Abstract
BackgroundTraumatized 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.MethodsRs-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.ResultsSpatial 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.LimitationsThe 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.ConclusionSpontaneous 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.
Highlights
Traumatized earthquake survivors may develop poor memory function
Spontaneous brain activity of the left middle frontal gyrus and the left precuneus acquired by Resting-state functional magnetic resonance imaging (rs-fMRI) may be a brain mechanism of visual working memory that is related to post-traumatic stress disorder (PTSD) symptoms
Machine learning may be a useful tool in the identification of brain mechanisms of memory impairment in trauma survivors
Summary
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. Trauma survivors are at high risk of developing mental disorders, including a series of psychiatric symptoms (e.g., re-experiencing [1], depression [1, 2], and anxiety [1, 3, 4]) as well as cognitive decline (e.g., memory impairment [5, 6]). These symptoms are difficult to measure, especially cognitive decline, which may lead to loss of well-being in later life [7]. Dividing trauma survivors into two groups (with and without PTSD) based on diagnostic categories can cause brain, cognition, and behavioral changes in trauma survivors without critical PTSD to go unnoticed
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