Classification of first-episode psychosis using cortical thickness: A large multicenter MRI study.

Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between predictions and clinical variables. 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20,484 vertices) were extracted. Linear Support Vector Machine was used for classification within a repeated nested cross-validation framework. Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy of 66.2% and an Area Under the Curve of 72%. By stratifying our sample for MRI scanner, we increased generalizability across sites. Temporal brain areas resulted as the most influential in the classification. The predictive decision scores significantly correlated with age at onset, duration of treatment, and positive symptoms. In conclusion, although far from the threshold of clinical relevance, temporal cortical thickness proved to classify between FEP subjects and healthy individuals. The assessment of site-dependent variables permitted an increase in the across-site generalizability, thus attempting to address an important machine learning limitation.

Interaction between DRD2 variation and sound environment on mood and emotion-related brain activity.

Sounds, like music and noise, are capable of reliably affecting individuals' mood and emotions. However, these effects are highly variable across individuals. A putative source of variability is genetic background. Here we explored the interaction between a functional polymorphism of the dopamine D2 receptor gene (DRD2 rs1076560, G>T, previously associated with the relative expression of D2S/L isoforms) and sound environment on mood and emotion-related brain activity. Thirty-eight healthy subjects were genotyped for DRD2 rs1076560 (G/G=26; G/T=12) and underwent functional magnetic resonance imaging (fMRI) during performance of an implicit emotion-processing task while listening to music or noise. Individual variation in mood induction was assessed before and after the task. Results showed mood improvement after music exposure in DRD2GG subjects and mood deterioration after noise exposure in GT subjects. Moreover, the music, as opposed to noise environment, decreased the striatal activity of GT subjects as well as the prefrontal activity of GG subjects while processing emotional faces. These findings suggest that genetic variability of dopamine receptors affects sound environment modulations of mood and emotion processing.