Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects
Introduction
Currently, the diagnosis of psychiatric disorders such as schizophrenia and bipolar disorder is based predominantly on their clinical manifestations. While psychiatrists can establish the presence of illness (as distinct from its absence) with relative ease, discrimination between several possible diagnoses is far more complicated, especially in the early phase of schizophrenia and bipolar disorder. The availability of additional (objective) measures would assist psychiatrists in the process of diagnosis, with obvious benefits to efficiency of treatment and improved outcome. Magnetic resonance imaging (MRI) has proven to be an effective technique to detect structural brain abnormalities at group-level in schizophrenia patients (meta-analyses: Haijma et al., 2012, Olabi et al., 2011) and those with bipolar disorder (meta-analyses: Kempton et al., 2008, McDonald et al., 2004). Unfortunately, statistical group differences do not translate to discovering deviations from normal on an individual basis and therefore are not sufficient as a diagnostic aid.
Using machine learning techniques, promising results have been obtained for the classification of schizophrenia patients and healthy subjects based on MRI scans. Pioneering work was done by Davatzikos et al. (2005), followed by numerous other investigations. The support vector machine (SVM; Fan et al., 2008, Ingalhalikar et al., 2010, Koutsouleris et al., 2009, Pohl and Sabuncu, 2009, Vapnik, 1999) and the Discriminant Function Analysis (Karageorgiou et al., 2011, Kasparek et al., 2011, Leonard et al., 1999, Liu et al., 2004, Nakamura et al., 2004, Takayanagi et al., 2011) are the most frequently used methods (for an overview of schizophrenia classification studies using structural MRI, see Nieuwenhuis et al. (2012)). We recently demonstrated in two large independent samples that a classification model built from one data set can be used to classify new subjects as schizophrenia patients or healthy subjects with 71% accuracy. To the best of our knowledge, no studies have been published investigating the use of MRI to separate bipolar patients from healthy subjects or schizophrenia patients [although one study combined structural MRI brain measures and neuropsychological test scores for this purpose (Pardo et al., 2006)]. Given the brain abnormalities found in bipolar disorder and schizophrenia and the differences between these abnormalities (Arnone et al., 2009, Ellison-Wright and Bullmore, 2010, Hulshoff Pol et al., 2012, Koo et al., 2008, McDonald et al., 2005, Qiu et al., 2008, Rimol et al., 2010, Rimol et al., 2012), it may be fruitful to apply these classification models to help separate these two disorders. We train three SVM models to separate patients with schizophrenia from those with bipolar disorder, healthy subjects from patients with schizophrenia, and healthy subjects from patients with bipolar disorder. Although it could be of theoretical interest to build a three-group classifier that separates the three groups in a single step, our approach addresses the clinical relevant issue of separating the two disorders using MRI. Furthermore, it provides brain patterns that discriminate between the respective groups, which can be analyzed to indicate which features are unique to the discrimination between schizophrenia and bipolar disorder. We test the predictive power of the models both in the dataset they were built on and in an independent dataset.
Section snippets
General
In this study we used two datasets. The first set, called discovery sample, was used to build classification models for the separation of healthy subjects and patients with schizophrenia and bipolar disorder. The models were tested on this set too. On the second set, called validation sample, no models were built; this independent sample was used to test the generalizability of the models built on the first set.
Subjects — discovery sample
Schizophrenia patients (SZ), patients with bipolar disorder (BP) and healthy
Results
The classification results of the models are given in Table 2. In the discovery sample, the models involving SZ patients performed best, with classification accuracies of 86% or higher for the models including all subjects (M). SZ patients can thus be separated well from HC subjects and from BP patients (p < 0.001). The separation of BP patients and HC subjects, on the other hand, turned out to be less accurate: 67% of the HC subjects were correctly classified (p = 0.02) and only 53% of the BP
Discussion
The purpose of this study was to classify patients with schizophrenia, bipolar disorder, and healthy controls on the basis of their structural MRI scans. We used a support vector machine (SVM) to create three models from gray matter density images, each separating two of the three groups. We confirmed that it is possible to separate schizophrenia patients from healthy subjects with an average accuracy of 90%. Additionally, we demonstrated that schizophrenia patients can be separated from those
Acknowledgments
We thank Dorret Boomsma for providing us with healthy twin subjects from the Netherlands Twin Registry. We thank and Astrid van der Schot for assistance with data acquisition and processing.
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Authors contributed equally.