Alteration in resting-state effective connectivity within the Papez circuit in Presbycusis

Previous studies have suggested that the Papez circuit may be involved in the cognitive impairment observed after hearing loss in presbycusis patients, yet relatively little is known about the pattern of changes in effective connectivity within the circuit. The aim of this study was to investigate abnormal alterations in resting-state effective connectivity within the Papez circuit and their association with cognitive decline in presbycusis patients. The spectral dynamic causal modelling (spDCM) approach was used for resting-state effective connectivity analysis in 61 presbycusis patients and 52 healthy controls (HCs) within the Papez circuit. The hippocampus (HPC), mamillary body (MB), anterior thalamic nuclei (ATN), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), entorhinal cortex (ERC), subiculum (Sub) and parahippocampal gyrus (PHG) were selected as the regions of interest (ROIs). The fully connected model difference in effective connectivity between the two groups was assessed, and the correlation between effective connectivity alteration and cognitive scale was analysed. We found that presbycusis patients demonstrated decreased effective connectivity from MB, PCC, and Sub to ACC relative to HCs, whereas higher effective connectivity strength was shown from HPC to MB, from ATN to PHG and from PHG to Sub. The effective connectivity from PHG to Sub was significantly negatively correlated with the complex figure test (CFT)-delay score (rho = -0.259, p = 0.044). The results support and reinforce the role of abnormal effective connectivity within the Papez circuit in the pathophysiology of presbycusis-related cognitive impairment and reveal its potential as a novel imaging marker.

Altered habenular connectivity in chronic low back pain: An fMRI and machine learning study

The habenula has been implicated in the pathogenesis of pain and analgesia, while evidence concerning its function in chronic low back pain (cLBP) is sparse. This study aims to investigate the resting-state functional connectivity (rsFC) and effective connectivity of the habenula in 52 patients with cLBP and 52 healthy controls (HCs) and assess the feasibility of distinguishing cLBP from HCs based on connectivity by machine learning methods. Our results indicated significantly enhanced rsFC of the habenula-left superior frontal cortex (SFC), habenula-right thalamus, and habenula-bilateral insular pathways as well as decreased rsFC of the habenula-pons pathway in cLBP patients compared to HCs. Dynamic causal modelling revealed significantly enhanced effective connectivity from the right thalamus to right habenula in cLBP patients compared with HCs. RsFC of the habenula-SFC was positively correlated with pain intensities and Hamilton Depression scores in the cLBP group. RsFC of the habenula-right insula was negatively correlated with pain duration in the cLBP group. Additionally, the combination of the rsFC of the habenula-SFC, habenula-thalamus, and habenula-pons pathways could reliably distinguish cLBP patients from HCs with an accuracy of 75.9% by support vector machine, which was validated in an independent cohort (N = 68, accuracy = 68.8%, p = .001). Linear regression and random forest could also distinguish cLBP and HCs in the independent cohort (accuracy = 73.9 and 55.9%, respectively). Overall, these findings provide evidence that cLBP may be associated with abnormal rsFC and effective connectivity of the habenula, and highlight the promise of machine learning in chronic pain discrimination.

Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders

INTRODUCTION

Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear.

OBJECTS

The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state.

MATERIALS AND METHODS

The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients' clinical characteristics.

RESULTS

For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the "triple networks" system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores.

CONCLUSION

Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease.

Modulation of the brain's core-self network by self-appraisal processes

The 'core' regions of the default mode network (DMN) - the medial prefrontal cortex (MPFC), the posterior cingulate cortex (PCC), and inferior parietal lobules (IPL) - show consistent involvement across mental states that involve self-oriented processing. Precisely how these regions interact in support of such processes remains an important unanswered question. In the current functional magnetic resonance imaging (fMRI) study, we examined dynamic interactions of the 'core-self' DMN regions during two forms of self-referential cognition: direct self-appraisal (thinking about oneself) and reflected self-appraisal (thinking about oneself from a third-person perspective). One-hundred and eleven participants completed our dual self-appraisal task during fMRI, and general linear models were used to characterize common and distinct neural responses to these conditions. Informed by these results, we then applied dynamic causal modelling to examine causal interactions among the 'core-self' regions, and how they were specifically modulated under the influence of direct and reflected self-appraisal. As a primary observation, this network modelling revealed a distinct inhibitory influence of the left IPL on the PCC during reflected compared to direct self-appraisal, which was accompanied by evidence of greater activation in both regions during the reflected self-appraisal condition. We suggest that the greater engagement posterior DMN regions during reflected self-appraisal is a function of the higher-order processing needed for this form of self-appraisal, with the left IPL supporting abstract self-related processes including episodic memory retrieval and shifts of perspective. Overall, we show that core DMN regions interact in functionally unique ways in support of self-referential processes, even when these processes are inter-related. Further characterization of DMN functional interactions across self-related mental states is likely to inform a deeper understanding of how this brain network orchestrates the self.

Cognition Without Neural Representation: Dynamics of a Complex System

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).

Altered Amygdala-prefrontal Connectivity in Chronic Nonspecific Low Back Pain: Resting-state fMRI and Dynamic Causal Modelling Study

Chronic nonspecific low back pain (cNLBP) is a leading contributor to disease burden worldwide that is difficult to treat due to its nonspecific aetiology and complexity. The amygdala is a complex of structurally and functionally heterogeneous nuclei that serve as a key neural substrate for the interactions between pain and negative affective states. However, whether the functions of amygdalar subcomponents are differentially altered in cNLBP remains unknown. Little attention has focused on effective connectivity of the amygdala with the cortex in cNLBP. In this study, thirty-three patients with cNLBP and 33 healthy controls (HCs) were included. Resting-state functional connectivity (rsFC) and effective connectivity of the amygdala and its subregions were examined. Our results showed that the patient group exhibited significantly greater rsFC between the left amygdala and left dorsal medial prefrontal cortex (mPFC), which was negatively correlated with pain intensity ratings. Subregional analyses suggested a difference located at the superficial nuclei of the amygdala. Dynamic causal modelling revealed significantly lower effective connectivity from the left amygdala to the dorsal mPFC in patients with cNLBP than in HCs. Both groups exhibited stronger effective connectivity from the left amygdala to the right amygdala. In summary, these findings not only suggested altered rsFC of the amygdala-mPFC pathway in cNLBP but also implicated an abnormal direction of information processing between the amygdala and mPFC in these patients. Our results further highlight the involvement of the amygdala in the neuropathology of cNLBP.

Altered effective connectivity of the extended face processing system in depression and its association with treatment response: findings from the YoDA-C randomized controlled trial

BACKGROUND

Depression is commonly associated with fronto-amygdala dysfunction during the processing of emotional face expressions. Interactions between these regions are hypothesized to contribute to negative emotional processing biases and as such have been highlighted as potential biomarkers of treatment response. This study aimed to investigate depression associated alterations to directional connectivity and assess the utility of these parameters as predictors of treatment response.

METHODS

Ninety-two unmedicated adolescents and young adults (mean age 20.1; 56.5% female) with moderate-to-severe major depressive disorder and 88 healthy controls (mean age 19.8; 61.4% female) completed an implicit emotional face processing fMRI task. Patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or placebo. Using dynamic causal modelling, we examined functional relationships between six brain regions implicated in emotional face processing, comparing both patients and controls and treatment responders and non-responders.

RESULTS

Depressed patients demonstrated reduced inhibition from the dlPFC to vmPFC and reduced excitation from the dlPFC to amygdala during sad expression processing. During fearful expression processing patients showed reduced inhibition from the vmPFC to amygdala and reduced excitation from the amygdala to dlPFC. Response was associated with connectivity from the amygdala to dlPFC during sad expression processing and amygdala to vmPFC connectivity during fearful expression processing.

CONCLUSIONS

Our study clarifies the nature of face processing network alterations in adolescents and young adults with depression, highlighting key interactions between the amygdala and prefrontal cortex. Moreover, these findings highlight the potential utility of these interactions in predicting treatment response.

A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies.

 Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics.

To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings.

Neural effective connectivity explains subjective fatigue in stroke

Persistent fatigue is a major debilitating symptom in many psychiatric and neurological conditions, including stroke. Post-stroke fatigue has been linked to low corticomotor excitability. Yet, it remains elusive as to what the neuronal mechanisms are that underlie motor cortex excitability and chronic persistence of fatigue.

In this cross-sectional observational study, in two experiments we examined a total of 59 non-depressed stroke survivors with minimal motoric and cognitive impairments using ‘resting-state’ MRI and single- and paired-pulse transcranial magnetic stimulation.

In the first session of Experiment 1, we assessed resting motor thresholds—a typical measure of cortical excitability—by applying transcranial magnetic stimulation to the primary motor cortex (M1) and measuring motor-evoked potentials in the hand affected by stroke. In the second session, we measured their brain activity with resting-state MRI to assess effective connectivity interactions at rest. In Experiment 2 we examined effective inter-hemispheric connectivity in an independent sample of patients using paired-pulse transcranial magnetic stimulation. We also assessed the levels of non-exercise induced, persistent fatigue using Fatigue Severity Scale (FSS-7), a self-report questionnaire that has been widely applied and validated across different conditions. We used spectral dynamic causal modelling in Experiment 1 and paired-pulse transcranial magnetic stimulation in Experiment 2 to characterize how neuronal effective connectivity relates to self-reported post-stroke fatigue. In a multiple regression analysis, we used the balance in inhibitory connectivity between homologue regions in M1 as the main predictor, and have included lesioned hemisphere, resting motor threshold and levels of depression as additional predictors.

Our novel index of inter-hemispheric inhibition balance was a significant predictor of post-stroke fatigue in Experiment 1 (β = 1.524, P = 7.56 × 10⁻⁵, confidence interval: 0.921 to 2.127) and in Experiment 2 (β = 0.541, P = 0.049, confidence interval: 0.002 to 1.080). In Experiment 2, depression scores and corticospinal excitability, a measure associated with subjective fatigue, also significantly accounted for variability in fatigue.

We suggest that the balance in inter-hemispheric inhibitory effects between primary motor regions can explain subjective post-stroke fatigue. Findings provide novel insights into neural mechanisms that underlie persistent fatigue.

Persistent dependent behaviour is accompanied by dynamic switching between the ventral and dorsal striatal connections in internet gaming disorder

Cross-sectional studies have suggested that functional heterogeneity within the striatum in individuals with addictive behaviours may involve the transition from ventral to dorsal partitions; however, due to limitations of the cross-sectional design, whether the contribution of this transition to addiction was confused by individual differences remains unclear, especially for internet gaming disorder (IGD). Longitudinal functional magnetic resonance imaging (fMRI) data from 22 IGD subjects and 18 healthy controls were collected at baseline and more than 6 months later. We examined the connectivity features of subregions within the striatum between these two scans. Based on the results, we further performed dynamic causal modelling to explore the directional effect between regions and used these key features for data classification in machine learning to test the replicability of the results. Compared with controls, IGD subjects exhibited decreased functional connectivity between the left dorsal striatum (putamen) and the left insula, whereas connectivity between the right ventral striatum (nucleus accumbens [Nacc]) and the left insula was relatively stable over time. An inhibitory effective connectivity from the left putamen to the right Nacc was found in IGD subjects during the follow-up scan. Using the above features, the classification accuracy of the training model developed with the follow-up was better than that of the model based on the initial scan. Persistent IGD status was accompanied by a switch in the locus of control within the striatum, which provided new insights into association between IGD and drug addiction.

Disruptions in Effective Connectivity within and between Default Mode Network and Anterior Forebrain Mesocircuit in Prolonged Disorders of Consciousness

Recent research indicates prolonged disorders of consciousness (PDOC) result from structural and functional impairments to key cortical and subcortical networks, including the default mode network (DMN) and the anterior forebrain mesocircuit (AFM). However, the specific mechanisms which underpin such impairments remain unknown. It is known that disruptions in the striatal-pallidal pathway can result in the over inhibition of the thalamus and lack of excitation to the cortex that characterizes PDOC. Here, we used spectral dynamic causal modelling and parametric empirical Bayes on rs-fMRI data to assess whether DMN changes in PDOC are caused by disruptions in the AFM. PDOC patients displayed overall reduced coupling within the AFM, and specifically, decreased self-inhibition of the striatum, paired with reduced coupling from striatum to thalamus. This led to loss of inhibition from AFM to DMN, mostly driven by posterior areas including the precuneus and inferior parietal cortex. In turn, the DMN showed disruptions in self-inhibition of the precuneus and medial prefrontal cortex. Our results provide support for the anterior mesocircuit model at the subcortical level but highlight an inhibitory role for the AFM over the DMN, which is disrupted in PDOC.

Second waves, social distancing, and the spread of COVID-19 across the USA

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several instantiations of this (epidemic) model to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity-and the exchange of people between regions-and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.

GABAergic cortical network physiology in frontotemporal lobar degeneration

The clinical syndromes caused by frontotemporal lobar degeneration are heterogeneous, including the behavioural variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy. Although pathologically distinct, they share many behavioural, cognitive and physiological features, which may in part arise from common deficits of major neurotransmitters such as γ-aminobutyric acid (GABA). Here, we quantify the GABAergic impairment and its restoration with dynamic causal modelling of a double-blind placebo-controlled crossover pharmaco-magnetoencephalography study. We analysed 17 patients with bvFTD, 15 patients with progressive supranuclear palsy, and 20 healthy age- and gender-matched controls. In addition to neuropsychological assessment and structural MRI, participants undertook two magnetoencephalography sessions using a roving auditory oddball paradigm: once on placebo and once on 10 mg of the oral GABA reuptake inhibitor tiagabine. A subgroup underwent ultrahigh-field magnetic resonance spectroscopy measurement of GABA concentration, which was reduced among patients. We identified deficits in frontotemporal processing using conductance-based biophysical models of local and global neuronal networks. The clinical relevance of this physiological deficit is indicated by the correlation between top-down connectivity from frontal to temporal cortex and clinical measures of cognitive and behavioural change. A critical validation of the biophysical modelling approach was evidence from parametric empirical Bayes analysis that GABA levels in patients, measured by spectroscopy, were related to posterior estimates of patients’ GABAergic synaptic connectivity. Further evidence for the role of GABA in frontotemporal lobar degeneration came from confirmation that the effects of tiagabine on local circuits depended not only on participant group, but also on individual baseline GABA levels. Specifically, the phasic inhibition of deep cortico-cortical pyramidal neurons following tiagabine, but not placebo, was a function of GABA concentration. The study provides proof-of-concept for the potential of dynamic causal modelling to elucidate mechanisms of human neurodegenerative disease, and explains the variation in response to candidate therapies among patients. The laminar- and neurotransmitter-specific features of the modelling framework, can be used to study other treatment approaches and disorders. In the context of frontotemporal lobar degeneration, we suggest that neurophysiological restoration in selected patients, by targeting neurotransmitter deficits, could be used to bridge between clinical and preclinical models of disease, and inform the personalized selection of drugs and stratification of patients for future clinical trials.

Second waves, social distancing, and the spread of COVID-19 across the USA

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several instantiations of this (epidemic) model to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity-and the exchange of people between regions-and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.

Expecting social punishment facilitates control over a decision under uncertainty by recruiting medial prefrontal cortex

In many decision-making situations, sub-optimal choices are increased by uncertainty. However, when wrong choices could lead to social punishment, such as blame, people might try to improve their performance by minimizing sub-optimal choices, which could be achieved by increasing the subjective cost of errors, thereby globally reducing decision noise or reducing an uncertainty-induced component of decision noise. In this functional magnetic resonance imaging (fMRI) study, 46 participants performed a choice task in which the probability of a correct choice with a given cue and the conditional probability of blame feedback (by making an incorrect choice) changed continuously. By comparing computational models of behaviour, we found that participants optimized their performance by preferentially reducing a component of decision noise associated with uncertainty. Simultaneously, expecting blame significantly deteriorated participants' mood. Model-based fMRI analyses and dynamic causal modelling indicate that the optimization mechanism based on the expectation of being blamed would be controlled by a neural circuit centred on the right medial prefrontal cortex. These results show novel behavioural and neural mechanisms regarding how humans optimize uncertain decisions under the expectation of being blamed.

Effective immunity and second waves: a dynamic causal modelling study

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.

Effective immunity and second waves: a dynamic causal modelling study

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.

Dynamic causal modelling of COVID-19

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

Effective brain connectivity at rest is associated with choice-induced preference formation

Preferences can change as a consequence of making hard decisions whereby the value of chosen options increases and the value of rejected options decreases. Such choice-induced preference changes have been associated with brain areas detecting choice conflict (anterior cingulate cortex, ACC), updating stimulus value (dorsolateral prefrontal cortex, dlPFC) and supporting memory of stimulus value (hippocampus and ventromedial prefrontal cortex, vmPFC). Here we investigated whether resting-state neuronal activity within these regions is associated with the magnitude of individuals' preference updates. We fitted a dynamic causal model (DCM) to resting-state neuronal activity in the spectral domain (spDCM) and estimated the causal connectivity among core regions involved in preference formation following hard choices. The extent of individuals' choice-induced preference changes were found to be associated with a diminished resting-state excitation between the left dlPFC and the vmPFC, whereas preference consistency was related to a higher resting-state excitation from the ACC to the left hippocampus and vmPFC. Our results point to a model of preference formation during which the dynamic network configurations between left dlPFC, ACC, vmPFC and left hippocampus at rest are linked to preference change or stability.

Second waves, social distancing, and the spread of COVID-19 across America

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity-and the exchange of people between regions-and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.

Dynamic causal modelling of COVID-19

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

Structure learning in coupled dynamical systems and dynamic causal modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill-posed problem that commonly arises when modelling real-world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of network architectures-and implicit coupling functions-in terms of their Bayesian model evidence. These methods are collectively referred to as dynamic causal modelling. We focus on a relatively new approach that is proving remarkably useful, namely Bayesian model reduction, which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.

Dynamic Causal Modelling of Active Vision

In this paper, we draw from recent theoretical work on active perception, which suggests that the brain makes use of an internal (i.e., generative) model to make inferences about the causes of sensations. This view treats visual sensations as consequent on action (i.e., saccades) and implies that visual percepts must be actively constructed via a sequence of eye movements. Oculomotor control calls on a distributed set of brain sources that includes the dorsal and ventral frontoparietal (attention) networks. We argue that connections from the frontal eye fields to ventral parietal sources represent the mapping from “where”, fixation location to information derived from “what” representations in the ventral visual stream. During scene construction, this mapping must be learned, putatively through changes in the effective connectivity of these synapses. Here, we test the hypothesis that the coupling between the dorsal frontal cortex and the right temporoparietal cortex is modulated during saccadic interrogation of a simple visual scene. Using dynamic causal modeling for magnetoencephalography with (male and female) human participants, we assess the evidence for changes in effective connectivity by comparing models that allow for this modulation with models that do not. We find strong evidence for modulation of connections between the two attention networks; namely, a disinhibition of the ventral network by its dorsal counterpart.

SIGNIFICANCE STATEMENT This work draws from recent theoretical accounts of active vision and provides empirical evidence for changes in synaptic efficacy consistent with these computational models. In brief, we used magnetoencephalography in combination with eye-tracking to assess the neural correlates of a form of short-term memory during a dot cancellation task. Using dynamic causal modeling to quantify changes in effective connectivity, we found evidence that the coupling between the dorsal and ventral attention networks changed during the saccadic interrogation of a simple visual scene. Intuitively, this is consistent with the idea that these neuronal connections may encode beliefs about “what I would see if I looked there”, and that this mapping is optimized as new data are obtained with each fixation.

Structural and effective brain connectivity underlying biological motion detection

The perception of actions underwrites a wide range of socio-cognitive functions. Previous neuroimaging and lesion studies identified several components of the brain network for visual biological motion (BM) processing, but interactions among these components and their relationship to behavior remain little understood. Here, using a recently developed integrative analysis of structural and effective connectivity derived from high angular resolution diffusion imaging (HARDI) and functional magnetic resonance imaging (fMRI), we assess the cerebro-cerebellar network for processing of camouflaged point-light BM. Dynamic causal modeling (DCM) informed by probabilistic tractography indicates that the right superior temporal sulcus (STS) serves as an integrator within the temporal module. However, the STS does not appear to be a "gatekeeper" in the functional integration of the occipito-temporal and frontal regions: The fusiform gyrus (FFG) and middle temporal cortex (MTC) are also connected to the right inferior frontal gyrus (IFG) and insula, indicating multiple parallel pathways. BM-specific loops of effective connectivity are seen between the left lateral cerebellar lobule Crus I and right STS, as well as between the left Crus I and right insula. The prevalence of a structural pathway between the FFG and STS is associated with better BM detection. Moreover, a canonical variate analysis shows that the visual sensitivity to BM is best predicted by BM-specific effective connectivity from the FFG to STS and from the IFG, insula, and STS to the early visual cortex. Overall, the study characterizes the architecture of the cerebro-cerebellar network for BM processing and offers prospects for assessing the social brain.

Working Memory-Related Effective Connectivity in Huntington's Disease Patients

Huntington’s disease (HD) is a genetically caused neurodegenerative disorder characterized by heterogeneous motor, psychiatric, and cognitive symptoms. Although motor symptoms may be the most prominent presentation, cognitive symptoms such as memory deficits and executive dysfunction typically co-occur. We used functional magnetic resonance imaging (fMRI) and task fMRI-based dynamic causal modeling (DCM) to evaluate HD-related changes in the neural network underlying working memory (WM). Sixty-four pre-symptomatic HD mutation carriers (preHD), 20 patients with early manifest HD symptoms (earlyHD), and 83 healthy control subjects performed an n-back fMRI task with two levels of WM load. Effective connectivity was assessed in five predefined regions of interest, comprising bilateral inferior parietal cortex, left anterior cingulate cortex, and bilateral dorsolateral prefrontal cortex. HD mutation carriers performed less accurately and more slowly at high WM load compared with the control group. While between-group comparisons of brain activation did not reveal differential recruitment of the cortical WM network in mutation carriers, comparisons of brain connectivity as identified with DCM revealed a number of group differences across the whole WM network. Most strikingly, we observed decreasing connectivity from several regions toward right dorsolateral prefrontal cortex (rDLPFC) in preHD and even more so in earlyHD. The deterioration in rDLPFC connectivity complements results from previous studies and might mirror beginning cortical neural decline at premanifest and early manifest stages of HD. We were able to characterize effective connectivity in a WM network of HD mutation carriers yielding further insight into patterns of cognitive decline and accompanying neural deterioration.

Ion channels in EEG: isolating channel dysfunction in NMDA receptor antibody encephalitis

See Roberts and Breakspear (doi:10.1093/brain/awy136) for a scientific commentary on this article.Neurological and psychiatric practice frequently lack diagnostic probes that can assess mechanisms of neuronal communication non-invasively in humans. In N-methyl-d-aspartate (NMDA) receptor antibody encephalitis, functional molecular assays are particularly important given the presence of NMDA antibodies in healthy populations, the multifarious symptomology and the lack of radiological signs. Recent advances in biophysical modelling techniques suggest that inferring cellular-level properties of neural circuits from macroscopic measures of brain activity is possible. Here, we estimated receptor function from EEG in patients with NMDA receptor antibody encephalitis (n = 29) as well as from encephalopathic and neurological patient controls (n = 36). We show that the autoimmune patients exhibit distinct fronto-parietal network changes from which ion channel estimates can be obtained using a microcircuit model. Specifically, a dynamic causal model of EEG data applied to spontaneous brain responses identifies a selective deficit in signalling at NMDA receptors in patients with NMDA receptor antibody encephalitis but not at other ionotropic receptors. Moreover, though these changes are observed across brain regions, these effects predominate at the NMDA receptors of excitatory neurons rather than at inhibitory interneurons. Given that EEG is a ubiquitously available clinical method, our findings suggest a unique re-purposing of EEG data as an assay of brain network dysfunction at the molecular level.

Modulation of task-related cortical connectivity in the acute and subacute phase after stroke

The functional relevance of cortical reorganization post-stroke is still not well understood. In this study, we investigated task-specific modulation of cortical connectivity between neural oscillations in key motor regions during the early phase after stroke. EEG and EMG recordings were examined from 15 patients and 18 controls during a precision grip task using the affected hand. Each patient attended two sessions in the acute and subacute phase (median of 3 and 34 days) post-stroke. Dynamic causal modelling (DCM) for induced responses was used to investigate task-specific modulations of oscillatory couplings in a bilateral network comprising supplementary motor area (SMA), dorsal premotor cortex (PMd) and primary motor cortex (M1). Fourteen models were constructed for each subject, and the input induced by the experimental manipulation (task) was set to inferior parietal lobule (IPL). Bayesian model selection favoured a fully connected model. A reduced coupling from SMA and intact M1 in the γ-band (31-48 Hz) to lesioned M1 in the β-band (15-30 Hz) was observed in patients in the acute phase compared to controls. Behavioural performance improved significantly in the subacute phase, while an increased positive coupling from intact PMd to lesioned M1 and a less negative modulation from lesioned M1 to intact M1 were observed for patients compared to controls both from the γ-band to the β-band. We infer that the observed differences in cross-frequency cortical interactions are important for functional recovery.

Dopamine controls Parkinson's tremor by inhibiting the cerebellar thalamus

Parkinson's resting tremor is related to altered cerebral activity in the basal ganglia and the cerebello-thalamo-cortical circuit. Although Parkinson's disease is characterized by dopamine depletion in the basal ganglia, the dopaminergic basis of resting tremor remains unclear: dopaminergic medication reduces tremor in some patients, but many patients have a dopamine-resistant tremor. Using pharmacological functional magnetic resonance imaging, we test how a dopaminergic intervention influences the cerebral circuit involved in Parkinson's tremor. From a sample of 40 patients with Parkinson's disease, we selected 15 patients with a clearly tremor-dominant phenotype. We compared tremor-related activity and effective connectivity (using combined electromyography-functional magnetic resonance imaging) on two occasions: ON and OFF dopaminergic medication. Building on a recently developed cerebral model of Parkinson's tremor, we tested the effect of dopamine on cerebral activity associated with the onset of tremor episodes (in the basal ganglia) and with tremor amplitude (in the cerebello-thalamo-cortical circuit). Dopaminergic medication reduced clinical resting tremor scores (mean 28%, range -12 to 68%). Furthermore, dopaminergic medication reduced tremor onset-related activity in the globus pallidus and tremor amplitude-related activity in the thalamic ventral intermediate nucleus. Network analyses using dynamic causal modelling showed that dopamine directly increased self-inhibition of the ventral intermediate nucleus, rather than indirectly influencing the cerebello-thalamo-cortical circuit through the basal ganglia. Crucially, the magnitude of thalamic self-inhibition predicted the clinical dopamine response of tremor. Dopamine reduces resting tremor by potentiating inhibitory mechanisms in a cerebellar nucleus of the thalamus (ventral intermediate nucleus). This suggests that altered dopaminergic projections to the cerebello-thalamo-cortical circuit have a role in Parkinson's tremor.aww331media15307619934001.

Neuronal interactions between mentalising and action systems during indirect request processing

Human communication relies on the ability to process linguistic structure and to map words and utterances onto our environment. Furthermore, as what we communicate is often not directly encoded in our language (e.g. in the case of irony, jokes or indirect requests), we need to extract additional cues to infer the beliefs and desires of our conversational partners. Although the functional interplay between language and the ability to mentalise has been discussed in theoretical accounts in the past, the neurobiological underpinnings of these dynamics are currently not well understood. Here, we address this issue using functional imaging (fMRI). Participants listened to question-reply dialogues. In these dialogues, a reply is interpreted as a direct reply, an indirect reply or a request for action, depending on the question. We show that inferring meaning from indirect replies engages parts of the mentalising network (mPFC) while requests for action also activate the cortical motor system (IPL). Subsequent connectivity analysis using Dynamic Causal Modelling (DCM) revealed that this pattern of activation is best explained by an increase in effective connectivity from the mentalising network (mPFC) to the action system (IPL). These results are an important step towards a more integrative understanding of the neurobiological basis of indirect speech processing.

Empirical Bayes for Group (DCM) Studies: A Reproducibility Study

This technical note addresses some key reproducibility issues in the dynamic causal modelling of group studies of event related potentials. Specifically, we address the reproducibility of Bayesian model comparison (and inferences about model parameters) from three important perspectives namely: (i) reproducibility with independent data (obtained by averaging over odd and even trials); (ii) reproducibility over formally distinct models (namely, classic ERP and canonical microcircuit or CMC models); and (iii) reproducibility over inversion schemes (inversion of the grand average and estimation of group effects using empirical Bayes). Our hope was to illustrate the degree of reproducibility one can expect from DCM when analysing different data, under different models with different analyses.

Evidence that Subanesthetic Doses of Ketamine Cause Sustained Disruptions of NMDA and AMPA-Mediated Frontoparietal Connectivity in Humans

Following the discovery of the antidepressant properties of ketamine, there has been a recent resurgence in the interest in this NMDA receptor antagonist. Although detailed animal models of the molecular mechanisms underlying ketamine's effects have emerged, there are few MEG/EEG studies examining the acute subanesthetic effects of ketamine infusion in man. We recorded 275 channel MEG in two experiments (n = 25 human males) examining the effects of subanesthetic ketamine infusion. MEG power spectra revealed a rich set of significant oscillatory changes compared with placebo sessions, including decreases in occipital, parietal, and anterior cingulate alpha power, increases in medial frontal theta power, and increases in parietal and cingulate cortex high gamma power. Each of these spectral effects demonstrated their own set of temporal dynamics. Dynamic causal modeling of frontoparietal connectivity changes with ketamine indicated a decrease in NMDA and AMPA-mediated frontal-to-parietal connectivity. AMPA-mediated connectivity changes were sustained for up to 50 min after ketamine infusion had ceased, by which time perceptual distortions were absent. The results also indicated a decrease in gain of parietal pyramidal cells, which was correlated with participants' self-reports of blissful state. Based on these results, we suggest that the antidepressant effects of ketamine may depend on its ability to change the balance of frontoparietal connectivity patterns.

SIGNIFICANCE STATEMENT

In this paper, we found that subanesthetic doses of ketamine, similar to those used in antidepressant studies, increase anterior theta and gamma power but decrease posterior theta, delta, and alpha power, as revealed by magnetoencephalographic recordings. Dynamic causal modeling of frontoparietal connectivity changes with ketamine indicated a decrease in NMDA and AMPA-mediated frontal-to-parietal connectivity. AMPA-mediated connectivity changes were sustained for up to 50 min after ketamine infusion had ceased, by which time perceptual distortions were absent. The results also indicated a decrease in gain of parietal pyramidal cells, which was correlated with participants' self-reports of blissful state. The alterations in frontoparietal connectivity patterns we observe here may be important in generating the antidepressant response to ketamine.

Neuroticism and conscientiousness respectively constrain and facilitate short-term plasticity within the working memory neural network

Individual differences in cognitive efficiency, particularly in relation to working memory (WM), have been associated both with personality dimensions that reflect enduring regularities in brain configuration, and with short‐term neural plasticity, that reflects task‐related changes in brain connectivity. To elucidate the relationship of these two divergent mechanisms, we tested the hypothesis that personality dimensions, which reflect enduring aspects of brain configuration, inform about the neurobiological framework within which short‐term, task‐related plasticity, as measured by effective connectivity, can be facilitated or constrained. As WM consistently engages the dorsolateral prefrontal (DLPFC), parietal (PAR), and anterior cingulate cortex (ACC), we specified a WM network model with bidirectional, ipsilateral, and contralateral connections between these regions from a functional magnetic resonance imaging dataset obtained from 40 healthy adults while performing the 3‐back WM task. Task‐related effective connectivity changes within this network were estimated using Dynamic Causal Modelling. Personality was evaluated along the major dimensions of Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. Only two dimensions were relevant to task‐dependent effective connectivity. Neuroticism and Conscientiousness respectively constrained and facilitated neuroplastic responses within the WM network. These results suggest individual differences in cognitive efficiency arise from the interplay between enduring and short‐term plasticity in brain configuration. Hum Brain Mapp 36:4158–4163, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Abnormal dopaminergic modulation of striato-cortical networks underlies levodopa-induced dyskinesias in humans

Dopaminergic signalling in the striatum contributes to reinforcement of actions and motivational enhancement of motor vigour. Parkinson's disease leads to progressive dopaminergic denervation of the striatum, impairing the function of cortico-basal ganglia networks. While levodopa therapy alleviates basal ganglia dysfunction in Parkinson's disease, it often elicits involuntary movements, referred to as levodopa-induced peak-of-dose dyskinesias. Here, we used a novel pharmacodynamic neuroimaging approach to identify the changes in cortico-basal ganglia connectivity that herald the emergence of levodopa-induced dyskinesias. Twenty-six patients with Parkinson's disease (age range: 51-84 years; 11 females) received a single dose of levodopa and then performed a task in which they had to produce or suppress a movement in response to visual cues. Task-related activity was continuously mapped with functional magnetic resonance imaging. Dynamic causal modelling was applied to assess levodopa-induced modulation of effective connectivity between the pre-supplementary motor area, primary motor cortex and putamen when patients suppressed a motor response. Bayesian model selection revealed that patients who later developed levodopa-induced dyskinesias, but not patients without dyskinesias, showed a linear increase in connectivity between the putamen and primary motor cortex after levodopa intake during movement suppression. Individual dyskinesia severity was predicted by levodopa-induced modulation of striato-cortical feedback connections from putamen to the pre-supplementary motor area (Pcorrected = 0.020) and primary motor cortex (Pcorrected = 0.044), but not feed-forward connections from the cortex to the putamen. Our results identify for the first time, aberrant dopaminergic modulation of striatal-cortical connectivity as a neural signature of levodopa-induced dyskinesias in humans. We argue that excessive striato-cortical connectivity in response to levodopa produces an aberrant reinforcement signal producing an abnormal motor drive that ultimately triggers involuntary movements.

Structural and effective connectivity reveals potential network-based influences on category-sensitive visual areas

Visual category perception is thought to depend on brain areas that respond specifically when certain categories are viewed. These category-sensitive areas are often assumed to be “modules” (with some degree of processing autonomy) and to act predominantly on feedforward visual input. This modular view can be complemented by a view that treats brain areas as elements within more complex networks and as influenced by network properties. This network-oriented viewpoint is emerging from studies using either diffusion tensor imaging to map structural connections or effective connectivity analyses to measure how their functional responses influence each other. This literature motivates several hypotheses that predict category-sensitive activity based on network properties. Large, long-range fiber bundles such as inferior fronto-occipital, arcuate and inferior longitudinal fasciculi are associated with behavioral recognition and could play crucial roles in conveying backward influences on visual cortex from anterior temporal and frontal areas. Such backward influences could support top-down functions such as visual search and emotion-based visual modulation. Within visual cortex itself, areas sensitive to different categories appear well-connected (e.g., face areas connect to object- and motion sensitive areas) and their responses can be predicted by backward modulation. Evidence supporting these propositions remains incomplete and underscores the need for better integration of DTI and functional imaging.

The prefrontal cortex achieves inhibitory control by facilitating subcortical motor pathway connectivity

Communication between the prefrontal cortex and subcortical nuclei underpins the control and inhibition of behavior. However, the interactions in such pathways remain controversial. Using a stop-signal response inhibition task and functional imaging with analysis of effective connectivity, we show that the lateral prefrontal cortex influences the strength of communication between regions in the frontostriatal motor system. We compared 20 generative models that represented alternative interactions between the inferior frontal gyrus, presupplementary motor area (preSMA), subthalamic nucleus (STN), and primary motor cortex during response inhibition. Bayesian model selection revealed that during successful response inhibition, the inferior frontal gyrus modulates an excitatory influence of the preSMA on the STN, thereby amplifying the downstream polysynaptic inhibition from the STN to the motor cortex. Critically, the strength of the interaction between preSMA and STN, and the degree of modulation by the inferior frontal gyrus, predicted individual differences in participants' stopping performance (stop-signal reaction time). We then used diffusion-weighted imaging with tractography to assess white matter structure in the pathways connecting these three regions. The mean diffusivity in tracts between preSMA and the STN, and between the inferior frontal gyrus and STN, also predicted individual differences in stopping efficiency. Finally, we found that white matter structure in the tract between preSMA and STN correlated with effective connectivity of the same pathway, providing important cross-modal validation of the effective connectivity measures. Together, the results demonstrate the network dynamics and modulatory role of the prefrontal cortex that underpin individual differences in inhibitory control.

Resting state functional MRI in Parkinson's disease: the impact of deep brain stimulation on 'effective' connectivity

Deep brain stimulation is an established therapy for Parkinson’s disease, although its mechanism of action remains unclear. Kahan et al. use resting state fMRI and dynamic causal modelling to study changes in ‘effective’ connectivity within the basal ganglia. Analyses implicate subthalamic afferents and the direct pathway in the clinical response.

Depleted of dopamine, the dynamics of the parkinsonian brain impact on both ‘action’ and ‘resting’ motor behaviour. Deep brain stimulation has become an established means of managing these symptoms, although its mechanisms of action remain unclear. Non-invasive characterizations of induced brain responses, and the effective connectivity underlying them, generally appeals to dynamic causal modelling of neuroimaging data. When the brain is at rest, however, this sort of characterization has been limited to correlations (functional connectivity). In this work, we model the ‘effective’ connectivity underlying low frequency blood oxygen level-dependent fluctuations in the resting Parkinsonian motor network—disclosing the distributed effects of deep brain stimulation on cortico-subcortical connections. Specifically, we show that subthalamic nucleus deep brain stimulation modulates all the major components of the motor cortico-striato-thalamo-cortical loop, including the cortico-striatal, thalamo-cortical, direct and indirect basal ganglia pathways, and the hyperdirect subthalamic nucleus projections. The strength of effective subthalamic nucleus afferents and efferents were reduced by stimulation, whereas cortico-striatal, thalamo-cortical and direct pathways were strengthened. Remarkably, regression analysis revealed that the hyperdirect, direct, and basal ganglia afferents to the subthalamic nucleus predicted clinical status and therapeutic response to deep brain stimulation; however, suppression of the sensitivity of the subthalamic nucleus to its hyperdirect afferents by deep brain stimulation may subvert the clinical efficacy of deep brain stimulation. Our findings highlight the distributed effects of stimulation on the resting motor network and provide a framework for analysing effective connectivity in resting state functional MRI with strong a priori hypotheses.

Functional activity and effective connectivity of the posterior medial prefrontal cortex during processing of incongruent mental states

The neurocognitive components of Theory of Mind reasoning remain poorly understood. In particular the role of the posterior medial prefrontal cortex in the processing of other's mental states such as beliefs that are incongruent with one's own knowledge of reality is not clear-cut. It is unknown whether this region is involved in computing discrepant mental states or in subsequently resolving a response conflict between the discrepant others' and one's own beliefs. To test this, we adapted a false belief paradigm for the separate inspection of functional brain activity related to (1) the computation of diverging beliefs and (2) the subsequent consideration and selection of another's or one's own belief. Based on statistical parametric findings from functional neuroimaging, we employed dynamic causal modelling combined with Bayesian model selection to further characterize the interplay of resulting brain regions. In the initial computation of diverging beliefs, the posterior medial prefrontal cortex (pMPFC) and the bilateral temporoparietal cortex were crucially involved. The findings suggest that the bilateral temporal cortex engages in the construction and adjustment of diverging mental states by encoding relevant environmental information. The pMPFC inhibits this stimulus-bound processing which helps to compute discrepant mental states and process another's false belief decoupled from one's own perception of reality. In the subsequent question phase the right temporoparietal cortex showed increased activity related to switching to and reconsidering another's beliefs in order to select the correct response.

Impact of blindness onset on the functional organization and the connectivity of the occipital cortex

Contrasting the impact of congenital versus late-onset acquired blindness provides a unique model to probe how experience at different developmental periods shapes the functional organization of the occipital cortex. We used functional magnetic resonance imaging to characterize brain activations of congenitally blind, late-onset blind and two groups of sighted control individuals while they processed either the pitch or the spatial attributes of sounds. Whereas both blind groups recruited occipital regions for sound processing, activity in bilateral cuneus was only apparent in the congenitally blind, highlighting the existence of region-specific critical periods for crossmodal plasticity. Most importantly, the preferential activation of the right dorsal stream (middle occipital gyrus and cuneus) for the spatial processing of sounds was only observed in the congenitally blind. This demonstrates that vision has to be lost during an early sensitive period in order to transfer its functional specialization for space processing toward a non-visual modality. We then used a combination of dynamic causal modelling with Bayesian model selection to demonstrate that auditory-driven activity in primary visual cortex is better explained by direct connections with primary auditory cortex in the congenitally blind whereas it relies more on feedback inputs from parietal regions in the late-onset blind group. Taken together, these results demonstrate the crucial role of the developmental period of visual deprivation in (re)shaping the functional architecture and the connectivity of the occipital cortex. Such findings are clinically important now that a growing number of medical interventions may restore vision after a period of visual deprivation.

The right hemisphere supports but does not replace left hemisphere auditory function in patients with persisting aphasia

In this study, we used magnetoencephalography and a mismatch paradigm to investigate speech processing in stroke patients with auditory comprehension deficits and age-matched control subjects. We probed connectivity within and between the two temporal lobes in response to phonemic (different word) and acoustic (same word) oddballs using dynamic causal modelling. We found stronger modulation of self-connections as a function of phonemic differences for control subjects versus aphasics in left primary auditory cortex and bilateral superior temporal gyrus. The patients showed stronger modulation of connections from right primary auditory cortex to right superior temporal gyrus (feed-forward) and from left primary auditory cortex to right primary auditory cortex (interhemispheric). This differential connectivity can be explained on the basis of a predictive coding theory which suggests increased prediction error and decreased sensitivity to phonemic boundaries in the aphasics' speech network in both hemispheres. Within the aphasics, we also found behavioural correlates with connection strengths: a negative correlation between phonemic perception and an inter-hemispheric connection (left superior temporal gyrus to right superior temporal gyrus), and positive correlation between semantic performance and a feedback connection (right superior temporal gyrus to right primary auditory cortex). Our results suggest that aphasics with impaired speech comprehension have less veridical speech representations in both temporal lobes, and rely more on the right hemisphere auditory regions, particularly right superior temporal gyrus, for processing speech. Despite this presumed compensatory shift in network connectivity, the patients remain significantly impaired.

Network discovery with DCM

This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity. This network discovery uses Bayesian model selection to identify the sparsity structure (absence of edges or connections) in a graph that best explains observed time-series. The implicit adjacency matrix specifies the form of the network (e.g., cyclic or acyclic) and its graph-theoretical attributes (e.g., degree distribution). The scheme is illustrated using functional magnetic resonance imaging (fMRI) time series to discover functional brain networks. Crucially, it can be applied to experimentally evoked responses (activation studies) or endogenous activity in task-free (resting state) fMRI studies. Unlike conventional approaches to network discovery, DCM permits the analysis of directed and cyclic graphs. Furthermore, it eschews (implausible) Markovian assumptions about the serial independence of random fluctuations. The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks. The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions. We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies.

A systems perspective on the effective connectivity of overt speech production

The aim of this study was to provide a computational system model of effective connectivity in the human brain underlying overt speech production. Meta-analysis of neuroimaging studies and functional magnetic resonance imaging data acquired during a verbal fluency task revealed a core network consisting of Brodmann's area (BA) 44 in Broca's region, anterior insula, basal ganglia, cerebellum, premotor cortex (PMC, BA 6) and primary motor cortex (M1, areas 4a/4p). Dynamic causal modelling (DCM) indicated the highest evidence for a system architecture featuring the insula in a serial position between BA 44 and two parallel nodes (cerebellum/basal ganglia), from which information converges onto the PMC and finally M1. Parameter inference revealed that effective connectivity from the insular relay into the cerebellum/basal ganglia is primarily task driven (preparation) while the output into the cortical motor system strongly depends on the actual word production rate (execution). DCM hence allowed not only a quantitative characterization of the human speech production network, but also the distinction of a preparatory and an executive subsystem within it. The proposed model of physiological integration during speech production may now serve as a reference for investigations into the neurobiology of pathological states such as dysarthria and apraxia of speech.

Bayesian estimation of synaptic physiology from the spectral responses of neural masses

We describe a Bayesian inference scheme for quantifying the active physiology of neuronal ensembles using local field recordings of synaptic potentials. This entails the inversion of a generative neural mass model of steady-state spectral activity. The inversion uses Expectation Maximization (EM) to furnish the posterior probability of key synaptic parameters and the marginal likelihood of the model itself. The neural mass model embeds prior knowledge pertaining to both the anatomical [synaptic] circuitry and plausible trajectories of neuronal dynamics. This model comprises a population of excitatory pyramidal cells, under local interneuron inhibition and driving excitation from layer IV stellate cells. Under quasi-stationary assumptions, the model can predict the spectral profile of local field potentials (LFP). This means model parameters can be optimised given real electrophysiological observations. The validity of inferences about synaptic parameters is demonstrated using simulated data and experimental recordings from the medial prefrontal cortex of control and isolation-reared Wistar rats. Specifically, we examined the maximum a posteriori estimates of parameters describing synaptic function in the two groups and tested predictions derived from concomitant microdialysis measures. The modelling of the LFP recordings revealed (i) a sensitization of post-synaptic excitatory responses, particularly marked in pyramidal cells, in the medial prefrontal cortex of socially isolated rats and (ii) increased neuronal adaptation. These inferences were consistent with predictions derived from experimental microdialysis measures of extracellular glutamate levels.