Fibre density and fibre-bundle cross-section of the corticospinal tract are distinctly linked to psychosis-specific symptoms in antipsychotic-naïve patients with first-episode schizophrenia.

Multiple lines of research support the dysconnectivity hypothesis of schizophrenia. However, findings on white matter (WM) alterations in patients with schizophrenia are widespread and non-specific. Confounding factors from magnetic resonance image (MRI) processing, clinical diversity, antipsychotic exposure, and substance use may underlie some of the variability. By application of refined methodology and careful sampling, we rectified common confounders investigating WM and symptom correlates in a sample of strictly antipsychotic-naïve first-episode patients with schizophrenia. Eighty-six patients and 112 matched controls underwent diffusion MRI. Using fixel-based analysis (FBA), we extracted fibre-specific measures such as fibre density and fibre-bundle cross-section. Group differences on fixel-wise measures were examined with multivariate general linear modelling. Psychopathology was assessed with the Positive and Negative Syndrome Scale. We separately tested multivariate correlations between fixel-wise measures and predefined psychosis-specific versus anxio-depressive symptoms. Results were corrected for multiple comparisons. Patients displayed reduced fibre density in the body of corpus callosum and in the middle cerebellar peduncle. Fibre density and fibre-bundle cross-section of the corticospinal tract were positively correlated with suspiciousness/persecution, and negatively correlated with delusions. Fibre-bundle cross-section of isthmus of corpus callosum and hallucinatory behaviour were negatively correlated. Fibre density and fibre-bundle cross-section of genu and splenium of corpus callosum were negative correlated with anxio-depressive symptoms. FBA revealed fibre-specific properties of WM abnormalities in patients and differentiated associations between WM and psychosis-specific versus anxio-depressive symptoms. Our findings encourage an itemised approach to investigate the relationship between WM microstructure and clinical symptoms in patients with schizophrenia.

Lower Vitamin C Levels Are Associated With Less Improvement in Negative Symptoms in Initially Antipsychotic-Naïve Patients With First-Episode Psychosis.

Low levels of vitamin C have been observed in patients with schizophrenia and psychosis, and vitamin C may affect the dopaminergic system. Likewise, antipsychotic medication modulates striatal dopamine D2 receptors. We measured vitamin C levels in 52 patients with first-episode psychoses (24 females, age 23.1 ± 5.2 years) and 57 matched HCs (20 females, age 22.7 ± 4.3 years) before and after 6 weeks where patients received aripiprazole monotherapy (mean dose 10.4 mg ± 4.8 mg). At baseline, patients displayed lower levels of vitamin C (57.4 ± 25.9 µM) than controls (72.7 ± 21.4 µM) (t = 3.4, P = .001). Baseline symptoms and vitamin C levels were not correlated. Higher baseline vitamin C levels were associated with more improvement in negative symptoms (n = 39, R2 = 0.20, F = 8.2, P = .007), but not with age, sex, or p-aripiprazole. Because negative symptoms are generally considered challenging to alleviate, a potential adjunctive effect of vitamin C on treatment response should be tested in future randomized clinical trials.

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.

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.