Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea.
Alzheimers Res Ther. 2024 Oct 9;16(1):217. doi: 10.1186/s13195-024-01589-3.
Early detection of Alzheimer's disease (AD) is essential for timely management and consideration of therapeutic options; therefore, detecting the risk of conversion from mild cognitive impairment (MCI) to AD is crucial during neurodegenerative progression. Existing neuroimaging studies have mostly focused on group differences between individuals with MCI (or AD) and cognitively normal (CN), discarding the temporal information of conversion time. Here, we aimed to develop a prognostic model for AD conversion using functional connectivity (FC) and Cox regression suitable for conversion event modeling.
We developed a prognostic model using a large-scale Alzheimer's Disease Neuroimaging Initiative dataset, and it was validated using external data obtained from the Open Access Series of Imaging Studies. We considered individuals who were initially CN or had MCI but progressed to AD and those with MCI with no progression to AD during the five-year follow-up period. As the exact conversion time to AD is unknown, we inferred this information using imputation approaches. We generated cortex-wide principal FC gradients using manifold learning techniques and computed subcortical-weighted manifold degrees from baseline functional magnetic resonance imaging data. A penalized Cox regression model with an elastic net penalty was adopted to define a risk score predicting the risk of conversion to AD, using FC gradients and clinical factors as regressors.
Our prognostic model predicted the conversion risk and confirmed the role of imaging-derived manifolds in the conversion risk. The brain regions that largely contributed to predicting AD conversion were the heteromodal association and visual cortices, as well as the caudate and hippocampus. Our risk score based on Cox regression was consistent with the expected disease trajectories and correlated with positron emission tomography tracer uptake and symptom severity, reinforcing its clinical usefulness. Our findings were validated using an independent dataset. The cross-sectional application of our model showed a higher risk for AD than that for MCI, which correlated with symptom severity scores in the validation dataset.
We proposed a prognostic model predicting the risk of conversion to AD. The associated risk score may provide insights for early intervention in individuals at risk of AD conversion.
阿尔茨海默病（AD）的早期检测对于及时管理和考虑治疗选择至关重要；因此，在神经退行性进展过程中，检测从轻度认知障碍（MCI）向 AD 转化的风险至关重要。现有的神经影像学研究大多集中在 MCI（或 AD）和认知正常（CN）个体之间的组间差异上，忽略了转化时间的时间信息。在这里，我们旨在使用功能连接（FC）和 Cox 回归为 AD 转化开发一个适合转化事件建模的预后模型。
我们使用大规模阿尔茨海默病神经影像学倡议数据集开发了一个预后模型，并使用从开放访问成像研究系列获得的外部数据进行了验证。我们考虑了在五年随访期间最初为 CN 或患有 MCI 但进展为 AD 以及患有 MCI 但无进展为 AD 的个体。由于 AD 的确切转化时间未知，我们使用推断方法推断出这些信息。我们使用流形学习技术生成皮质全脑主要 FC 梯度，并从基线功能磁共振成像数据中计算亚皮质加权流形度。采用具有弹性网惩罚的惩罚 Cox 回归模型，使用 FC 梯度和临床因素作为回归量，定义预测向 AD 转化风险的风险评分。
我们的预后模型预测了转化风险，并证实了成像衍生流形在转化风险中的作用。对预测 AD 转化贡献最大的脑区是异模态联合和视觉皮质，以及尾状核和海马体。我们基于 Cox 回归的风险评分与预期疾病轨迹一致，并与正电子发射断层扫描示踪剂摄取和症状严重程度相关，增强了其临床实用性。我们的发现使用独立数据集进行了验证。该模型的横断面应用显示 AD 的风险高于 MCI，与验证数据集中的症状严重程度评分相关。
我们提出了一种预测向 AD 转化风险的预后模型。相关风险评分可能为有 AD 转化风险的个体提供早期干预的见解。
