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

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 use...

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Bibliographic Details
Published in:Diagnostics (Basel) 2021-08, Vol.11 (8), p.1416
Main Authors: Zhu, Ziyu, Lei, Du, Qin, Kun, Suo, Xueling, Li, Wenbin, Li, Lingjiang, DelBello, Melissa P, Sweeney, John A, Gong, Qiyong
Format: Article
Language:English
Subjects:
Accuracy
Alzheimer's disease
Brain research
Classification
Deep learning
Earthquakes
graph theory
Medical imaging
Mental disorders
neuroimaging
Post traumatic stress disorder
posttraumatic stress disorder
salience network
support vector machine
Support vector machines
Time series
Trauma
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Summary: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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics11081416