Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features

Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder(MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia(FES), 125 with MDD, and 237 demographically-matched healthy controls(HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with aone-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD.Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.

Morphological changes in gray matter volume correlate with catechol-O-methyl transferase gene Val158Met polymorphism in first-episode treatment-nave patients with schizophrenia

The catechol-O-methyltransferase（COMT） gene is a schizophrenia susceptibility gene. A common functional polymorphism of this gene,Val158/158 Met,has been proposed to influence gray matter volume（GMV）. However,the effects of this polymorphism on cortical thickness/surface area in schizophrenic patients are less clear. In this study,we explored the relationship between the Val158 Met polymorphism of the COMT gene and the GMV/ cortical thickness/cortical surface area in 150 firstepisode treatment-nave patients with schizophrenia and 100 healthy controls. Main effects of diagnosis were found for GMV in the cerebellum and the visual,medial temporal,parietal,and middle frontal cortex. Patients with schizophrenia showed reduced GMVs in these regions. And main effects of genotype were detected for GMV in the left superior frontal gyrus. Moreover,a diagnosis × genotype interaction was found for the GMV of the left precuneus,and the effect of the COMT gene on GMV was due mainly to cortical thickness rather than cortical surface area. In addition,a pattern ofincreased GMV in the precuneus with increasing Met dose found in healthy controls was lost in patients with schizophrenia. These findings suggest that the COMTMet variant is associated with the disruption of dopaminergic influence on gray matter in schizophrenia,and the effect of the COMT gene on GMV in schizophrenia is mainly due to changes in cortical thickness rather than in cortical surface area.

Testing the role of genetic variation of the MC4R gene in Chinese population in antipsychotic-induced metabolic disturbance

Antipsychotic-induced metabolic disturbance(AIMD) is a common adverse effect of antipsychotics with genetics partly underpinning variation in susceptibility among schizophrenia patients. Melanocortin4 receptor(MC4 R) gene, one of the candidate genes for AIMD, has been under-studied in the Chinese patients. We conducted a pharmacogenetic study in a large cohort of Chinese patients with schizophrenia. In this study, we investigated the genetic variation of MC4 R in Chinese population by genotyping two SNPs(rs489693 and rs17782313) in 1,991 Chinese patients and examined association of these variants with the metabolic effects that were often observed to be related to AIMD. Metabolic measures, including body mass index(BMI), waist circumference(WC), glucose, triglyceride, high-density lipoprotein(HDL), and low-density lipoprotein(LDL) levels were assessed at baseline and after 6-week antipsychotic treatment. We found that interaction of SNP×medication status(drug-na?ve/medicated) was significantly associated with BMI, WC, and HDL change %, respectively. Both SNPs were significantly associated with baseline BMI and WC in the medicated group. Moderate association of rs489693 with WC, Triglyceride, and HDL change % were observed in the whole sample. In the drug-na?ve group, we found recessive effects of rs489693 on BMI gain more than 7%, WC and Triglyceride change %, with AA incurring more metabolic adverse effects. In conclusion, the association between rs489693 and the metabolic measures is ubiquitous but moderate. Rs17782313 is less involved in AIMD. Two SNPs confer risk of AIMD to patients treated with different antipsychotics in a similar way.

Interaction Between Variations in Dopamine D2 and Serotonin 2A Receptor is Associated with Short-Term Response to Antipsychotics in Schizophrenia

Dear Editor,Schizophrenia is a chronic and debilitating brain disorder,which has a strong genetic component with heritability ranging from 66%to 85%[1,2].Currently,antipsychotic drugs remain the most effective treatment for the psychotic symptoms of schizophrenia[3].Because of the severe sideeffects of first-generation antipsychotics(FGAs),secondgeneration antipsychotics(SGAs)have become more widely used in the treatment of schizophrenia.