Multi-site reproducibility of prefrontal-hippocampal connectivity estimates by stochastic DCM.

This study examined the reproducibility of prefrontal-hippocampal connectivity estimates obtained by stochastic dynamic causal modeling (sDCM). 180 healthy subjects were measured by functional magnetic resonance imaging (fMRI) during a standard working memory N-Back task at three different sites (Mannheim, Bonn, Berlin; each with 60 participants). The reproducibility of regional activations in key regions for working memory (dorsolateral prefrontal cortex, DLPFC; hippocampal formation, HF) was evaluated using conjunction analyses across locations. These analyses showed consistent activation of right DLPFC and deactivation of left HF across all three different sites. The effective connectivity between DLPFC and HF was analyzed using a simple two-region sDCM. For each subject, we evaluated sixty-seven alternative sDCMs and compared their relative plausibility using Bayesian model selection (BMS). Across all locations, BMS consistently revealed the same winning model, with the 2-Back working memory condition as driving input to both DLPFC and HF and with a connection from DLPFC to HF. Statistical tests on the sDCM parameter estimates did not show any significant differences across the three sites. The consistency of both the BMS results and model parameter estimates indicates the reliability of sDCM in our paradigm. This provides a basis for future genetic and clinical studies using this approach

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

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

Structural Basis of Large-Scale Functional Connectivity in the Mouse

Translational neuroimaging requires approaches and techniques that can bridge between multiple different species and disease states. One candidate method that offers insights into the brain's functional connectivity (FC) is resting-state fMRI (rs-fMRI). In both humans and nonhuman primates, patterns of FC (often referred to as the functional connectome) have been related to the underlying structural connectivity (SC; also called the structural connectome). Given the recent rise in preclinical neuroimaging of mouse models, it is an important question whether the mouse functional connectome conforms to the underlying SC. Here, we compared FC derived from rs-fMRI in female mice with the underlying monosynaptic structural connectome as provided by the Allen Brain Connectivity Atlas. We show that FC between interhemispheric homotopic cortical and hippocampal areas, as well as in cortico-striatal pathways, emerges primarily via monosynaptic structural connections. In particular, we demonstrate that the striatum (STR) can be segregated according to differential rs-fMRI connectivity patterns that mirror monosynaptic connectivity with isocortex. In contrast, for certain subcortical networks, FC emerges along polysynaptic pathways as shown for left and right STR, which do not share direct anatomical connections, but high FC is putatively driven by a top-down cortical control. Finally, we show that FC involving cortico-thalamic pathways is limited, possibly confounded by the effect of anesthesia, small regional size, and tracer injection volume. These findings provide a critical foundation for using rs-fMRI connectivity as a translational tool to study complex brain circuitry interactions and their pathology due to neurological or psychiatric diseases across species.A comprehensive understanding of how the anatomical architecture of the brain, often referred to as the "connectome," corresponds to its function is arguably one of the biggest challenges for understanding the brain and its pathologies. Here, we use the mouse as a model for comparing functional connectivity (FC) derived from resting-state fMRI with gold standard structural connectivity measures based on tracer injections. In particular, we demonstrate high correspondence between FC measurements of cortico-cortical and cortico-striatal regions and their anatomical underpinnings. This work provides a critical foundation for studying the pathology of these circuits across mouse models and human patients

GABA-ergic Dynamics in Human Frontotemporal Networks Confirmed by Pharmaco-Magnetoencephalography.

To bridge the gap between preclinical cellular models of disease and in vivo imaging of human cognitive network dynamics, there is a pressing need for informative biophysical models. Here we assess dynamic causal models (DCM) of cortical network responses, as generative models of magnetoencephalographic observations during an auditory oddball roving paradigm in healthy adults. This paradigm induces robust perturbations that permeate frontotemporal networks, including an evoked 'mismatch negativity' response and transiently induced oscillations. Here, we probe GABAergic influences in the networks using double-blind placebo-controlled randomized-crossover administration of the GABA reuptake inhibitor, tiagabine (oral, 10 mg) in healthy older adults. We demonstrate the facility of conductance-based neural mass mean-field models, incorporating local synaptic connectivity, to investigate laminar-specific and GABAergic mechanisms of the auditory response. The neuronal model accurately recapitulated the observed magnetoencephalographic data. Using parametric empirical Bayes for optimal model inversion across both drug sessions, we identify the effect of tiagabine on GABAergic modulation of deep pyramidal and interneuronal cell populations. We found a transition of the main GABAergic drug effects from auditory cortex in standard trials to prefrontal cortex in deviant trials. The successful integration of pharmaco- magnetoencephalography with dynamic causal models of frontotemporal networks provides a potential platform on which to evaluate the effects of disease and pharmacological interventions.SIGNIFICANCE STATEMENT Understanding human brain function and developing new treatments require good models of brain function. We tested a detailed generative model of cortical microcircuits that accurately reproduced human magnetoencephalography, to quantify network dynamics and connectivity in frontotemporal cortex. This approach identified the effect of a test drug (GABA-reuptake inhibitor, tiagabine) on neuronal function (GABA-ergic dynamics), opening the way for psychopharmacological studies in health and disease with the mechanistic precision afforded by generative models of the brain

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Effective connectivity during working memory and resting states: A DCM study

Although the relationship between resting-state functional connectivity and task-related activity has been addressed, the relationship between task and resting-state directed or effective connectivity – and its behavioral concomitants – remains elusive. We evaluated effective connectivity under an N-back working memory task in 24 participants using stochastic dynamic causal modelling (DCM) of 7 T fMRI data. We repeated the analysis using resting-state data, from the same subjects, to model connectivity among the same brain regions engaged by the N-back task. This allowed us to: (i) examine the relationship between intrinsic (task-independent) effective connectivity during resting (Arest) and task states (Atask), (ii) cluster phenotypes of task-related changes in effective connectivity (Btask) across participants, (iii) identify edges (Btask) showing high inter-individual effective connectivity differences and (iv) associate reaction times with the similarity between Btaskand Arestin these edges. We found a strong correlation between Arestand Ataskover subjects but a marked difference between Btaskand Arest. We further observed a strong clustering of individuals in terms of Btask, which was not apparent in Arest. The task-related effective connectivity Btaskvaried highly in the edges from the parietal to the frontal lobes across individuals, so the three groups were clustered mainly by the effective connectivity within these networks. The similarity between Btaskand Arestat the edges from the parietal to the frontal lobes was positively correlated with 2-back reaction times. This result implies that a greater change in context-sensitive coupling – from resting-state connectivity – is associated with faster reaction times. In summary, task-dependent connectivity endows resting-state connectivity with a context sensitivity, which predicts the speed of information processing during the N-back task

Investigating the dynamic role of fluctuations in ongoing activity in the human brain

Traditionally, the focus in cognitive neuroscience has been on so-called evoked neural activity in response to certain stimuli or experiences. However, most of the brain’s activity is actually spontaneous and therefore not ascribed to the processing of a certain task or stimulus – or in other words, uncoupled to overt stimuli or motor outputs. In this thesis I investigated the functional role of spontaneous activity with a focus on its role in contextual changes ranging from recent experiences of individuals to trial-by-trial variability in a certain task. I studied the nature of ongoing activity from two perspectives: One looking at changes in the ongoing activity due to learning, and the other one looking at the predictive role of prestimulus activity using different methodologies, i.e. EEG and fMRI. Finally, I ventured into the realm of inter-individual differences and mind-wandering to investigate the relationship between ongoing activity, certain behavioural traits and neuronal connectivity