Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging.
Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging.
复制标题
使用静息态功能磁共振成像，通过基于功能性脑连接组的机器学习分类来预测超级衰老者。
作者：
Chang;Bori R Kim;Heesoon Park;Soo Mee Lim;E. Kim;J. Jeong;G. Kim
超级老年人被定义为具有与中年人相当的年轻记忆表现的老年人。使用机器学习建模基于大脑连接体对超级老年人进行分类可以为成功衰老的生理学基础提供重要的见解。我们的目的是研究超级老年人功能性大脑连接体的独特模式，并基于机器学习方法开发预测模型来区分超级老年人和典型老年人。我们从32名超级老年人和58名典型老年人中获得了静息态功能磁共振成像（rsfMRI）数据和认知测量。包括线性支持向量机分类器（SV），随机森林分类器（RF）和逻辑回归分类器（LR）在内的三种机器学习方法在预测超级年龄方面的准确性相当（SV = 0.944，RF = 0.944，LR = 0.944）;然而，RF达到了最高的曲线下面积（AUC; 0.979）。集成学习方法结合三个分类器实现了最高的AUC（0.986）。预测老年人的最具鉴别力的节点包括楔前叶、后扣带回、岛叶皮层、额上级、中间和下级脑回，这些区域位于默认、突出和多需求网络中。因此，rsfMRI数据可以提供高精度的预测超级老人，从而捕捉和描述他们的功能性大脑连接体的独特特征。
Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology underlying successful aging. We aimed to investigate the unique patterns of functional brain connectome of superagers and develop predictive models to differentiate superagers from typical agers based on machine learning methods. We obtained resting-state functional magnetic resonance imaging (rsfMRI) data and cognitive measures from 32 superagers and 58 typical agers. The accuracies of three machine learning methods including the linear support vector machine classifier (SV), the random forest classifier (RF), and the logistic regression classifier (LR) in predicting superagers were comparable (SV = 0.944, RF = 0.944, LR = 0.944); however, RF achieved the highest area under the curve (AUC; 0.979). An ensemble learning method combining the three classifiers achieved the highest AUC (0.986). The most discriminative nodes for predicting superagers encompassed areas in the precuneus; posterior cingulate gyrus; insular cortex; and superior, middle, and inferior frontal gyrus, which were located in default, salient, and multiple-demand networks. Thus, rsfMRI data can provide high accuracy for predicting superagers, thereby capturing and describing the unique characteristics of their functional brain connectome.