tractography on diffusion MRI data. The BrainNetGAN model is trained to generate fake brain connectivity matrices, which are expected to reflect latent distribution of the real brain network data. Finally, a convolutional neural network classifier is proposed for binary dementia classification. Numerical results show that the binary classification performance in the testing set was improved using the BrainNetGAN augmented dataset. The proposed methodology allows quick synthesis of an arbitrary number of augmented connectivity matrices and can be easily transferred to similar classification tasks.
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