Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China.
Department of Radiology, Geriatric Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210000, China.
Behav Brain Res. 2021 Aug 6;411:113387. doi: 10.1016/j.bbr.2021.113387. Epub 2021 May 25.
To explore whether the whole brain resting-state functional connectivity (rs-FC) could predict episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment.
This study included 33 cognitive normal (CN), 26 subjective cognitive decline (SCD) and 27 amnestic mild cognitive impairment (aMCI) patients, and all the participants completed resting-state fMRI (rs-fMRI) scan and neuropsychological scale test data. Connectome-based predictive modeling (CPM) based on the rs-FC data was used to predict the auditory verbal learning test-delayed recall (AVLT-DR) scores, which measured episodic memory in individuals. Pearson correlation between each brain connection in the connectivity matrices and AVLT-DR scores was computed across the patients in predementia stages of Alzheimer's disease (AD). The Pearson correlation coefficient values separated into a positive network and a negative network. Predictive networks were then defined and employed by calculating positive and negative network strengths. CPM with leave-one-out cross-validation (LOOCV) was conducted to train linear models to respectively relate positive and negative network strengths to AVLT-DR scores in the training set. During the testing procedure, each left-out testing subject's strengths of positive and negative network was normalized using the parameters acquired during training procedure, and then the trained models were used to predict the testing participant's AVLT-DR score.
The negative network predictive model tested LOOCV significantly predicted individual differences in episodic memory from rs-FC. Key nodes that brain regions contributed to the prediction model were mainly located in the prefrontal cortex, frontal cortex, parietal cortex and temporal lobe.
Our findings demonstrated that rs-FC among multiple neural systems could predict episodic memory at the individual level.
探索全脑静息态功能连接（rs-FC）是否可以预测主观认知下降和遗忘型轻度认知障碍个体的情景记忆表现。
本研究纳入了 33 名认知正常（CN）、26 名主观认知下降（SCD）和 27 名遗忘型轻度认知障碍（aMCI）患者，所有参与者均完成了静息态 fMRI（rs-fMRI）扫描和神经心理学量表测试数据。使用基于 rs-FC 数据的连接组预测建模（CPM）来预测个体情景记忆的听觉词语学习测试延迟回忆（AVLT-DR）得分。计算了连接矩阵中每个脑连接与 AD 痴呆前阶段患者的 AVLT-DR 得分之间的 Pearson 相关系数。Pearson 相关系数值分为正网络和负网络。然后定义了预测网络，并通过计算正网络和负网络的强度来使用它们。通过留一法交叉验证（LOOCV）进行 CPM，以训练线性模型，分别将正网络和负网络的强度与训练集中的 AVLT-DR 得分相关联。在测试过程中，使用训练过程中获得的参数对每个被留一法测试的受试者的正网络和负网络的强度进行归一化，然后使用训练好的模型预测测试参与者的 AVLT-DR 得分。
负网络预测模型在 LOOCV 测试中显著预测了 rs-FC 个体间情景记忆的差异。对预测模型有贡献的关键节点主要位于前额叶皮层、额叶皮层、顶叶皮层和颞叶。
我们的研究结果表明，多个神经系统的 rs-FC 可以在个体水平上预测情景记忆。
