Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

Abstract

Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n = 25) and healthy controls (n = 25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.

Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder

“…In the application of the AAFC method to study the WM differences between MDD and healthy controls, we found that diffusion properties were affected in left ILF, right CST, left CB and right TF, which suggested potential WM abnormalities in emotional processing and sensorimotor areas in MDD. Similar to our findings, previous studies have reported WM abnormalities in the right CST (Osoba et al, 2013; Sacchet et al, 2014b,a; Wang et al, 2013b), the left CB (Arnold et al, 2012; Emberson et al, 2014; Wang et al, 2013b), the right TF (Lyden et al, 2014; Schnyer et al, 2017; Wang et al, 2013b) and the left ILF (de Diego-Adelino et al, 2014; Kieseppä et al, 2010). In this study, we found generally decreased MD and increased FA in the MDD subjects compared to the healthy controls.…”

Support Vector Machine Classification of Obsessive-Compulsive Disorder Based on Whole-Brain Volumetry and Diffusion Tensor Imaging

“…Above procedures were automatically processed in PRoNTo's “Specify model” programs. And the whole process had been described in previous studies detailedly ( 24 ).…”

“…As for performance evaluation, once the SVM algorithm has been established, it is used to predict a new and previously unseen subject to and decide which group it belongs ( 44 ). A 1,000-times non-parametric permutation test ( 24 , 28 , 45 , 46 ) was used to obtain a corrected p- value to determine the statistical significance of the accuracy, sensitivity and specificity. In detail, accuracy is the proportion of subjects correctly classified into the patient or control group.…”

<p>Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study</p>

“…Another research avenue could also be the inclusion of neuroimaging biomarkers, which have been identified for PTSD. [57][58][59][60][61] Indeed, the combination of self-reported data and neuroimaging features could provide a complete model of the risk of depressive status in military personnel. A study revealed that using functional MRI data in an SVM could identify patients with severe depression, but did not perform well for milder depression.…”