Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089.
Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089.
Proc Natl Acad Sci U S A. 2023 Jan 10;120(2):e2214634120. doi: 10.1073/pnas.2214634120. Epub 2023 Jan 3.
The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, = 351) and Alzheimer's disease (AD, = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.
基于磁共振成像（MRI）估计的年龄（CA）与生物学脑龄之间的差距反映了个体神经解剖衰老模式与典型轨迹的偏离程度。基于 MRI 的脑龄（BA）估计通常使用深度学习模型来获取，这些模型在新数据上的表现可能相对较差，或者缺乏神经解剖学的可解释性。本研究引入了一种卷积神经网络（CNN），该网络在对 4681 名认知正常（CN）参与者的 MRI 进行训练并对来自独立样本的 1170 名 CN 参与者进行测试后，用于估计 BA。BA 估计误差明显低于先前研究的误差。在个体和队列水平上，CNN 提供了大脑衰老模式的详细解剖图谱，揭示了轻度认知障碍（MCI， = 351）和阿尔茨海默病（AD， = 359）成年人中的性别二态和神经认知轨迹。在 MCI 个体中（其中 54%在从 MRI 采集到 10.9 年内被诊断为痴呆症），BA 在捕捉痴呆症症状严重程度、功能障碍和执行功能方面明显优于 CA。大脑衰老的性别二态和侧化模式也映射到反映认知下降的神经解剖变化模式上。BA 与神经认知测量之间的显著关联表明，所提出的框架可以系统地映射 CN 个体以及 MCI 或 AD 参与者中与衰老相关的神经解剖变化与关系。早期识别这些神经解剖变化可以帮助根据 AD 风险对个体进行筛查。
