Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative dementia. Recent studies found that subjective cognitive decline (SCD) may be the early clinical precursor that precedes mild cognitive impairment (MCI) for AD. SCD subjects with normal cognition may already have some medial temporal lobe atrophy. Although brain changes by AD have been widely studied in the literature, it is still challenging to investigate the anatomical subtle changes in SCD. This paper proposes a machine learning framework by combination of sparse coding and random forest (RF) to identify the informative imaging biomarkers for assessment and prediction of cognitive functions and their changes in individuals with MCI, SCD and normal control (NC) using magnetic resonance imaging (MRI). First, we compute the volumes from both the regions of interest from whole brain and the subregions of hippocampus and amygdala as the features of structural MRIs. Then, sparse coding is applied to identify the relevant features. Finally, the proximity-based RF is used to combine three sets of volumetric features and establish a regression model for predicting clinical scores. Our method has double feature selections to better explore the relevant features for prediction and is evaluated with the T1-weighted structural MR images from 36 MCI, 112 SCD, 78 NC subjects. The results demonstrate the effectiveness of proposed method. In addition to hippocampus and amygdala, we also found that the fimbria, basal nucleus and cortical nucleus subregions are more important than other regions for prediction of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores and their changes.

阿尔茨海默病 (AD) 是一种进行性和不可逆的神经退行性痴呆。最近的研究发现，主观认知能力下降 (SCD) 可能是 AD 轻度认知障碍 (MCI) 之前的早期临床前兆。认知正常的 SCD 受试者可能已经有一些内侧颞叶萎缩。尽管 AD 引起的大脑变化已在文献中得到广泛研究，但研究 SCD 的解剖细微变化仍然具有挑战性。本文提出了一种机器学习框架，通过结合稀疏编码和随机森林 (RF) 来识别信息成像生物标志物，用于使用磁共振成像评估和预测 MCI、SCD 和正常对照 (NC) 个体的认知功能及其变化（核磁共振）。第一的，我们计算来自整个大脑的感兴趣区域以及海马和杏仁核子区域的体积作为结构 MRI 的特征。然后，应用稀疏编码来识别相关特征。最后，利用基于邻近度的RF结合三组体积特征，建立预测临床评分的回归模型。我们的方法具有双重特征选择，以更好地探索预测的相关特征，并使用来自 36 个 MCI、112 个 SCD、78 个 NC 受试者的 T1 加权结构 MR 图像进行评估。结果证明了所提出方法的有效性。除了海马体和杏仁核，我们还发现了菌毛，