Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Clin Neurol Neurosurg. 2024 Mar;238:108177. doi: 10.1016/j.clineuro.2024.108177. Epub 2024 Feb 15.
The importance of early treatment for mild cognitive impairment (MCI) has been extensively shown. However, classifying patients presenting with memory complaints in clinical practice as having MCI vs normal results is difficult. Herein, we assessed the feasibility of applying a machine learning approach based on structural volumes and functional connectomic profiles to classify the cognitive levels of cognitively unimpaired (CU) and amnestic MCI (aMCI) groups. We further applied the same method to distinguish aMCI patients with a single memory impairment from those with multiple memory impairments.
Fifty patients with aMCI were enrolled and classified as having either verbal or visual-aMCI (verbal or visual memory impairment), or both aMCI (verbal and visual memory impairments) based on memory test results. In addition, 26 CU patients were enrolled in the control group. All patients underwent structural T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. We obtained structural volumes and functional connectomic profiles from structural and functional MRI, respectively, using graph theory. A support vector machine (SVM) algorithm was employed, and k-fold cross-validation was performed to discriminate between groups.
The SVM classifier based on structural volumes revealed an accuracy of 88.9% at classifying the cognitive levels of patients with CU and aMCI. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 92.9%. In the classification of verbal or visual-aMCI (n = 22) versus both aMCI (n = 28), the SVM classifier based on structural volumes revealed a low accuracy of 36.7%. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 53.1%.
Structural volumes and functional connectomic profiles obtained using a machine learning approach can be used to classify cognitive levels to distinguish between aMCI and CU patients. In addition, combining the functional connectomic profiles with structural volumes results in a better classification performance than the use of structural volumes alone for identifying both "aMCI versus CU" and "verbal- or visual-aMCI versus both aMCI" patients.
早期治疗轻度认知障碍（MCI）的重要性已得到广泛证实。然而，在临床实践中，将出现记忆主诉的患者归类为 MCI 或正常结果是困难的。在此，我们评估了一种基于结构体积和功能连接组学特征的机器学习方法来分类认知正常（CU）和遗忘型 MCI（aMCI）组认知水平的可行性。我们还应用相同的方法来区分仅有单一记忆障碍的 aMCI 患者与存在多种记忆障碍的 aMCI 患者。
纳入 50 例 aMCI 患者，并根据记忆测试结果将其分为言语或视觉 aMCI（言语或视觉记忆障碍）或两者均有 aMCI（言语和视觉记忆障碍）。此外，纳入 26 例 CU 患者作为对照组。所有患者均行结构 T1 加权磁共振成像（MRI）和静息态功能 MRI。我们分别使用图论从结构和功能 MRI 中获得结构体积和功能连接组学特征。采用支持向量机（SVM）算法，通过 k 折交叉验证进行组间判别。
基于结构体积的 SVM 分类器在区分 CU 和 aMCI 患者的认知水平方面的准确率为 88.9%。然而，当结合结构体积和功能连接组学特征时，准确率提高到 92.9%。在言语或视觉 aMCI（n=22）与两者均有 aMCI（n=28）的分类中，基于结构体积的 SVM 分类器的准确率较低，为 36.7%。然而，当结合结构体积和功能连接组学特征时，准确率提高到 53.1%。
基于机器学习方法获得的结构体积和功能连接组学特征可用于分类认知水平，以区分 aMCI 和 CU 患者。此外，与仅使用结构体积相比，将功能连接组学特征与结构体积相结合可提高识别“aMCI 与 CU”和“言语或视觉 aMCI 与两者均有 aMCI”患者的分类性能。
