Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects

• Published in: NeuroImage (Orlando, Fla.) Vol. 84; pp. 299 - 306
• Main Authors: Schnack, Hugo G., Nieuwenhuis, Mireille, van Haren, Neeltje E.M., Abramovic, Lucija, Scheewe, Thomas W., Brouwer, Rachel M., Hulshoff Pol, Hilleke E., Kahn, René S.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.01.2014; Elsevier; Elsevier Limited
• Subjects: Adult; Adult and adolescent clinical studies; Age; Algorithms; Artificial Intelligence; Biological and medical sciences; Bipolar disorder; Bipolar Disorder - pathology; Bipolar disorders; Brain - pathology; Classification; Diagnosis, Differential; Diffusion Tensor Imaging - methods; Female; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Independent sample; Machine learning; Male; Medical sciences; Mood disorders; MRI; NMR; Nuclear magnetic resonance; Pattern Recognition, Automated - methods; Psychiatrists; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Psychoses; Psychotropic drugs; Reference Values; Reproducibility of Results; Schizophrenia; Schizophrenia - pathology; Sensitivity and Specificity; Studies; Schizophrenia; Bipolar disorder; MRI; Machine learning; Classification; Psychosis; Mood disorder; Learning; Human; Acquisition process; Nuclear magnetic resonance imaging
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.08.053

Although structural magnetic resonance imaging (MRI) has revealed partly non-overlapping brain abnormalities in schizophrenia and bipolar disorder, it is unknown whether structural MRI scans can be used to separate individuals with schizophrenia from those with bipolar disorder. An algorithm capable of discriminating between these two disorders could become a diagnostic aid for psychiatrists. Here, we scanned 66 schizophrenia patients, 66 patients with bipolar disorder and 66 healthy subjects on a 1.5T MRI scanner. Three support vector machines were trained to separate patients with schizophrenia from healthy subjects, patients with schizophrenia from those with bipolar disorder, and patients with bipolar disorder from healthy subjects, respectively, based on their gray matter density images. The predictive power of the models was tested using cross-validation and in an independent validation set of 46 schizophrenia patients, 47 patients with bipolar disorder and 43 healthy subjects scanned on a 3T MRI scanner. Schizophrenia patients could be separated from healthy subjects with an average accuracy of 90%. Additionally, schizophrenia patients and patients with bipolar disorder could be distinguished with an average accuracy of 88%.The model delineating bipolar patients from healthy subjects was less accurate, correctly classifying 67% of the healthy subjects and only 53% of the patients with bipolar disorder. In the latter group, lithium and antipsychotics use had no influence on the classification results. Application of the 1.5T models on the 3T validation set yielded average classification accuracies of 76% (healthy vs schizophrenia), 66% (bipolar vs schizophrenia) and 61% (healthy vs bipolar). In conclusion, the accurate separation of schizophrenia from bipolar patients on the basis of structural MRI scans, as demonstrated here, could be of added value in the differential diagnosis of these two disorders. The results also suggest that gray matter pathology in schizophrenia and bipolar disorder differs to such an extent that they can be reliably differentiated using machine learning paradigms. •We separated patients with schizophrenia and bipolar disorder based on their sMRI scans.•We trained a support vector machine (SVM) model to do this in a 1.5T ‘discovery set’.•Using cross-validation the model obtained a classification accuracy of >80% in this set.•Applying this model to an independent 3T ‘validation set’ yielded 66% accuracy.•Patterns of brain abnormalities differ between schizophrenia and bipolar disorder.