title: The application of nonlinear Dynamic Causal Modelling for fMRI in subjects at high genetic risk of schizophrenia.
abstract: 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.
authors: Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, UK. M.R.Dauvermann@sms.ed.ac.uk
authors: Maria R
authors: MR
authors: Dauvermann
authors: Heather C
authors: HC
authors: Whalley
authors: Liana
authors: L
authors: Romaniuk
authors: Vincent
authors: V
authors: Valton
authors: David G C
authors: DG
authors: Owens
authors: Eve C
authors: EC
authors: Johnstone
authors: Stephen M
authors: SM
authors: Lawrie
authors: Thomas W J
authors: TW
authors: Moorhead
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology; D003702:Delusions / Q000473:pathology / Q000523:psychology; D005260:Female; D020022:Genetic Predisposition to Disease; D006212:Hallucinations / Q000473:pathology / Q000523:psychology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D009434:Neural Pathways / Q000033:anatomy & histology / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology; D017711:Nonlinear Dynamics; D011597:Psychomotor Performance / Q000502:physiology; D012306:Risk; D012559:Schizophrenia / Q000235:genetics*; D012565:Schizophrenic Psychology; D013788:Thalamus / Q000473:pathology; D055815:Young Adult