The application of nonlinear Dynamic Causal Modelling for fMRI in subjects at high genetic risk of schizophrenia.

Nonlinear Dynamic Causal Modelling (DCM) for fMRI provides computational modelling of gating mechanisms at the neuronal population level. It allows for estimations of connection strengths with nonlinear modulation within task-dependent networks. This paper presents an application of nonlinear DCM in subjects at high familial risk of schizophrenia performing the Hayling Sentence Completion Task (HSCT). We analysed scans of 19 healthy controls and 46 subjects at high familial risk of schizophrenia, which included 26 high risk subjects without psychotic symptoms and 20 subjects with psychotic symptoms. The activity-dependent network consists of the intra parietal cortex (IPS), inferior frontal gyrus (IFG), middle temporal gyrus (MTG), anterior cingulate cortex (ACC) and the mediodorsal (MD) thalamus. The connections between the MD thalamus and the IFG were gated by the MD thalamus. We used DCM to investigate altered connection strength of these connections. Bayesian Model Selection (BMS) at the group and family level was used to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging (BMA) was used to assess the connection strengths with the gating from the MD thalamus and the IFG. The nonlinear models provided the better explanation of the data. Furthermore, the BMA analysis showed significantly lower connection strength of the thalamocortical connection with nonlinear modulation from the MD thalamus in high risk subjects with psychotic symptoms and those who subsequently developed schizophrenia. These findings demonstrate that nonlinear DCM provides a method to investigate altered connectivity at the level of neural circuits. The reduced connection strength with thalamic gating may be a neurobiomarker implicated in the development of psychotic symptoms. This study suggests that nonlinear DCM could lead to new insights into functional and effective dysconnection at the network level in subjects at high familial risk.

Relationship between gyrification and functional connectivity of the prefrontal cortex in subjects at high genetic risk of schizophrenia.

Measures of cortical folding ('gyrification') and connectivity are both reported to be disrupted in schizophrenia. There are also reports that increases in prefrontal gyrification may be predictive of subsequent illness in individuals at familial risk of the disorder. Such measures therefore have important potential clinical relevance. The nature of the relationship between cortical morphology and underlying connectivity is however unclear. In the current study we sought to explore the relationship between measures of gyrification and functional connectivity in a cohort of individuals at high genetic risk for the disorder. The theoretical background is based on the hypothesis that increased gyrification index (GI) in the prefrontal cortex may reflect increased short range regional connectivity. The cohort comprised 68 young unaffected relatives of schizophrenia patients and 21 healthy controls. Cortical folding was assessed using an automated Gyrification Index method (A-GI). Participants performed the Hayling sentence completion paradigm in the scanner and measures of functional connectivity were assessed using a correlation based approach. In the high risk subjects significant positive associations were found between prefrontal GI and prefrontal lateral-medial connectivity, while a negative correlation was found between prefrontal GI and prefrontal-thalamic connectivity. These associations indicate that measures describing morphological features of the brain surface relate to measures of underlying functional connectivity in the high risk subjects. Correlations in high risk people were more pronounced than in control subjects. We suggest our previous finding of increased prefrontal gyrification may therefore relate to increased local short range prefrontal connectivity and reduced long range connectivity.