Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder
Introduction
A method for objectively identifying the presence or absence of psychiatric disorders, such as major depressive disorder, is a long standing need in psychiatry (Kapur et al., 2012). One promising approach is to use advances in MRI methods and analytics to derive an objective diagnosis. Although mood disorders have been extensively studied with MRI (Drevets et al., 2008, Lorenzetti et al., 2009), including both structural and functional neuroimaging, few studies have used imaging data to classify MDD. The current study examines whether in vivo diffusion tensor MRI (DTI), a measure of white matter microstructure of the brain, can be used to accurately diagnose major depressive disorder (MDD) (Bracht et al., 2015, Versace et al., 2010). Given the view that depression results from vulnerabilities across interconnected brain networks rather than specific brain nodes (Mayberg, 1997, Wang et al., 2016; Mulders et al., 2015), approaches that look at the underlying white matter structure that connects these networks could provide important diagnostic utility.
Diffusion tensor imaging (DTI) is a technique that utilizes the ability of MRI to tag water molecules and then wait some period of time to determine the extent to which those molecules are microscopically diffused. By measuring multiple spatial directions, vectors can be generated for each brain voxel to quantify the fiber orientation and integrity of white matter pathways within the cerebral cortex. There are a number of different metrics that can be generated from DTI, but scalar measures are more commonly used in MDD as they can be correlated with disease severity and/or symptoms.
Scalar measures are derived from calculations of one or more of the 3 principle directional vectors of the “diffusion tensor” represented as an ellipsoid. One common metric is fractional anisotropy (FA), which is the extent to which diffusion is characterized as anisotropic, or highly directional (high FA) vs unrestricted or isotropic (low FA). For example, one of the white matter pathways with the highest FA values is the corpus callosum, due to its highly organized, densely packed fibers that run mainly in a left-right direction. In addition to directionality, FA is influenced by axon size and density, pathway geometry, and extent of fiber intersections (Alexander et al., 2007, Beaulieu, 2002).
Another scalar measure is calculated as the average of the 3 directional vectors and is referred to as mean diffusivity (MD). MD reflects the extent to which there is water movement at all and is a useful clinical measure to indicate edema and restricted liquid flow. Axial diffusivity (AD) is the strength of the primary directional vector and radial diffusivity (RD) is the mean of the 2 non-principle vectors. While all these measures can be calculated from DTI imaging, FA is the most reliably sensitive measure of between group microstructural white matter differences (see Feldman et al., 2010).
A number of studies have demonstrated differences in FA values between patients with MDD and healthy controls. A meta-analysis of 11 studies that examined FA in individuals with MDD (Liao et al., 2013) identified 4 consistent locations associated with altered FA in MDD compared to healthy controls: right and left dorsal frontal regions, a region of the right fusiform and a region of the right occipital lobe. A review paper that focused on 35 studies of WM alterations in pathways associated with the reward circuit (Bracht et al., 2015), found reduced FA in the cingulum bundle, increases and decreases of FA in the uncinate fasciculus in adolescents, and reduced FA in the uncinate fasciculus and the anterior thalamic radiation/supero-lateral medial forebrain bundle during acute depressive episodes in adults. Other studies have focused on WM microstructure in those at risk for MDD either by virtue of family history (Keedwell et al., 2012) or genetic polymorphisms (Pacheco et al., 2009).
Given the heterogeneity of findings, an important theme that emerges from this work is that white matter microstructure alterations in MDD are distributed across many defined brain networks. Thus, the use of DTI to understand underlying WM features associated with MDD has been useful in characterizing the underlying brain circuits associated with the psychiatric disorder. However, despite these interesting results, it remains unclear if and when DTI might be implemented as a promising diagnostic tool. One of the steps needed in order to accomplish this goal would be to quantitatively determine how well DTI measures can discriminate people with and without MDD.
One approach to examine the diagnostic utility of MRI modalities involves applying multivariate machine learning classification algorithms in order to identify individuals with a specific disorder (Orrù et al., 2012). There has been increasing interest in applying multivariate pattern analysis methods in order to categorize patients suffering from psychiatric disorders from healthy controls (Cohen et al., 2011). The main advantage of these approaches is that they are predictive. Once a classifier has been defined, it can then be tested on new individuals to predict group membership. These approaches have utilized functional brain imaging (Craddock et al., 2009, Zeng et al., 2012) and structural brain images (Ardekani et al., 2011). This approach is starting to be applied to MDD (for review see (Patel et al., 2016)).
To date, this machine learning approach has been applied to a range of MRI modalities in an effort to automate the diagnosis of a number of disorders (Magnin et al., 2009) but few studies have been completed with MDD. One of the earliest studies examined the application of SVM to depression diagnosis using resting-state fMRI in 20 patients with MDD and 20 matched controls. While the purpose of this work was to examine different feature selection approaches, the unselected data yielded only modest classification accuracy – 62.5%. In another study of 32 women (14 with MDD), researchers applied global tractography-based graph metrics for the classification of depression (Sacchet, 2015). The investigators characterized connectivity between 34 cortical regions resulting in 9 global graph metrics that were then used in a SVM classification. Combined, the 9 metrics classified MDD and controls at a performance level of 71.9% accuracy.
A second study applying SVM classification to DTI in order to study depression applied probabilistic tractography to reconstruct specific WM tracts and then extracted anatomical networks (Fang et al., 2012). SVM was then applied to determine the most discriminating connections within these networks. The resulting classifications were highly accurate (91.7%) and revealed that the most discriminating connections were primarily within the cortical-limbic network where it was revealed that young adult first episode MDD patients displayed increased anatomical connectivity relative to healthy controls. In this study, a two sample t-test approach was taken to select features to be utilized in classification. An important limitation of the use of feature selection algorithms can often produce sample-specific results that may not generalize to new data.
The aim of the current study is to continue to explore the utility of DTI in the classification of individuals diagnosed with MDD. The previous examinations using SVM classification of DTI imaging in MDD did not report the utility of standard scalar metrics such as FA, MD, RD and therefore, those metrics will be examined here. Moreover, when feature selection techniques were applied (Fang et al., 2012), classification accuracy was greatly increased. It is important to examine the predictive power of classification both with and without feature selection to understand the predictive range of these techniques. This approach was applied to a sample of treatment-seeking participants with DSM-IV Major Depressive Disorder who were part of a study testing the efficacy of attention bias modification (Beevers et al., 2015).
Section snippets
Sample
Fifty-two treatment-seeking participants with DSM-IV Major Depressive Disorder (MDD) and 45 healthy control (HC) participants were recruited for this study from advertisements placed online, in newspapers, and on late-night TV. Participants were screened for medical or physical conditions that would preclude participation in an fMRI study (e.g., orthodontic braces). They also completed an abbreviated Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998) to determine
Demographic characteristics
Table 1 shows the demographic and depression symptom profile of the MDD and HC groups. The groups were well matched on age, gender and income but were marginally different on ethnic distribution. Given the MDD diagnosis, the groups were significantly different on BDI-II and IDAS (Inventory of Depression and Anxiety Symptoms, (Watson et al., 2008)).
TBSS voxel-wise results
Contrasting the FA skeleton between MDD and HC groups revealed only a single significant cluster: FA values were greater for MDD than HC in the right
Discussion
The current report supports using machine learning algorithms to capture the diagnostic information contained in structural MRI data in order to differentiate between patients diagnosed with MDD and healthy controls. Despite the relatively small sample size, using both an unselected and a feature selected DTI dataset, support vector machine binary classification was able to significantly distinguish between MDD and HC using the DTI metric of fractional anisotropy (FA). While this approach
Acknowledgements
This research was supported in part by an award from the National Institute of Health (R21MH092430). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Special thanks to Robert Chapman for his help with data collection. We would also like to thank Ian Dobbins for his assistance with the support vector machine figure. P.C. Clasen is now affiliated with Facebook.
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