Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

Abstract

Deficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D2 receptor antagonist, amisulpride (280.3±159 mg per day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups. We identified two statistically distinct subgroups of patients. We found no significant baseline psychopathological differences between these subgroups, but the effect of treatment in the groups was predicted with an accuracy of 74.3% (P=0.003). In conclusion, electrophysiology and cognition data may be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors to apply data-driven, multivariate and multimodal models to facilitate progress from symptom-based psychiatry toward individualized treatment regimens.

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data

“…In future work, there are several other mechanisms that could be tested. These include alternative late integration schemes, as well as other imputation types, such as multiple imputation and imputation with reject option 17,57 .…”

The impact of schizophrenia and intelligence on the relationship between age and brain volume

“…As part of a multimodal cohort study, both in- and out-patients aged 18 to 45 years presenting with a first-episode of schizophrenia were referred from Mental Health Services in the Capital Region of Denmark from December 2008 to April 2014. The cohort was initially presented in Nielsen et al (2012) , and there is partial overlap with aspects of data presented on cortical thickness, WM and psychotic symptoms, and machine learning ( Bak et al, 2017 ; Ebdrup et al, 2016 ; Jessen et al, 2018 ), see also www.cinsr.dk for other publications from this cohort. Patients fulfilling criteria of an ICD-10 diagnosis of schizophrenia were included.…”

Length of stay for inpatient incompetent to stand trial patients: importance of clinical and demographic variables

“…Recent research examining treatment resistance in schizophrenia has pointed out several factors, including issues of cognitive functioning or cognitive disability in schizophrenia. 23,24 A study which conducted long-term follow-up of schizophrenia patients showed that those with the poorest response to treatment typically had higher rates of negative symptoms and worse cognitive functioning at baseline. 25 Likewise, another study recently found that measures of cognitive functioning accurately predicted treatment response to antipsychotic medication, even when there were no differences in measures of psychopathology between treatment responders and nonresponders.…”

“…25 Likewise, another study recently found that measures of cognitive functioning accurately predicted treatment response to antipsychotic medication, even when there were no differences in measures of psychopathology between treatment responders and nonresponders. 24 Given the relation between poor treatment response and cognitive functioning, it is worth noting that cognitive impairment of some form is considered a hallmark of schizophrenia. [26][27][28] Generally, in our hospitals, we have found most patients actively psychotic are stabilized with the use of antipsychotic medication early in their hospital stay.…”