Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer’s disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

中文翻译：

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基于深度脑网络和全球 14468 人的大脑年龄和疾病在整个生命周期中的 MRI 特征。

深度学习已成为构建正常大脑老化以及与脑部疾病相关的各种神经病理过程的成像特征的强大方法。特别是，MRI 衍生的大脑年龄已被用作大脑健康的综合生物标志物，可以通过与典型大脑衰老的偏差来识别晚期和有弹性的衰老个体。各种脑部疾病的成像特征，包括精神分裂症和阿尔茨海默病，也已使用机器学习进行了识别。由于需要复杂且不易重现的处理步骤，从中得出典型脑老化轨迹的样本动力不足或多样化，以及跨人群和 MRI 扫描仪的重现性有限，先前推导这些指数的努力受到阻碍。在此处，n = 11 729）一组来自高度多样化队列的 MRI 扫描，涵盖世界各地不同的研究、扫描仪、年龄和地理位置。使用交叉验证和 2739 人的单独复制队列进行的测试表明，DeepBrainNet 从这些不同的数据集中获得了可靠的脑龄估计，而无需专门的图像数据准备和处理。此外，我们展示的证据表明，适度拟合的脑老化模型可以提供最能区分患有病态个体的脑年龄估计值。这并不出人意料，因为紧密拟合的脑龄模型自然会产生脑龄估计值，这些估计值提供的年龄以外的信息很少，而松散拟合的模型可能包含很多噪音。我们的结果提供了一些反对普遍追求的紧拟合模型的实验证据。我们表明，与测试的四个疾病组中的两个紧密拟合模型相比，适度拟合的大脑年龄模型获得了显着更高的分化。至关重要的是，我们证明，与直接在患者与健康对照数据集上训练分类器或使用 ImageNet 等常见成像数据库相比，利用 DeepBrainNet 以及迁移学习可以让我们构建更准确的多种脑部疾病分类器。因此，我们推导出一个特定领域的深度网络，可能会减少对通用深度学习网络的特定应用程序适应和调整的需求。我们向社区免费提供 DeepBrainNet 模型，用于基于 MRI 评估普通人群和整个生命周期的大脑健康状况。