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A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation.
Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission.
Cognitive data significantly classified patients from controls (accuracies = 60–69%; p values = 0.0001–0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission.
In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.
Schizophrenia has been associated with changes in both cortical thickness and surface area, but antipsychotic exposure, illness progression and substance use may confound observations. In antipsychotic-naïve schizophrenia patients, we investigated cortical thickness and surface area as well as mean curvature before and after monotherapy with amisulpride, a relatively selective dopamine D2/3 receptor antagonist.
Fifty-six patients and 59 matched healthy controls (HCs) underwent T1-weighted 3T magnetic resonance imaging. Forty-one patients and 51 HCs were re-scanned. FreeSurfer-processed baseline, follow-up values and symmetrized percentage changes (SPC) in cortical structures were analysed using univariate analysis of variance. Clinical measures comprised psychopathology ratings, assessment of functioning and tests of premorbid and current intelligence. We applied false discovery rate correction to account for multiple comparisons.
At baseline, groups did not differ in cortical thickness or surface area; however, curvature in the left hemisphere was higher in patients (p = 0.015). In both patients and HCs, higher curvature was associated with lower premorbid (p = 0.009) and current intelligence (p < 0.001). Lower surface area was associated with lower premorbid intelligence (p = 0.017). After 6 weeks, the cortical structures did not differ between groups. Amisulpride dose (275.0 mg/day) did not correlate with any cortical structures (p > 0.43). Cortical thickness SPC was negatively associated with symptom improvement (p = 0.002).
Schizophrenia appears associated with subtle, yet clinically relevant aberrations in cortical structures. Mean curvature holds promise as a sensitive supplement to cortical thickness and surface area to detect complex structural brain abnormalities.
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