Grey matter volume alterations in patients with schizophrenia and unaffected siblings show region-specific effects of genetic risk and disease-related factors

Patients with schizophrenia (scz) display a pattern of grey matter abnormalities in the prefrontal cortex (PFC), the thalamus and the cerebellum, as shown by voxel-based morphometry studies. Furthermore, post-mortem studies indicate in patients neuronal loss in specific sub- regions of the thalamus such as the mediodorsal thalamic nucleus (MD). However, it is unclear to what extent these alterations in schizophrenia are associated with the genetic risk or with state-specific factors. The present study investigated the association between genetic risk for schizophrenia and grey matter volume abnormalities

Grey Matter Volume Patterns in Thalamic Nuclei are Associated with Familial Risk for Schizophrenia

Previous evidence suggests reduced thalamic grey matter volume (GMV) in patients with schizophrenia (SCZ). However, it is not considered an intermediate phenotype for schizophrenia, possibly because previous studies did not assess the contribution of individual thalamic nuclei and employed univariate statistics.Here,we hypothesized that multivariate statistics would reveal an association of GMV in different thalamic nuclei with familial risk for schizophrenia.We also hypothesized that accounting for the heterogeneity of thalamic GMV in healthy controls would improve the detection of subjects at familial risk for the disorder. We acquired MRI scans for 96 clinically stable SCZ, 55 non-affected siblings of patients with schizophrenia (SIB), and 249 HC. The thalamus was parceled into seven regions of interest (ROIs). After a canonical univariate analysis, we used GMV estimates of thalamic ROIs, together with total thalamic GMV and premorbid intelligence, as features in Random Forests to classify HC, SIB, and SCZ. Then, we computed aMisclassification Index for each individual and tested the improvement in SIB detection after excluding a subsample of HCmisclassified as patients. Random Forests discriminated SCZ from HC (accuracy = 81%) and SIB from HC (accuracy = 75%). Left anteromedial thalamic volumeswere significantly associatedwith bothmultivariate classifications (p b 0.05). Excluding HCmisclassified as SCZ improved greatly HC vs. SIB classification (Cohen's d=1.39). These findings suggest that multivariate statistics identify a familial background associated with thalamic GMV reduction in SCZ. They also suggest the relevance of inter-individual variability of GMV patterns for the discrimination of individuals at familial risk for the disorder