Neurocognitive subgroups among newly diagnosed patients with schizophrenia spectrum or bipolar disorders: A hierarchical cluster analysis.

Studies across schizophrenia (SZ) and bipolar disorder (BD) indicate common transdiagnostic neurocognitive subgroups. However, existing studies of patients with long-term illness precludes insight into whether impairments result from effects of chronic illness, medication or other factors. This study aimed to investigate whether neurocognitive subgroups across SZ and BD can be demonstrated during early illness stages. Data from overlapping neuropsychological tests were pooled from cohort studies of antipsychotic-naïve patients with first-episode SZ spectrum disorders (n = 150), recently diagnosed BD (n = 189) or healthy controls (HC) (n = 280). Hierarchical cluster analysis was conducted to examine if transdiagnostic subgroups could be identified based on the neurocognitive profile. Patterns of cognitive impairments and patient characteristics across subgroups were examined. Patients could be clustered into two, three and four subgroups, of which the three-cluster solution (with 83% accuracy) was selected for posthoc analyses. This solution revealed a subgroup covering 39% of patients (predominantly BD) who were cognitively relatively intact, a subgroup of 33% of patients (more equal distributions of SZ and BD) displaying selective deficits, particularly in working memory and processing speed, and a subgroup of 28% (mainly SZ) with global impairments. The globally impaired group exhibited lower estimated premorbid intelligence than the other subgroups. Globally impaired BD patients also showed more functional disability than cognitively relatively intact patients. No differences were observed across subgroups in symptoms or medications. Neurocognitive results can be understood by clustering analysis with similar clustering solutions occurring across diagnoses. The subgroups were not explained by clinical symptoms or medication, suggesting neurodevelopmental origins.

Macroscale EEG characteristics in antipsychotic-naïve patients with first-episode psychosis and healthy controls.

Electroencephalography in patients with a first episode of psychosis (FEP) may contribute to the diagnosis and treatment response prediction. Findings in the literature vary due to small sample sizes, medication effects, and variable illness duration. We studied macroscale resting-state EEG characteristics of antipsychotic naïve patients with FEP. We tested (1) for differences between FEP patients and controls, (2) if EEG could be used to classify patients as FEP, and (3) if EEG could be used to predict treatment response to antipsychotic medication. In total, we studied EEG recordings of 62 antipsychotic-naïve patients with FEP and 106 healthy controls. Spectral power, phase-based and amplitude-based functional connectivity, and macroscale network characteristics were analyzed, resulting in 60 EEG variables across four frequency bands. Positive and Negative Symptom Scale (PANSS) were assessed at baseline and 4-6 weeks follow-up after treatment with amisulpride or aripiprazole. Mann-Whitney U tests, a random forest (RF) classifier and RF regression were used for statistical analysis. Our study found that at baseline, FEP patients did not differ from controls in any of the EEG characteristics. A random forest classifier showed chance-level discrimination between patients and controls. The random forest regression explained 23% variance in positive symptom reduction after treatment in the patient group. In conclusion, in this largest antipsychotic- naïve EEG sample to date in FEP patients, we found no differences in macroscale EEG characteristics between patients with FEP and healthy controls. However, these EEG characteristics did show predictive value for positive symptom reduction following treatment with antipsychotic medication.