Genetic study of the myelin oligodendrocyte glycoprotein (MOG) gene in schizophrenia

Schizophrenia (SCZ) is a neuropsychiatric disorder that affects approximately 1% of the general population. The human leukocyte antigen (HLA) system has been implicated in several genetic studies of SCZ. The myelin oligodendrocyte glycoprotein (MOG) gene, which is located close to the HLA region, is considered a candidate for SCZ due to its association with white matter abnormalities and its importance in mediating the complement cascade. Four polymorphisms in the MOG gene (CA)n (TAAA)n, and two intronic polymorphisms, C1334T and C10991T, were investigated for the possibility of association with SCZ using 111 SCZ proband and their families. We examined the transmission of the alleles of each of these polymorphisms with the transmission disequilibrium test. We did not observe significant evidence for biased transmission of alleles at the (CA)n (χ2 = 2.430, 6 df, P = 0.876) (TAAA)n (χ2 = 3.550, 5 df, P = 0.616), C1334T (χ2 = 0.040, 1 df, P = 0.841) and C10991T (χ2 = 0.154, 1 df, P = 0.695) polymorphisms. Overall haplotype analysis using the TRANSMIT program was also not significant (χ2 = 7.954, 9 df, P = 0.539). Furthermore, our results comparing mean age at onset in the genotype groups using the Kruskal–Wallis Test were not significant. Our case-control analyses (182 cases age-, sex- and ethnicity-matched with healthy controls) and combined z-score [(CA)n: z-score =−1.126, P = 0.130; (TAAA)n: z-score = −0.233, P = 0.408; C1334T: z-score = 0.703, P = 0.241; C10991T: z-score = 0.551, P = 0.291] were also not significant. Although our data are negative, the intriguing hypothesis for MOG in SCZ may warrant further investigation of this gene

Symptom changes in five dimensions of the Positive and Negative Syndrome Scale in refractory psychosis

Refractory psychosis units currently have little information regarding which symptoms profiles should be expected to respond to treatment. In the current study, we provide this information using structural equation modeling of Positive and Negative Syndrome Scale (PANSS) ratings at admission and discharge on a sample of 610 patients admitted to a treatment refractory psychosis program at a Canadian tertiary care unit between 1990 and 2011. The hypothesized five-dimensional structure of the PANSS fit the data well at both admission and discharge, and the latent variable scores are reported as a function of symptom dimension and diagnostic category. The results suggest that, overall, positive symptoms (POS) responded to treatment better than all other symptoms dimensions, but for the schizoaffective and bipolar groups, greater response on POS was observed relative to the schizophrenia and major depression groups. The major depression group showed the most improvement on negative symptoms and emotional distress, and the bipolar group showed the most improvement on disorganization. Schizophrenia was distinct from schizoaffective disorder in showing reduced treatment response on all symptom dimensions. These results can assist refractory psychosis units by providing information on how PANSS symptom dimensions respond to treatment and how this depends on diagnostic category

Predicting response to repetitive transcranial magnetic stimulation in patients with schizophrenia using structural magnetic resonance imaging: a multisite machine learning analysis

Background: The variability of responses to plasticity-inducing repetitive transcranial magnetic stimulation (rTMS) challenges its successful application in psychiatric care. No objective means currently exists to individually predict the patients' response to rTMS. Methods: We used machine learning to develop and validate such tools using the pre-treatment structural Magnetic Resonance Images (sMRI) of 92 patients with schizophrenia enrolled in the multisite RESIS trial (http://clinicaltrials.gov, NCT00783120): patients were randomized to either active (N = 45) or sham (N = 47) 10-Hz rTMS applied to the left dorsolateral prefrontal cortex 5 days per week for 21 days. The prediction target was nonresponse vs response defined by a ≥20% pre-post Positive and Negative Syndrome Scale (PANSS) negative score reduction. Results: Our models predicted this endpoint with a cross-validated balanced accuracy (BAC) of 85% (nonresponse/response: 79%/90%) in patients receiving active rTMS, but only with 51% (48%/55%) in the sham-treated sample. Leave-site-out cross-validation demonstrated cross-site generalizability of the active rTMS predictor despite smaller training samples (BAC: 71%). The predictive pre-treatment pattern involved gray matter density reductions in prefrontal, insular, medio-temporal, and cerebellar cortices, and increments in parietal and thalamic structures. The low BAC of 58% produced by the active rTMS predictor in sham-treated patients, as well as its poor performance in predicting positive symptom courses supported the therapeutic specificity of this brain pattern. Conclusions: Individual responses to active rTMS in patients with predominant negative schizophrenia may be accurately predicted using structural neuromarkers. Further multisite studies are needed to externally validate the proposed treatment stratifier and develop more personalized and biologically informed rTMS interventions