Of Genes and Patients: Stochastic Dynamic Causal Modellingof the Prefrontal-Hippocampal Network

Abstract

The research field of functional magnetic resonance imaging (fMRI) has made possible a remarkable progress in the understanding of the human brain enabling neuroscientists to study spatio-temporal alterations in the healthy and the diseased brain. While current theories of schizophrenia stress the critical role that plays aberrant connectivity among brain regions, other theories point towards the crucial role that plays functional excitation-inhibition (E-I) balance. Indeed, recent neuroscientific research has revealed increasing evidence that taking functional brain connectivity into account is essential to understand how the human brain works, and many studies have reviewed that serious behavioural impairments in mental disorders such as schizophrenia result from increases in the functional (E-I) balance within the neural microcircuitry. Particularly, the connection between the dorsolateral prefrontal cortex (DLPFC) and the hippocampal formation (HF) during working memory (WM) was found to be increased in carriers of schizophrenia risk genes and patients. However, less is known about causality, i.e. which region drives the altered connection. Stochastic Dynamic Causal Modelling (sDCM) is a novel mathematical algorithm for studying the causal connectivity among higher cognitive brain regions from fMRI data. The main purpose of this study is to identify alterations on genetic risk carriers and patients from the DLPFC-HF network estimated with sDCM and describe how these alterations have an impact on the behavior. Over the study, we strive to give to the sDCM parameter estimates a neurobiological explanation by linking the concepts of causal connectivity with functional (E-I) balance. In this work, we applied this methodology in two samples by constructing a systematic set of sDCMs describing interactions between right DLPFC and left HF. In a first sample, 180 healthy subjects were measured by fMRI during a standard working memory N-Back task at three different sites (Mannheim, Bonn, Berlin; each with 60 participants). Bayes Model Selection (BMS) revealed the same causal pattern or winning model across the three sites, with the 2-Back working memory condition as driving input to both DLPFC and HF and with a connection from DLPFC to HF. Furthermore, a genome-wide risk variant for schizophrenia: ZNF804A (rs1344706), showed a strong impact on the DLPFC-HF network. On the one hand, risk homozygotes showed higher effective connectivity or higher functional (E-I) balance between DLPFC and HF. On the other hand, risk allele carriers showed higher functional (E-I) balance on the self-connection in the DLPFC. In a second sample, 33 schizophrenia patients were measured by fMRI during the same working memory N-Back task. We pair-wise matched healthy volunteers of the first sample and patients and applied the same methodology. BMS revealed the same winning model in patients but sDCM parameter estimates differed significantly between groups. Patients showed higher functional (E-I) balance on both self-connections in comparison to healthy volunteers. In summary, we observed that risk allele carriers and patients have a higher functional (E-I) balance within the DLPFC-HF network. In view of these research findings, we hypothesized a possible biological functioning of ZNF804A (rs1344706) on the DLPFC-HF network and suggested a mechanistic model for explaining the underlying neurobiology of schizophrenia within this network. Then, we reported causal relations between sDCM parameter estimates and behavior in terms of functional (E-I) balance in both samples. On the one hand, we observed that risk allele carriers and patients require lower functional (E-I) balance on the DLPFC-HF network in order to achieve the best performance during the task. On the other hand, we found that healthy volunteers require higher functional (E-I) balance on the network in order to achieve the optimal behavior. This study investigated the applicability of computational models like sDCM to establish the functional significance of specific genetic polymorphisms for schizophrenia and identify causal mechanisms associated with the disease in relation to the underlying neurobiology and behavior. In forthcoming studies, we plan to investigate whether subject-specific directed connections strengths between DLPFC and HF, and genotype, contain sufficiently rich information to enable accurate predictions of behavior. In order to study how temporal patterns in the neuronal ensembles and genotype convey robust information about behavior, multivariate regressors or statistical decoding algorithms will be used in both samples.