title: Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects.
abstract: 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.
authors.affiliation: Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands. Electronic address: h.schnack@umcutrecht.nl.
authors.forename: Hugo G
authors.initials: HG
authors.lastname: Schnack
authors.forename: Mireille
authors.initials: M
authors.lastname: Nieuwenhuis
authors.forename: Neeltje E M
authors.initials: NE
authors.lastname: van Haren
authors.forename: Lucija
authors.initials: L
authors.lastname: Abramovic
authors.forename: Thomas W
authors.initials: TW
authors.lastname: Scheewe
authors.forename: Rachel M
authors.initials: RM
authors.lastname: Brouwer
authors.forename: Hilleke E
authors.initials: HE
authors.lastname: Hulshoff Pol
authors.forename: René S
authors.initials: RS
authors.lastname: Kahn
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001714:Bipolar Disorder / Q000473:pathology*; D001921:Brain / Q000473:pathology*; D003937:Diagnosis, Differential; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D012016:Reference Values; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000473:pathology*; D012680:Sensitivity and Specificity
keywords: Bipolar disorder; Classification; MRI; Machine learning; Schizophrenia