Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.
Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China.
BMC Psychiatry. 2020 Feb 3;20(1):43. doi: 10.1186/s12888-020-2452-5.
Traumatized earthquake survivors may develop poor memory function. Resting-state functional magnetic resonance imaging (rs-fMRI) and machine learning techniques may one day aid the clinical assessment of individual psychiatric patients. This study aims to use machine learning with Rs-fMRI from the perspectives of neurophysiology and neuroimaging to explore the association between it and the individual memory function of trauma survivors.
Rs-fMRI data was acquired for eighty-nine survivors (male (33%), average age (SD):45.18(6.31) years) of Wenchuan earthquakes in 2008 each of whom was screened by experienced psychiatrists based on the clinician-administered post-traumatic stress disorder (PTSD) scale (CAPS), and their memory function scores were determined by the Wechsler Memory Scale-IV (WMS-IV). We explored which memory function scores were significantly associated with CAPS scores. Using simple multiple kernel learning (MKL), Rs-fMRI was used to predict the memory function scores that were associated with CAPS scores. A support vector machine (SVM) was also used to make classifications in trauma survivors with or without PTSD.
Spatial addition (SA), which is defined by spatial working memory function, was negatively correlated with the total CAPS score (r = - 0.22, P = 0.04). The use of simple MKL allowed quantitative association of SA scores with statistically significant accuracy (correlation = 0.28, P = 0.03; mean squared error = 8.36; P = 0.04). The left middle frontal gyrus and the left precuneus contributed the largest proportion to the simple MKL association frame. The SVM could not make a quantitative classification of diagnosis with statistically significant accuracy.
The use of the cross-sectional study design after exposure to an earthquake and the leave-one-out cross-validation (LOOCV) increases the risk of overfitting.
Spontaneous brain activity of the left middle frontal gyrus and the left precuneus acquired by rs-fMRI may be a brain mechanism of visual working memory that is related to PTSD symptoms. Machine learning may be a useful tool in the identification of brain mechanisms of memory impairment in trauma survivors.
创伤后地震幸存者可能会出现记忆功能不良。静息态功能磁共振成像(rs-fMRI)和机器学习技术有朝一日可能会帮助临床评估个体精神病人。本研究旨在使用 rs-fMRI 从神经生理学和神经影像学的角度,探索其与创伤幸存者个体记忆功能之间的关联。
对 2008 年汶川地震的 89 名幸存者(男性占 33%,平均年龄(标准差):45.18(6.31)岁)进行了 rs-fMRI 数据采集,每位幸存者均由经验丰富的精神科医生根据临床医生管理的创伤后应激障碍 (PTSD) 量表 (CAPS) 进行筛查,他们的记忆功能评分由韦氏记忆量表-IV (WMS-IV) 确定。我们探索了哪些记忆功能评分与 CAPS 评分显著相关。使用简单的多核学习 (MKL),对与 CAPS 评分相关的记忆功能评分进行 rs-fMRI 预测。还使用支持向量机 (SVM) 在有或没有 PTSD 的创伤幸存者中进行分类。
空间加法 (SA),由空间工作记忆功能定义,与总 CAPS 评分呈负相关(r=−0.22,P=0.04)。简单的 MKL 允许 SA 分数与统计上显著的准确性进行定量关联(相关性=0.28,P=0.03;均方误差=8.36;P=0.04)。左中额回和左楔前叶对简单 MKL 关联框架的贡献最大。SVM 不能以统计上显著的准确性对诊断进行定量分类。
在经历地震后采用横断面研究设计和留一法交叉验证 (LOOCV) 增加了过度拟合的风险。
通过 rs-fMRI 获得的左中额回和左楔前叶的自发脑活动可能是与 PTSD 症状相关的视觉工作记忆的大脑机制。机器学习可能是识别创伤幸存者记忆障碍的大脑机制的有用工具。