Xu Enze, Zhang Jingwen, Li Jiadi, Song Qianqian, Yang Defu, Wu Guorong, Chen Minghan
Department of Computer Science, Wake Forest University, Winston-Salem, North Carolina, USA.
Department of Psychology, Wake Forest University, Winston-Salem, North Carolina, USA.
Med Phys. 2024 Feb;51(2):1190-1202. doi: 10.1002/mp.16655. Epub 2023 Jul 31.
Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by three neurobiological factors beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles.
Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms.
We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations.
Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome.
The proposed PSSN (i) reduces neuroimage data to low-dimensional feature vectors, (ii) combines AT[N]-Net based on real pathological pathways, (iii) predicts long-term biomarker trajectories, and (iv) stratifies subjects into fine-grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.
阿尔茨海默病（AD）是一种异质性、多因素的神经退行性疾病，其特征在于三种神经生物学因素：β-淀粉样蛋白、病理性tau蛋白和神经退行性变。AD晚期尚无有效治疗方法，因此迫切需要早期检测和预防。然而，AD亚型识别的神经影像学研究中现有的统计推断方法未考虑病理领域知识，这可能导致不适定的结果，有时与基本神经学原理不一致。
本研究将系统生物学建模与机器学习相结合，旨在根据基本生物学原理、神经学模式和认知症状进行亚群分类，以辅助临床AD预后评估。
我们提出了一种新颖的病理导向分层网络（PSSN），该网络通过反应扩散模型纳入AD病理中已确立的领域知识，其中我们考虑了主要生物标志物之间的非线性相互作用以及沿脑结构网络的扩散。该生物模型在纵向多模态神经影像学数据上进行训练，预测长期演变轨迹，捕捉个体特征进展模式，填补可用稀疏成像数据之间的空白。然后构建一个深度预测神经网络，以利用时空动态，将神经学检查与临床特征联系起来，并在个体基础上生成亚型分配概率。我们进一步通过广泛的模拟确定了一个进化疾病图，以量化亚型转换概率。
我们的分层在各种临床评分的簇间异质性和簇内同质性方面均表现出卓越性能。将我们的方法应用于老年人群的富集样本，我们识别出跨越AD谱系的六个亚型，其中每个亚型都表现出与其临床结果一致的独特生物标志物模式。
所提出的PSSN（i）将神经影像数据简化为低维特征向量，（ii）基于真实病理途径结合AT[N]-Net，（iii）预测长期生物标志物轨迹，以及（iv）将受试者分层为具有不同神经学基础的细粒度亚型。PSSN为症状前诊断提供了见解，并为临床治疗提供了实用指导，这可能会进一步推广到其他神经退行性疾病。
