Striatal Volume Increase After Six Weeks of Selective Dopamine D2/3 Receptor Blockade in First-Episode, Antipsychotic-Naïve Schizophrenia Patients.

Patients with chronic schizophrenia often display enlarged striatal volumes, and antipsychotic drugs may contribute via the dopamine D2/3 receptor (D2/3R) blockade. Separating the effects of disease from medication is challenging due to the lack of a proper placebo-group. To address this, we conducted a longitudinal study of antipsychotic-naïve, first-episode schizophrenia patients to test the hypothesis that selective blockade of D2/3R would induce a dose-dependent striatal volume increase. Twenty-one patients underwent structural magnetic resonance imaging (sMRI), single-photon emission computed tomography (SPECT), and symptom severity ratings before and after six weeks of amisulpride treatment. Twenty-three matched healthy controls underwent sMRI and baseline SPECT. Data were analyzed using repeated measures and multiple regression analyses. Correlations between symptom severity decrease, volume changes, dose and receptor occupancy were explored. Striatal volumes did not differ between patients and controls at baseline or follow-up, but a significant group-by-time interaction was found (p = 0.01). This interaction was explained by a significant striatal volume increase of 2.1% in patients (Cohens d = 0.45). Striatal increase was predicted by amisulpride dose, but not by either D2/3R occupancy or baseline symptom severity. A significant reduction in symptom severity was observed at a mean dose of 233.3 (SD = 109.9) mg, corresponding to D2/3R occupancy of 44.65%. Reduction in positive symptoms correlated significantly with striatal volume increase, driven by reductions in hallucinations. Our data demonstrate a clear link between antipsychotic treatment and striatal volume increase in antipsychotic-naïve schizophrenia patients. Moreover, the treatment-induced striatal volume increase appears clinically relevant by correlating to reductions in core symptoms of schizophrenia.

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.

Patterns of Cortical Structures and Cognition in Antipsychotic-Naïve Patients With First-Episode Schizophrenia: A Partial Least Squares Correlation Analysis.

BACKGROUND: Schizophrenia is associated with alterations in cortical structures and cognitive impairments, but antipsychotic medication may affect these measures. We investigated patterns of relationships between cortical structures and cognitive domains in antipsychotic-naïve patients with first-episode schizophrenia. METHODS: T1-weighted 3T magnetic resonance imaging was performed in 105 patients and 136 healthy control subjects. Using FreeSurfer, we obtained measurements of cortical thickness, surface area, and mean curvature. Using an extensive neurocognitive battery including the Danish Adult Reading Test and subtests from the Cambridge Neuropsychological Test Automated Battery, we obtained estimates of premorbid intelligence, spatial working memory, spatial planning, intra-extradimensional set shifting, and reaction and movement times. With univariate analyses, we tested group differences between cortical structures and cognition. With partial least squares correlation analyses, we investigated patterns of associations between cortical structures and cognition. RESULTS: Patients had significantly higher mean curvature and were impaired on 7 of 11 cognitive parameters. The between-group partial least squares correlation analysis revealed two cortical thickness/cognition patterns that differentiated patients and healthy control subjects (omnibus test, p = .011). Most cortical regions contributed reliably to these patterns. In patients, spatial working memory, spatial planning, reaction and movement times, and premorbid intelligence contributed reliably to the pattern; in healthy control subjects, spatial planning and intra-extradimensional set shifting contributed reliably. CONCLUSIONS: Antipsychotic-naïve patients with first-episode schizophrenia displayed a higher mean curvature, but no significant difference in other gray matter indices was found. Nevertheless, the pattern of associations between global cortical thickness and cognitive functions was markedly different between groups. These multivariate analyses reveal a novel linkage between regional cortical brain structure and cognitive deficits at the earliest, never-medicated illness stage.

Cloning and transcription analysis of an AGAMOUS- and SEEDSTICK ortholog in the orchid Dendrobium thyrsiflorum (Reichb. f.).

Studies have shown that several plant species possess AGAMOUS (AG) and SEEDSTICK (STK) orthologs. These genes are part of the so-called C- and D MADS-box gene lineages and play key roles in ovule development in Arabidopsis thaliana. We have cloned an AG- and STK ortholog in the orchid Dendrobium thyrsiflorum, named DthyrAG1 and DthyrAG2, respectively, and analyzed their expression patterns. Quantification by real-time RT-PCR analysis shows that both are transcribed in the mature flowers and during ovule development. Localization of the transcripts by in situ hybridization analysis in flowers reveals that both genes are transcribed in the rostellum, stigma, and stylar canal. Expression analysis during ovule development shows that DthyrAG1 is expressed only in the initial periods of placenta- and ovule development, whereas the DthyrAG2 is transcribed throughout ovule development. These results suggest that both C- and D lineage orthologs are involved in various aspects of flower development and that DthyrAG2 have a more prominent role than DthyrAG1 in late ovule development in D. thyrsiflorum.