Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level.

Ziyu Zhu,Wenbin Li,Lingjiang Li,Melissa P Delbello,Du Lei,Xueling Suo,Kun Qin,John A Sweeney,Qiyong Gong

doi:10.3390/diagnostics11081416

Abstract

Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate PTSD from trauma-exposed non-PTSD at the individual level and to identify its most discriminant features. Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients. To evaluate our DL method, we used the traditional support vector machine (SVM) classifier as a reference. Our results showed that the proposed DL model allowed single-subject discrimination of PTSD and trauma-exposed non-PTSD individuals with higher accuracy (average: 80%) than the traditional SVM (average: 57.7%). The top 10 DL features were identified within the default mode, central executive, and salience networks; the first two of these networks were also identified in the SVM classification. We also found that nodal efficiency in the left fusiform gyrus was negatively correlated with the Clinician Administered PTSD Scale score. These findings demonstrate that DL based on graphical features is a promising method for assisting in the diagnosis of PTSD.

Highlights

Exposure to a disaster has been associated with a variety of mental health consequences [1]
Prior research has reported that survivors of natural disasters are highly likely to develop posttraumatic stress disorder (PTSD) [2], which is characterized by a heightened sensitivity to potential threats and can be devastating to the affected individuals and their families
In our previous study [11] on the topological organization of the functional connectome of individuals with PTSD, we demonstrated that topological alterations predominantly involved the default-mode network (DMN) and the salience network (SN), which are associated with affective processing [12] and interoceptive-autonomic processing [13], respectively

Summary

Introduction

Exposure to a disaster has been associated with a variety of mental health consequences [1]. Prior research has reported that survivors of natural disasters are highly likely to develop posttraumatic stress disorder (PTSD) [2], which is characterized by a heightened sensitivity to potential threats (including those related to the initial traumatic experience) and can be devastating to the affected individuals and their families. Many survivors exhibit posttraumatic stress symptoms in the weeks and months after exposure [3], but waiting for individuals to develop PTSD before intervening can delay preventive or early effective treatment. Chronic PTSD is associated with a host of physical ailments (e.g., irritable bowel syndrome [4]). It can be pernicious and disabling for many across the lifespan. There is an urgent need to find an accurate method to diagnose PTSD as early as possible after major acute stress

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