Department of Electrical and Computer Engineering, NUS Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore.
School of Medicine, Stanford University, Stanford, CA, USA.
Neuroimage. 2019 Nov 1;201:116043. doi: 10.1016/j.neuroimage.2019.116043. Epub 2019 Jul 22.
Individuals with Alzheimer's disease (AD) dementia exhibit significant heterogeneity across clinical symptoms, atrophy patterns, and spatial distribution of Tau deposition. Most previous studies of AD heterogeneity have focused on atypical clinical subtypes, defined subtypes with a single modality, or restricted their analyses to a priori brain regions and cognitive tests. Here, we considered a data-driven hierarchical Bayesian model to identify latent factors from atrophy patterns and cognitive deficits simultaneously, thus exploiting the rich dimensionality within each modality. Unlike most previous studies, our model allows each factor to be expressed to varying degrees within an individual, in order to reflect potential multiple co-existing pathologies. By applying our model to ADNI-GO/2 AD dementia participants, we found three atrophy-cognitive factors. The first factor was associated with medial temporal lobe atrophy, episodic memory deficits and disorientation to time/place ("MTL-Memory"). The second factor was associated with lateral temporal atrophy and language deficits ("Lateral Temporal-Language"). The third factor was associated with atrophy in posterior bilateral cortex, and visuospatial executive function deficits ("Posterior Cortical-Executive"). While the MTL-Memory and Posterior Cortical-Executive factors were discussed in previous literature, the Lateral Temporal-Language factor is novel and emerged only by considering atrophy and cognition jointly. Several analyses were performed to ensure generalizability, replicability and stability of the estimated factors. First, the factors generalized to new participants within a 10-fold cross-validation of ADNI-GO/2 AD dementia participants. Second, the factors were replicated in an independent ADNI-1 AD dementia cohort. Third, factor loadings of ADNI-GO/2 AD dementia participants were longitudinally stable, suggesting that these factors capture heterogeneity across patients, rather than longitudinal disease progression. Fourth, the model outperformed canonical correlation analysis at capturing associations between atrophy patterns and cognitive deficits. To explore the influence of the factors early in the disease process, factor loadings were estimated in ADNI-GO/2 mild cognitively impaired (MCI) participants. Although the associations between the atrophy patterns and cognitive profiles were weak in MCI compared to AD, we found that factor loadings were associated with inter-individual regional variation in Tau uptake. Taken together, these results suggest that distinct atrophy-cognitive patterns exist in typical Alzheimer's disease, and are associated with distinct patterns of Tau depositions before clinical dementia emerges.
患有阿尔茨海默病（AD）痴呆的个体在临床症状、萎缩模式和 Tau 沉积的空间分布方面表现出显著的异质性。大多数关于 AD 异质性的先前研究都集中在非典型临床亚型上，使用单一模态定义亚型，或限制对预先确定的大脑区域和认知测试的分析。在这里，我们考虑了一种基于数据的分层贝叶斯模型，以同时从萎缩模式和认知缺陷中识别潜在的因素，从而利用每个模态的丰富维度。与大多数先前的研究不同，我们的模型允许每个因素在个体中以不同程度表达，以反映潜在的多种共存病理学。通过将我们的模型应用于 ADNI-GO/2 AD 痴呆参与者，我们发现了三个萎缩-认知因素。第一个因素与内侧颞叶萎缩、情景记忆缺陷和时间/地点定向障碍有关（“MTL-Memory”）。第二个因素与外侧颞叶萎缩和语言缺陷有关（“外侧颞叶-语言”）。第三个因素与双侧后部皮质萎缩以及视空间执行功能缺陷有关（“后部皮质-执行”）。虽然 MTL-Memory 和 Posterior Cortical-Executive 因素在前人文献中有所讨论，但 Lateral Temporal-Language 因素是新颖的，仅通过同时考虑萎缩和认知而出现。进行了多项分析以确保估计因素的普遍性、可复制性和稳定性。首先，在对 ADNI-GO/2 AD 痴呆参与者进行 10 倍交叉验证的情况下，这些因素适用于新参与者。其次，这些因素在独立的 ADNI-1 AD 痴呆队列中得到了复制。第三，ADNI-GO/2 AD 痴呆参与者的因子负荷在纵向是稳定的，这表明这些因素捕获了患者之间的异质性，而不是纵向疾病进展。第四，该模型在捕捉萎缩模式和认知缺陷之间的关联方面优于典型相关分析。为了探索这些因素在疾病早期过程中的影响，在 ADNI-GO/2 轻度认知障碍（MCI）参与者中估计了因子负荷。尽管与 AD 相比，MCI 中萎缩模式和认知特征之间的关联较弱，但我们发现因子负荷与 Tau 摄取的个体间区域变化有关。总的来说，这些结果表明，在典型的阿尔茨海默病中存在不同的萎缩-认知模式，并且在临床痴呆出现之前与 Tau 沉积的不同模式相关。
