Multivariate pattern analysis of obsessive–compulsive disorder using structural neuroanatomy
Introduction
Obsessive–compulsive disorder (OCD) is a common psychiatric disorder characterized by the presence of obsessions in the form of intrusive and distressing thoughts, ideas or images as well as the urge to perform repetitive or ritualistic behaviors known as compulsions. OCD has a lifetime prevalence rate of approximately 2.3% in the general population, thus making it a major burden to society (Ruscio et al., 2010). Currently, OCD is diagnosed solely on the basis of a subjective clinical interview and rating measures rather than objective laboratory tests, which often leads to diagnostic variability among different clinicians, cultures and districts (Phillips et al., 2009). It is thus highly desirable to develop additional valid and objective biomarkers to distinguish patients with OCD from healthy control subjects (HCS).
In recent years, neuroimaging has attracted considerable attention in defining the neural correlates of OCD and has raised hopes that it may one day aid the clinical assessment of individuals with this disorder (Linden and Fallgatter, 2009, Savitz et al., 2013). Preliminary results obtained from volumetric studies have revealed differences in both cerebral gray matter (GM) and white matter (WM), as well as specific regional abnormalities in the basal ganglia and cerebellum (de Wit et al., 2014, Hoexter et al., 2012, Szeszko et al., 2008, van den Heuvel et al., 2009). Meanwhile, a growing number of neuroimaging studies have reported evidence of functional neural abnormalities in OCD relative to HCS, pointing to the involvement of a cortico-striatal circuit in the behavioral control functions of OCD (Anticevic et al., 2014, Marsh et al., 2014, Milad et al., 2013). Because functional neuroimaging studies are methodologically demanding and can vary dramatically in paradigms and analytic methodologies, structural neuroimaging is more often performed in clinical practice. Thus, structural neuroimaging is potentially more suitable for providing measures for clinical diagnostic applications. However, the majority of available studies that provide evidence for cerebral structural abnormalities between OCD patients and HCS are based on average estimates at a group level, which limit their usages in clinical practice where doctors need to make decisions about individuals.
In an effort to increase the translational applicability of neuroimaging results, there has been a recent shift toward the use of multivariate pattern analyses (MVPA) techniques (Dosenbach et al., 2010), which have emerged as a powerful tool owing to their ability to extract spatial and/or temporal patterns from neuroimaging data and use this information to categorize individuals into different categories (Lao et al., 2004). This approach has been applied to differentiate OCD subjects from HCS based on functional magnetic resonance imaging (fMRI) (Weygandt et al., 2012) and diffusion tensor imaging (DTI) data (Li et al., 2014). Compared with traditional standard mass-univariate analytical methods, the MVPA approach has two main advantages. First, MVPA allows inferences at the level of the individual and can thus provide results with higher translational applicability in everyday clinical practice. Second, MVPA takes interregional correlations into account and therefore may be more sensitive to subtle and spatially distributed differences (Pereira et al., 2009).
To our knowledge, no study has directly compared structural neuroimaging measures between individuals with OCD and HCS with MVPA, though there have been numerous studies on alterations of GM and WM in OCD with pooled results from meta-analyses (Piras et al., 2013, Radua and Mataix-Cols, 2009, Rotge et al., 2010). Nearly all previous neuroimaging classifications were performed using a specific MVPA approach known as support vector machine (SVM) (Orru et al., 2012), whereas probabilistic classification models such as Gaussian process classifier (GPC) were largely ignored. The SVM is an algorithm designed for binary classification that maximizes the margin between classes in a high dimensional space. Mathematically, the discriminant function is defined by a weight vector orthogonal to the decision boundary, which can be uniquely specified by the samples that lie closest to the decision boundary, referred to as support vectors. The decision boundary represents the rule for classification of new examples. The GPC is a probabilistic model and is a Bayesian extension of logistic regression classifier. In contrast to SVM, the predicted class is augmented by an estimate of the certainty of the prediction. GPCs are best described as a distribution over functions. Based on Bayes׳ rule the posterior distribution of functions which represent the training data is found in an optimal way. This posterior distribution is used to classify new examples according to the rules of probability (Wolfers et al., 2015). Therefore, we aimed to apply both SVM and GPC classification approaches to discriminate patients with OCD from controls based on whole-brain structural GM and WM images. We hypothesized that (1) GM and WM may contribute differently to the classification of OCD and (2) SVM and GPC may show differences in discriminating OCD from HCS.
Section snippets
Subjects
The study was approved by the Ethics Committee of the West China Hospital, Sichuan University, and written informed consent was obtained from each participant. A total of 33 OCD patients and 33 sex-, age- and years-of-education-matched HCS participated in the present investigation (Table 1). All participants were right-handed and native Chinese speakers. OCD patients were recruited from the Mental Health Center, West China Hospital, Sichuan University, and diagnoses were confirmed using the
Demographic and clinical characteristics
There were no significant differences between the 33 OCD patients and 33 controls in terms of gender (21 male, 12 female in both groups) or age (27.45±9.28 vs. 27.33±9.43 years, P=0.959), and all participants were right-handed. Nineteen patients were drug-naive, while the remaining 14 were receiving medication for OCD (4 were on clomipramine hydrochloride, 3 on paroxetine hydrochloride, 3 on fluoxetine hydrochloride, 3 on sertraline and only 1 on three types of drugs including clomipramine
Discussion
To the best of our knowledge, this is the first study to integrate both SVM and GPC pattern analysis into the automatic discrimination of OCD patients from controls based on GM and WM anatomical features. Our study produced three main findings. (1) Voxel-wise univariate analysis of the structural neuroimaging data did not detect any regions that showed a significant alteration between OCD patients and HCS while MVPA demonstrated that both SVM and GPC achieved good separation between OCD
Role of the funding source
This study was supported by the National Natural Science Foundation of China (Grant nos. 81030027, 81171488, 81227002, and 81220108013), the National Key Technologies R&D Program (Program no. 2012BAI01B03) and the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT, Grant no. IRT1272) of China. The authors declare no biomedical financial interests or potential conflicts of interest.
Contributors
X. Huang, Q. Gong and Y. Yang designed the study; X. Hu, B. Li, W. Tang and F. Li acquired the data; X. Hu, Q. Liu and H. Sun analyzed the data; X. Hu, X. Huang and Q. Gong wrote the article, which Q. Liu, B. Li, W. Tang, H. Sun, F. Li and Y. Yang reviewed. All authors approved the final version for publication.
Conflict of interest
All authors declare that they have no conflicts of interest.
Acknowledgments
We are grateful to the patients and healthy volunteers who participated in the study.
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