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Theoretical frameworks such as predictive coding suggest that the perception of the body and world – interoception and exteroception – involve intertwined processes of inference, learning, and prediction. In this framework, attention is thought to gate the influence of sensory information on perception. In contrast to exteroception, there is limited evidence for purely attentional effects on interoception. Here, we empirically tested if attentional focus modulates cortical processing of single heartbeats, using a newly-developed experimental paradigm to probe purely attentional differences between exteroceptive and interoceptive conditions in the heartbeat evoked potential (HEP) using EEG recordings. We found that the HEP is significantly higher during interoceptive compared to exteroceptive attention, in a time window of 524–620 ms after the R-peak. Furthermore, this effect predicted self-report measures of autonomic system reactivity. Our study thus provides direct evidence that the HEP is modulated by pure attention and suggests that this effect may provide a clinically relevant readout for assessing interoception., NeuroImage, 186, ISSN:1053-8119, ISSN:1095-9572

The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the “Bayesian brain” notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, predictive coding views perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs). Disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia.Here, we provide empirical evidence for this clinical theory, demonstrating the existence of multiple, hierarchically related PEs in a “roving MMN” paradigm. We applied a computational model, the Hierarchical Gaussian Filter (HGF), to single-trial EEG data from healthy volunteers that received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our computational trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207ms post-stimulus), while high-level PEs (about transition probability) are reflected by later components (152-199ms, 215-277ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts inference on abstract statistical regularities.Our findings are consistent with long-standing notions that NMDAR dysfunction may cause positive symptoms in schizophrenia by impairing hierarchical Bayesian inference about the world’s statistical structure. Beyond their relevance for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential disease mechanisms.

Drugs affecting neuromodulation, for example by dopamine or acetylcholine, take centre stage among therapeutic strategies in psychiatry. These neuromodulators can change both neuronal gain and synaptic plasticity and therefore affect electrophysiological measures. An important goal for clinical diagnostics is to exploit this effect in the reverse direction, i.e., to infer the status of specific neuromodulatory systems from electrophysiological measures. In this study, we provide proof-of-concept that the functional status of cholinergic (specifically muscarinic) receptors can be inferred from electrophysiological data using generative (dynamic causal) models. To this end, we used epidural EEG recordings over two auditory cortical regions during a mismatch negativity (MMN) paradigm in rats. All animals were treated, across sessions, with muscarinic receptor agonists and antagonists at different doses. Together with a placebo condition, this resulted in five levels of muscarinic receptor status. Using a dynamic causal model - embodying a small network of coupled cortical microcircuits - we estimated synaptic parameters and their change across pharmacological conditions. The ensuing parameter estimates associated with (the neuromodulation of) synaptic efficacy showed both graded muscarinic effects and predictive validity between agonistic and antagonistic pharmacological conditions. This finding illustrates the potential utility of generative models of electrophysiological data as computational assays of muscarinic function. In application to EEG data of patients from heterogeneous spectrum diseases, e.g. schizophrenia, such models might help identify subgroups of patients that respond differentially to cholinergic treatments. Significance Statement In psychiatry, the vast majority of pharmacological treatments affect actions of neuromodulatory transmitters, e.g. dopamine or acetylcholine. As treatment is largely trial-and-error based, one of the goals for computational psychiatry is to construct mathematical models that can serve as “computational assays” and infer the status of specific neuromodulatory systems in individual patients. This translational neuromodeling strategy has great promise for electrophysiological data in particular but requires careful validation. The present study demonstrates that the functional status of cholinergic (muscarinic) receptors can be inferred from electrophysiological data using dynamic causal models of neural circuits. While accuracy needs to be enhanced and our results must be replicated in larger samples, our current results provide proof-of-concept for computational assays of muscarinic function using EEG., NeuroImage, 237, ISSN:1053-8119, ISSN:1095-9572

Fatigue is one of the most common symptoms in multiple sclerosis (MS), with a major impact on patients’ quality of life. Currently, treatment proceeds by trial and error with limited success, probably due to the presence of multiple different underlying mechanisms. Recent neuroscientific advances offer the potential to develop tools for differentiating these mechanisms in individual patients and ultimately provide a principled basis for treatment selection. However, development of these tools for differential diagnosis will require guidance by pathophysiological and cognitive theories that propose mechanisms which can be assessed in individual patients. This article provides an overview of contemporary pathophysiological theories of fatigue in MS and discusses how the mechanisms they propose may become measurable with emerging technologies and thus lay a foundation for future personalised treatments., Journal of Neurology, Neurosurgery & Psychiatry, 90 (6), ISSN:0022-3050

The basal ganglia (BG) are a group of subcortical nuclei responsible for motor control, motor learning and executive function. Central to BG function are striatal medium spiny neurons (MSNs) expressing D1 and D2 dopamine receptors. D1 and D2 MSNs are typically considered functional antagonists that facilitate voluntary movements and inhibit competing motor patterns, respectively. While their opposite role is well documented for certain sensorimotor loops of the BG-thalamocortical network, it is unclear whether MSNs maintain a uniform functional role across the striatum and which influence they exert on brain areas outside the BG. Here, we addressed these questions by combining optogenetic activation of D1 and D2 MSNs in the mouse ventrolateral caudoputamen (vl CPu) with whole-brain functional MRI (fMRI) recordings. Neuronal excitation of either cell population in the vl CPu evoked distinct activity patterns in key regions of the BG-thalamocortical network including the pallidum, thalamus and motor cortex. Importantly, we report that striatal D1 and D2 MSN stimulation differentially engaged cerebellar and prefrontal regions. We characterised these long-range interactions by computational modelling of effective connectivity and confirmed that changes in D1 / D2 output drive functional relationships between regions within and beyond the BG. These results suggest a more complex functional organization of MSNs across the striatum than previously anticipated and provide evidence for the existence of an interconnected fronto - BG - cerebellar network modulated by striatal D1 and D2 MSNs.Graphical Abstract

The auditory mismatch negativity (MMN) has been proposed as a biomarker of NMDA receptor (NMDAR) dysfunction in schizophrenia. Pathophysiological theories suggest that such dysfunction might be partially caused by aberrant interactions of different modulatory neurotransmitters with NMDARs, which could explain heterogeneity among patients with schizophrenia and their treatment response. Understanding the differential impact of different neuromodulators on readouts of NMDAR function is therefore of high clinical relevance.Here, we report results from two studies (N=81 each) which systematically tested whether the MMN is sensitive to diminishing and enhancing cholinergic vs. dopaminergic function. Both studies used a double-blind, placebo-controlled between-subject design and monitored individual drug plasma levels. Using a novel variant of the auditory oddball paradigm, we contrasted phases with stable versus volatile probabilities of tone switches. In the first study, we found that the muscarinic acetylcholine receptor antagonist biperiden reduced and/or delayed mismatch responses, particularly during stable phases of the experiment, whereas this effect was absent for amisulpride, a dopamine D2/D3 receptor antagonist. The direct comparison between biperiden and amisulpride indicated a significant drug × mismatch interaction. In the second study, neither elevating acetylcholine nor dopamine levels via administration of galantamine and levodopa, respectively, exerted significant effects on MMN.Overall, our results indicate differential sensitivity of the MMN to changes in cholinergic (muscarinic) versus dopaminergic receptor function. This finding may prove useful for developments of future tools for predicting individual treatment responses in disorders that show abnormal MMN, such as schizophrenia.

The basal ganglia (BG) are a group of subcortical nuclei responsible for motor control, motor learning and executive function. Central to BG function are striatal medium spiny neurons (MSNs) expressing D1 and D2 dopamine receptors. D1 and D2 MSNs are typically considered functional antagonists that facilitate voluntary movements and inhibit competing motor patterns, respectively. While their opposite role is well documented for certain sensorimotor loops of the BG-thalamocortical network, it is unclear whether MSNs maintain a uniform functional role across the striatum and which influence they exert on brain areas outside the BG. Here, we addressed these questions by combining optogenetic activation of D1 and D2 MSNs in the mouse ventrolateral caudoputamen (vl CPu) with whole-brain functional MRI (fMRI) recordings. Neuronal excitation of either cell population in the vl CPu evoked distinct activity patterns in key regions of the BG-thalamocortical network including the pallidum, thalamus and motor cortex. Importantly, we report that striatal D1 and D2 MSN stimulation differentially engaged cerebellar and prefrontal regions. We characterised these long-range interactions by computational modelling of effective connectivity and confirmed that changes in D1 / D2 output drive functional relationships between regions within and beyond the BG. These results suggest a more complex functional organization of MSNs across the striatum than previously anticipated and provide evidence for the existence of an interconnected fronto - BG - cerebellar network modulated by striatal D1 and D2 MSNs.

The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal differences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This finding requires replication in a larger dataset. © 2020, The Author(s)., Scientific Reports, 10 (1), ISSN:2045-2322

Neuroimaging methods have greatly contributed to our understanding of structural and functional brain changes in schizophrenia. This chapter reviews seminal studies that started the field, as well as more recent neuroimaging research, including discovery-oriented and theory-driven approaches. The chapter structures the latter literature according to four major contemporary pathophysiological theories of schizophrenia, including: (1) the neurodevelopmental theory; (2) the dopamine theory; (3) the GABAergic and glutamatergic theory; and (4) the dysconnection theory. The chapter focuses on those neuroimaging studies that have contributed to elucidating the proposed disease mechanisms. Finally, the chapter discusses the central challenge of neuroimaging in schizophrenia research—to provide tools for individual treatment response prediction. It considers how technical advances in neuroimaging techniques, in combination with computational modelling approaches that strive for mechanistic interpretability, can improve the clinical utility of neuroimaging methods.

Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals. Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour – with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental ‘volatility’ – and used these computational quantities for analyses of fMRI data. Computational modelling of CHR individuals’ behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning. Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation., NeuroImage: Clinical, 26, ISSN:2213-1582

Subthalamic deep brain stimulation (STN-DBS) for Parkinson’s disease treats motor symptoms and improves quality of life, but can be complicated by adverse neuropsychiatric side-effects, including impulsivity. Several clinically important questions remain unclear: can ‘at-risk’ patients be identified prior to DBS; do neuropsychiatric symptoms relate to the distribution of the stimulation field; and which brain networks are responsible for the evolution of these symptoms? Using a comprehensive neuropsychiatric battery and a virtual casino to assess impulsive behaviour in a naturalistic fashion, 55 patients with Parkinson’s disease (19 females, mean age 62, mean Hoehn and Yahr stage 2.6) were assessed prior to STN-DBS and 3 months postoperatively. Reward evaluation and response inhibition networks were reconstructed with probabilistic tractography using the participant-specific subthalamic volume of activated tissue as a seed. We found that greater connectivity of the stimulation site with these frontostriatal networks was related to greater postoperative impulsiveness and disinhibition as assessed by the neuropsychiatric instruments. Larger bet sizes in the virtual casino postoperatively were associated with greater connectivity of the stimulation site with right and left orbitofrontal cortex, right ventromedial prefrontal cortex and left ventral striatum. For all assessments, the baseline connectivity of reward evaluation and response inhibition networks prior to STN-DBS was not associated with postoperative impulsivity; rather, these relationships were only observed when the stimulation field was incorporated. This suggests that the site and distribution of stimulation is a more important determinant of postoperative neuropsychiatric outcomes than preoperative brain structure and that stimulation acts to mediate impulsivity through differential recruitment of frontostriatal networks. Notably, a distinction could be made amongst participants with clinically-significant, harmful changes in mood and behaviour attributable to DBS, based upon an analysis of connectivity and its relationship with gambling behaviour. Additional analyses suggested that this distinction may be mediated by the differential involvement of fibres connecting ventromedial subthalamic nucleus and orbitofrontal cortex. These findings identify a mechanistic substrate of neuropsychiatric impairment after STN-DBS and suggest that tractography could be used to predict the incidence of adverse neuropsychiatric effects. Clinically, these results highlight the importance of accurate electrode placement and careful stimulation titration in the prevention of neuropsychiatric side-effects after STN-DBS.

Background Patients with schizophrenia make more errors than healthy subjects in the antisaccade task. In this paradigm, participants are required to inhibit a reflexive saccade to a target and to select the correct action (a saccade in the opposite direction). While the precise origin of this deficit is not clear, it has been connected to aberrant dopaminergic and cholinergic neuromodulation. Methods To study the impact of dopamine and acetylcholine on inhibitory control and action selection, we administered two selective drugs (levodopa 200 mg/galantamine 8 mg) to healthy volunteers (N = 100) performing the antisaccade task. The computational model SERIA (stochastic early reaction, inhibition, and late action) was employed to separate the contribution of inhibitory control and action selection to empirical reaction times and error rates. Results Modeling suggested that levodopa improved action selection (at the cost of increased reaction times) but did not have a significant effect on inhibitory control. By contrast, according to our model, galantamine affected inhibitory control in a dose-dependent fashion, reducing inhibition failures at low doses and increasing them at higher levels. These effects were sufficiently specific that the computational analysis allowed for identifying the drug administered to an individual with 70% accuracy. Conclusions Our results do not support the hypothesis that elevated tonic dopamine strongly impairs inhibitory control. Rather, levodopa improved the ability to select correct actions. However, inhibitory control was modulated by cholinergic drugs. This approach may provide a starting point for future computational assays that differentiate neuromodulatory abnormalities in heterogeneous diseases like schizophrenia., Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5 (3), ISSN:2451-9022, ISSN:2451-9030

Impulsivity in Parkinson's disease may be mediated by faulty evaluation of rewards or the failure to inhibit inappropriate choices. Despite prior work suggesting that distinct neural networks underlie these cognitive operations, there has been little study of these networks in Parkinson's disease, and their relationship to inter-individual differences in impulsivity. High-resolution diffusion MRI data were acquired from 57 individuals with Parkinson's disease (19 females, mean age 62, mean Hoehn and Yahr stage 2.6) prior to surgery for deep brain stimulation. Reward evaluation and response inhibition networks were reconstructed with seed-based probabilistic tractography. Impulsivity was evaluated using two approaches: (i) neuropsychiatric instruments were used to assess latent constructs of impulsivity, including trait impulsiveness and compulsivity, disinhibition, and also impatience; and (ii) participants gambled in an ecologically-valid virtual casino to obtain a behavioural read-out of explorative, risk-taking, impulsive behaviour. Multivariate analyses revealed that different components of impulsivity were associated with distinct variations in structural connectivity, implicating both reward evaluation and response inhibition networks. Larger bet sizes in the virtual casino were associated with greater connectivity of the reward evaluation network, particularly bilateral fibre tracts between the ventral striatum and ventromedial prefrontal cortex. In contrast, weaker connectivity of the response inhibition network was associated with increased exploration of alternative slot machines in the virtual casino, with right-hemispheric tracts between the subthalamic nucleus and the pre-supplementary motor area contributing most strongly. Further, reduced connectivity of the reward evaluation network was associated with more 'double or nothing' gambles, weighted by connections between the subthalamic nucleus and ventromedial prefrontal cortex. Notably, the variance explained by structural connectivity was higher for behavioural indices of impulsivity, derived from clinician-administered tasks and the gambling paradigm, as compared to questionnaire data. Lastly, a clinically-meaningful distinction could be made amongst participants with a history of impulse control behaviours based on the interaction of their network connectivity with medication dosage and gambling behaviour. In summary, we report structural brain-behaviour covariation in Parkinson's disease with distinct reward evaluation and response inhibition networks that underlie dissociable aspects of impulsivity (cf. choosing and stopping). More broadly, our findings demonstrate the potential of using naturalistic paradigms and neuroimaging techniques in clinical settings to assist in the identification of those susceptible to harmful behaviours.

Navigating the physical world requires learning probabilistic associations between sensory events and their change in time (volatility). Bayesian accounts of this learning process rest on hierarchical prediction errors (PEs) that are weighted by estimates of uncertainty (or its inverse, precision). In a previous fMRI study we found that low-level precision-weighted PEs about visual outcomes (that update beliefs about associations) activated the putative dopaminergic midbrain; by contrast, precision-weighted PEs about cue-outcome associations (that update beliefs about volatility) activated the cholinergic basal forebrain. These findings suggested selective dopaminergic and cholinergic influences on precision-weighted PEs at different hierarchical levels. Here, we tested this hypothesis, repeating our fMRI study under pharmacological manipulations in healthy participants. Specifically, we performed two pharmacological fMRI studies with a between-subject double-blind placebo-controlled design: study 1 used antagonists of dopaminergic (amisulpride) and muscarinic (biperiden) receptors, study 2 used enhancing drugs of dopaminergic (levodopa) and cholinergic (galantamine) modulation. Pooled across all pharmacological conditions of study 1 and study 2, respectively, we found that low-level precision-weighted PEs activated the midbrain and high-level precision-weighted PEs the basal forebrain as in our previous study. However, we found pharmacological effects on brain activity associated with these computational quantities only when splitting the precision-weighted PEs into their PE and precision components: in a brainstem region putatively containing cholinergic (pedunculopontine and laterodorsal tegmental) nuclei, biperiden (compared to placebo) enhanced low-level PE responses and attenuated high-level PE activity, while amisulpride reduced high-level PE responses. Additionally, in the putative dopaminergic midbrain, galantamine compared to placebo enhanced low-level PE responses (in a body-weight dependent manner) and amisulpride enhanced high-level precision activity. Task behaviour was not affected by any of the drugs. These results do not support our hypothesis of a clear-cut dichotomy between different hierarchical inference levels and neurotransmitter systems, but suggest a more complex interaction between these neuromodulatory systems and hierarchical Bayesian quantities. However, our present results may have been affected by confounds inherent to pharmacological fMRI. We discuss these confounds and outline improved experimental tests for the future., NeuroImage, 226, ISSN:1053-8119, ISSN:1095-9572

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics., NeuroImage, 225, ISSN:1053-8119, ISSN:1095-9572

The hypothalamus and insular cortex play an essential role in the integration of endocrine and homeostatic signals and their impact on food intake. Resting-state functional connectivity alterations of the hypothalamus, posterior insula (PINS) and anterior insula (AINS) are modulated by metabolic states and caloric intake. Nevertheless, a deeper understanding of how these factors affect the strength of connectivity between hypothalamus, PINS and AINS is missing. This study investigated whether effective (directed) connectivity within this network varies as a function of prandial states (hunger vs. satiety) and energy availability (glucose levels and/or hormonal modulation). To address this question, we measured twenty healthy male participants of normal weight twice: once after 36 h of fasting (except water consumption) and once under satiated conditions. During each session, resting-state functional MRI (rs-fMRI) and hormone concentrations were recorded before and after glucose administration. Spectral dynamic causal modeling (spDCM) was used to assess the effective connectivity between the hypothalamus and anterior and posterior insula. Using Bayesian model selection, we observed that the same model was identified as the most likely model for each rs-fMRI recording. Compared to satiety, the hunger condition enhanced the strength of the forward connections from PINS to AINS and reduced the strength of backward connections from AINS to PINS. Furthermore, the strength of connectivity from PINS to AINS was positively related to plasma cortisol levels in the hunger condition, mainly before glucose administration. However, there was no direct relationship between glucose treatment and effective connectivity. Our findings suggest that prandial states modulate connectivity between PINS and AINS and relate to theories of interoception and homeostatic regulation that invoke hierarchical relations between posterior and anterior insula., NeuroImage, 217, ISSN:1053-8119, ISSN:1095-9572

Variations in the fat mass and obesity-associated (FTO) gene are linked to obesity. However, the underlying neurobiological mechanisms by which these genetic variants influence obesity, behavior, and brain are unknown. Given that Fto regulates D2/3R signaling in mice, we tested in humans whether variants in FTO would interact with a variant in the ANKK1 gene, which alters D2R signaling and is also associated with obesity. In a behavioral and fMRI study, we demonstrate that gene variants of FTO affect dopamine (D2)-dependent midbrain brain responses to reward learning and behavioral responses associated with learning from negative outcome in humans. Furthermore, dynamic causal modeling confirmed that FTO variants modulate the connectivity in a basic reward circuit of meso-striato-prefrontal regions, suggesting a mechanism by which genetic predisposition alters reward processing not only in obesity, but also in other disorders with altered D2R-dependent impulse control, such as addiction. Significance statement: Variations in the fat mass and obesity-associated (FTO) gene are associated with obesity. Here we demonstrate that variants of FTO affect dopamine-dependent midbrain brain responses and learning from negative outcomes in humans during a reward learning task. Furthermore, FTO variants modulate the connectivity in a basic reward circuit of meso-striato-prefrontal regions, suggesting a mechanism by which genetic vulnerability in reward processing can increase predisposition to obesity.

Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. Promising new avenues for translation are provided by computational modeling of neuroimaging data. This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. These approaches—biophysical network models, generative models, and model-based fMRI analyses of neuromodulation—strive to move beyond statistical characterizations and toward mechanistic explanations of neuroimaging data. Focusing on schizophrenia as a paradigmatic spectrum disease, we review applications of these models to psychiatric questions, identify methodological challenges, and highlight trends of convergence among computational neuroimaging approaches. We conclude by outlining a translational neuromodeling strategy, highlighting the importance of openly available datasets from prospective patient studies for evaluating the clinical utility of computational models.

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.

Insulin modulates dopamine neuron activity in midbrain and affects processes underlying food intake behaviour, including impulsivity and reward processing. Here, we used intranasal administration and task-free functional MRI in humans to assess time- and dose-dependent effects of insulin on functional connectivity of the dopaminergic midbrain – and how these effects varied depending on systemic insulin sensitivity as measured by HOMA-IR. Specifically, we used a repeated-measures design with factors dose (placebo, 40 IU, 100 IU, 160 IU), time (7 time points during a 90 min post-intervention interval), and group (low vs. high HOMA-IR). A factorial analysis identified a three-way interaction (with whole-brain significance) with regard to functional connectivity between midbrain and the ventromedial prefrontal cortex. This interaction demonstrates that systemic insulin sensitivity modulates the temporal course and dose-dependent effects of intranasal insulin on midbrain functional connectivity. It suggests that altered insulin sensitivity may impact on dopaminergic projections of the midbrain and might underlie the dysregulation of reward-related and motivational behaviour in obesity and diabetes. Perhaps most importantly, the time courses of midbrain functional connectivity we present may provide useful guidance for the design of future human studies that utilize intranasal insulin administration.

Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans. This review provides a brief overview of predictive coding and related hierarchical Bayesian theories, summarises their predictions with regard to layered cortical computations, examines how these predictions could be tested by laminar fMRI, and considers methodological challenges. We conclude by discussing the potential of laminar fMRI for clinically useful computational assays of layer-specific information processing.

The antisaccade task is a classic paradigm used to study the voluntary control of eye movements. It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction. Although several models have been proposed to explain error rates and reaction times in this task, no formal model comparison has yet been performed. Here, we describe a Bayesian modeling approach to the antisaccade task that allows us to formally compare different models on the basis of their evidence. First, we provide a formal likelihood function of actions (pro- and antisaccades) and reaction times based on previously published models. Second, we introduce the Stochastic Early Reaction, Inhibition, and late Action model (SERIA), a novel model postulating two different mechanisms that interact in the antisaccade task: an early GO/NO-GO race decision process and a late GO/GO decision process. Third, we apply these models to a data set from an experiment with three mixed blocks of pro- and antisaccade trials. Bayesian model comparison demonstrates that the SERIA model explains the data better than competing models that do not incorporate a late decision process. Moreover, we show that the early decision process postulated by the SERIA model is, to a large extent, insensitive to the cue presented in a single trial. Finally, we use parameter estimates to demonstrate that changes in reaction time and error rate due to the probability of a trial type (pro- or antisaccade) are best explained by faster or slower inhibition and the probability of generating late voluntary prosaccades., Author summary One widely replicated finding in schizophrenia research is that patients tend to make more errors than healthy controls in the antisaccade task, a psychometric paradigm in which participants are required to look in the opposite direction of a visual cue. This deficit has been suggested to be an endophenotype of schizophrenia, as first order relatives of patients tend to show similar but milder deficits. Currently, most models applied to experimental findings in this task are limited to fit average reaction times and error rates. Here, we propose a novel statistical model that fits experimental data from the antisaccade task, beyond summary statistics. The model is inspired by the hypothesis that antisaccades are the result of several competing decision processes that interact nonlinearly with each other. In applying this model to a relatively large experimental data set, we show that mean reaction times and error rates do not fully reflect the complexity of the processes that are likely to underlie experimental findings. In the future, our model could help to understand the nature of the deficits observed in schizophrenia by providing a statistical tool to study their biological underpinnings.

Background Present pharmacological treatment approaches in schizophrenia rest on “neuroleptic” drugs, all of which act as antagonists at dopamine D2/D3 receptors but additionally display major variability in their binding capacity to neurotransmitter receptors (Van Os & Kapur 2009). At present, the choice of any particular drug does not rest on any principled criteria: Once individual treatment has been started, therapeutic efficacy is monitored clinically, and a switch to a different drug is initiated when clear improvements remain absent after a few weeks. It is presently not possible to predict in advance which patients will respond well to a particular drug and who will experience little or no benefit (Case et al. 2011; Kapur et al. 2012). For instance, clozapine and olanzapine are often prescribed after other antipsychotics have shown to be ineffective in patients with schizophrenia or related disorders due to their pronounced side-effects. Both drugs, clozapine and olanzapine, share certain pharmacodynamic properties with comparatively low affinity towards dopamine D2-receptors, but very high affinity towards muscarinic receptors – a unique constellation that distinguishes them from other common antipsychotics. Importantly, previous studies have shown that a subgroup of schizophrenia patients might particularly benefit from these properties (Raedler et al. 2003, Scarr et al. 2009). Here, we present an ongoing observational study (COMPASS) which builds on these observations and addresses the question whether functional readouts of dopaminergic and muscarinic systems in individual patients could enable personalised treatment predictions. Guided by the dysconnection hypothesis of schizophrenia (Stephan et al., 2009), which postulates aberrant interactions between NMDA receptors and neuromodulators like dopamine/acetylcholine, the COMPASS study adopts a neuromodeling approach. The focus is on EEG/fMRI paradigms and computational models with empirically demonstrated sensitivity for altered function of NMDA, dopamine and muscarinic receptors, respectively. Methods To detect even small effect sizes, the study aims to recruit N=120 patients with schizophrenia who begin treatment with, switch to, or augment medication with olanzapine or clozapine. If possible, a replication sample (an additional N=120) will be recruited, too. Patients will be examined +/- 96h relative to treatment onset. Data acquisition encompasses the following measurements: Clinical interview, EEG (working memory, reward learning under volatility, auditory MMN under volatility, “resting”-state), MRI (optional; fMRI during auditory MMN under volatility, “resting”-state, and structural imaging), blood samples (genetic and biochemical analyses). After 2 and 8 weeks a clinical follow-up is conducted. Results The study is ongoing. Discussion The EEG/fMRI data will be analysed by computational models that infer functional states of glutamatergic, dopaminergic, and cholinergic systems (for review, Stephan et al. 2015). Model parameter estimates will serve as features in machine learning analyses of treatment prediction (Brodersen et al. 2014). If successful, this proof-of-concept study will lead to clinically useful tests for predicting the efficacy of clozapine/olanzapine prior to or during very early treatment. This could have a significant impact on clinical management as it would enable predicting, at an early stage, the therapeutic benefit for individual patients. Our neuromodeling approach to individual predictions may thus provide a principled basis for treatment decisions, help spare side-effects and enable informed switches in treatment strategy.

Dysregulation of mesolimbic dopamine transmission is implicated in a number of psychiatric illnesses characterised by disruption of reward processing and goal-directed behaviour, including schizophrenia, drug addiction and impulse control disorders associated with chronic use of dopamine agonists. Amphetamine sensitization (AS) has been proposed to model the development of this aberrant dopamine signalling and the subsequent dysregulation of incentive motivational processes. However, in humans the effects of AS on the dopamine-sensitive neural circuitry associated with reward processing remains unclear. Here we describe the effects of acute amphetamine administration, following a sensitising dosage regime, on blood oxygen level dependent (BOLD) signal in dopaminoceptive brain regions during a rewarded gambling task performed by healthy volunteers. Using a randomised, double-blind, parallel-groups design, we found clear evidence for sensitization to the subjective effects of the drug, while rewarded reaction times were unchanged. Repeated amphetamine exposure was associated with reduced dorsal striatal BOLD signal during decision making, but enhanced ventromedial caudate activity during reward anticipation. The amygdala BOLD response to reward outcomes was blunted following repeated amphetamine exposure. Positive correlations between subjective sensitization and changes in anticipation- and outcome-related BOLD signal were seen for the caudate nucleus and amygdala, respectively. These data show for the first time in humans that AS changes the functional impact of acute stimulant exposure on the processing of reward-related information within dopaminoceptive regions. Our findings accord with pathophysiological models which implicate aberrant dopaminergic modulation of striatal and amygdala activity in psychosis and drug-related compulsive disorders.

CoCoMac, the “Collation of Connectivity Data for the Macaque” is a relational database system which presently constitutes the largest electronic repository of published neuroanatomical connectivity data. Developed since 1996, CoCoMac comprises approximately 40,000 experimental findings on anatomical connections in the macaque brain, as derived from neuroanatomical tract tracing studies. In this historical review, I describe the origin and the history of CoCoMac from a personal perspective, illustrate the principles of its structure and outline the impact it has had on systems neuroscience, in particular as a prelude to the “Human Connectome” research programme. ISSN:1053-8119 ISSN:1095-9572

The dopaminergic system is involved in reward encoding and reinforcement learning. Dopaminergic neurons from this system in the substantia nigra/ventral tegmental area complex (SN/VTA) fire in response to unexpected reinforcing cues. The goal of this study was to investigate whether individuals can gain voluntary control of SN/VTA activity, thereby potentially enhancing dopamine release to target brain regions. Neurofeedback and mental imagery were used to self-regulate the SN/VTA. Real-time functional magnetic resonance imaging (rtfMRI) provided abstract visual feedback of the SN/VTA activity while the subject imagined rewarding scenes. Skin conductance response (SCR) was recorded as a measure of emotional arousal. To examine the effect of neurofeedback, subjects were assigned to either receiving feedback directly proportional (n=15, veridical feedback) or inversely proportional (n=17, inverted feedback) to SN/VTA activity. Both groups of subjects were able to up-regulate SN/VTA activity initially without feedback. Veridical feedback improved the ability to up-regulate SN/VTA compared to baseline while inverted feedback did not. Additional dopaminergic regions were activated in both groups. The ability to self-regulate SN/VTA was differentially correlated with SCR depending on the group, suggesting an association between emotional arousal and neurofeedback performance. These findings indicate that SN/VTA can be voluntarily activated by imagery and voluntary activation is further enhanced by neurofeedback. The findings may lead the way towards a non-invasive strategy for endogenous control of dopamine.

Lesion and functional imaging studies in humans have suggested that the dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), and intraparietal sulcus (IPS) are involved in orienting attention. A functional magnetic resonance imaging study supplemented by a behavioral experiment examined the effects of 5 Hz repetitive transcranial magnetic stimulation (rTMS) conditioning to the right and left DLPFC on reaction times and synaptic activity as indexed by changes in the blood oxygenation level-dependent (BOLD) signal during a cued choice reaction time task. Orienting precues were either correct (valid) or incorrect (invalid) with respect to the subsequent move cue. The effects of real and sham rTMS were compared for each site of stimulation. Invalid trials showed a significant increase in response times and increases in the BOLD signal in right frontal and parietal regions when compared with valid trials. Conditioning left DLPFC with rTMS led to decreased BOLD signal during performance of this reorienting task in areas including left VLPFC and left IPS. Comparing invalid to valid trials after right DLPFC conditioning revealed decreased BOLD signal in right VLPFC. Data from the behavioral study showed that right DLPFC rTMS selectively increases response times in invalid trials. This effect was only present in the first 10 min after rTMS conditioning. No effect was found in either validly or invalidly cued trials with left DLPFC conditioning. These results suggest that 5 Hz rTMS over right DLPFC exerts remote effects on the activity of areas that functionally interact with the DLPFC during attentional processes, particularly when the reorienting of attention is more demanding as in invalid trials.

Visual perception is strongly shaped by expectations, but it is poorly understood how such perceptual expectations are learned in our dynamic sensory environment. Here, we applied a Bayesian framework to investigate whether perceptual expectations are continuously updated from different aspects of ongoing experience. In two experiments, human observers performed an associative learning task in which rapidly changing expectations about the appearance of ambiguous stimuli were induced. We found that perception of ambiguous stimuli was biased by both learned associations and previous perceptual outcomes. Computational modeling revealed that perception was best explained by a model that continuously updated priors from associative learning and perceptual history and combined these priors with the current sensory information in a probabilistic manner. Our findings suggest that the construction of visual perception is a highly dynamic process that incorporates rapidly changing expectations from different sources in a manner consistent with Bayesian learning and inference., Frontiers in Human Neuroscience, 10, ISSN:1662-5161

Brain: A Journal of Neurology, 139 (7), ISSN:0006-8950, ISSN:1460-2156

Psychotomimetics like the N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine and the 5-hydroxytryptamine2A receptor (5-HT(2A)R) agonist psilocybin induce psychotic symptoms in healthy volunteers that resemble those of schizophrenia. Recent theories of psychosis posit that aberrant encoding of prediction errors (PE) may underlie the expression of psychotic symptoms. This study used a roving mismatch negativity (MMN) paradigm to investigate whether the encoding of PE is affected by pharmacological manipulation of NMDAR or 5-HT(2A)R, and whether the encoding of PE under placebo can be used to predict drug-induced symptoms. Using a double-blind within-subject placebo-controlled design, S-ketamine and psilocybin, respectively, were administrated to two groups of healthy subjects. Psychological alterations were assessed using a revised version of the Altered States of Consciousness (ASC-R) questionnaire. As an index of PE, we computed changes in MMN amplitudes as a function of the number of preceding standards (MMN memory trace effect) during a roving paradigm. S-ketamine, but not psilocybin, disrupted PE processing as expressed by a frontally disrupted MMN memory trace effect. Although both drugs produced positive-like symptoms, the extent of PE processing under placebo only correlated significantly with the severity of cognitive impairments induced by S-ketamine. Our results suggest that the NMDAR, but not the 5-HT(2A)R system, is implicated in PE processing during the MMN paradigm, and that aberrant PE signaling may contribute to the formation of cognitive impairments. The assessment of the MMN memory trace in schizophrenia may allow detecting early phases of the illness and might also serve to assess the efficacy of novel pharmacological treatments, in particular of cognitive impairments.

Introduction NMDA receptor (NMDAR) antibody encephalitis is an autoimmune disorder causing acute psychosis, confusion and seizures. Electroencephalography (EEG) findings are usually non-specific, with diagnosis confirmed by demonstration of serum/CSF autoantibodies. Improved clinical outcomes depend on early institution of immunotherapies. A non-invasive measure of NMDAR function could facilitate early treatment, and enable tracking of disease activity. Recent biophysical modeling advances allow inference of ion channel dynamics from macroscopic measures of brain activity. Here, we show that clinically relevant diagnoses of NMDAR ion channel dysfunction can be obtained using a microcircuit model fitted to individual human EEG data. Methods We compared resting state EEG cross-spectra from patients with NMDAR encephalitis, encephalopathy unrelated to NMDAR dysfunction, and non-encephalopathic controls. We fit biophysical models (dynamic causal models – DCMs) of connected neuronal ensembles, simulating post-synaptic membrane potentials and EEG spectra through lead field projection. We used canonical variate analysis (CVA) and an empirical Bayesian approach searching candidate models across the groups to reveal specific synaptic deficits. Results Clinical severity was matched between encephalopathic groups. However, there were clear differences in EEG cross-spectra at low (2–4 Hz) and high (28–40 Hz) frequencies, with increased beta-gamma power present in NMDAR antibody encephalitis patients. Subject-specific DCMs mapped model parameters representing dynamics at NMDA, AMPA and GABA receptors to spectral data features. Four inter-connected sources described delta-theta and beta-gamma cross-spectra observed at scalp sensors. Each source comprised a neural mass model with four neuronal subpopulations, with dynamics determined by intrinsic connections mediated via ionotropic receptors. Dynamics along intrinsic and extrinsic connections could reproduce scalp-based EEG recordings. Parameter estimate CVA showed significant differences between patients with and without NMDAR encephalitis in NMDAR parameters. A parametric empirical Bayes (PEB) analysis demonstrated that the group effects of NMDAR encephalitis were specific to NMDA model parameters, in contrast to other encephalopathies where differences were seen across receptor subtypes. Conclusion Neurological and psychiatric practice lack diagnostic probes to assess disease mechanisms at the neuronal level non-invasively in humans. We show that biophysical models of EEG applied to clinical EEG data identify abnormal NMDAR signaling in patients with NMDAR encephalitis but not in patients with other encephalopathies or neurological controls. By comparing model parameter estimates from these patient groups, we selectively identify NMDAR dysfunction in a multi ion-channel model. Given that EEG is ubiquitously available, our findings suggest the feasibility of EEG neuromodeling as a unique assay of brain dysfunction at the molecular-level.

Contains fulltext : 87926.pdf (Publisher’s version ) (Open Access) Theories of the positive symptoms of schizophrenia hypothesize a role for aberrant reinforcement signaling driven by dysregulated dopamine transmission. Recently, we provided evidence of aberrant reward learning in symptomatic, but not asymptomatic patients with schizophrenia, using a novel paradigm, the Salience Attribution Test (SAT). The SAT is a probabilistic reward learning game that employs cues that vary across task-relevant and task-irrelevant dimensions; it provides behavioral indices of adaptive and aberrant reward learning. As an initial step prior to future clinical studies, here we used functional magnetic resonance imaging to examine the neural basis of adaptive and aberrant reward learning during the SAT in healthy volunteers. As expected, cues associated with high relative to low reward probabilities elicited robust hemodynamic responses in a network of structures previously implicated in motivational salience; the midbrain, in the vicinity of the ventral tegmental area, and regions targeted by its dopaminergic projections, i.e. medial dorsal thalamus, ventral striatum and prefrontal cortex (PFC). Responses in the medial dorsal thalamus and polar PFC were strongly correlated with the degree of adaptive reward learning across participants. Finally, and most importantly, differential dorsolateral PFC and middle temporal gyrus (MTG) responses to cues with identical reward probabilities were very strongly correlated with the degree of aberrant reward learning. Participants who showed greater aberrant learning exhibited greater dorsolateral PFC responses, and reduced MTG responses, to cues erroneously inferred to be less strongly associated with reward. The results are discussed in terms of their implications for different theories of associative learning. 8 p.

Zusammenfassung Neurophysiologische und bildgebende Verfahren zur Messung von Hirnaktivitat, wie fMRI oder EEG, werden in den Neurowissenschaften eingesetzt, um Prozesse funktioneller Spezialisierung und funktioneller Integration im menschlichen Gehirn zu untersuchen. Funktionelle Integration kann auf zwei verschiedene Arten beschrieben werden: funktionelle Konnektivitat und effektive Konnektivitat. Wahrend die funktionelle Konnektivitat lediglich statistische Abhangigkeiten zwischen Zeitreihen beschreibt, erfordert das Konzept der effektiven Konnektivitat ein mechanistisches Modell der kausalen Effekte, die den beobachteten Daten zu Grunde liegen. Dieser Artikel fasst die konzeptionellen und methodischen Grundlagen moderner Techniken fur die Analyse funktioneller und effektiver Konnektivitat auf der Basis von fMRI und elektrophysiologischen Daten zusammen. Ein besonderer Schwerpunkt liegt dabei auf dem Dynamic Causal Modelling (DCM), einem neuen Verfahren zur Analyse nichtlinearer neuronaler Systeme. Diese Methode besitzt ein vielversprechendes Potenzial fur klinische Anwendungen, z. B. zur Entschlusselung pathophysiologischer Mechanismen bei Hirnerkrankungen und zur Etablierung neurophysiologisch fundierter diagnostischer Klassifikationen. Abstract Neurophysiological and imaging procedures to measure brain activity, such as fMRI or EEG, are employed in neuroscience to investigate processes of functional specialisation and functional integration in the human brain. Functioal integration can be described in two distinct ways: functional connectivity and effective connectivity. Whereas functional connectivity merely describes the statistical dependence between two time series, the concept of effective connectivity requires a mechanistic model of the causative effects upon which the data to be observed are based. This article summarises the conceptual and methodological principles of modern techniques for the analysis of functional and effective connectivity on the basis of fMRI and electrophysiological data. Particular emphasis is placed on dynamic causal modelling (DCM), a new procedure for the analysis of non-linear neuronal systems. This method has a highly promising potential for clinical applications, e. g., for decoding pathological mechanisms in brain diseases and for the establishment of neurologically valid diagnostic classifications.

The suppression of neuronal responses to a repeated event is a ubiquitous phenomenon in neuroscience. However, the underlying mechanisms remain largely unexplored. The aim of this study was to examine the temporal evolution of experience-dependent changes in connectivity induced by repeated stimuli. We recorded event-related potentials (ERPs) during frequency changes of a repeating tone. Bayesian inversion of dynamic causal models (DCM) of ERPs revealed systematic repetition-dependent changes in both intrinsic and extrinsic connections, within a hierarchical cortical network. Critically, these changes occurred very quickly, over inter-stimulus intervals that implicate short-term synaptic plasticity. Furthermore, intrinsic (within-source) connections showed biphasic changes that were much faster than changes in extrinsic (between-source) connections, which decreased monotonically with repetition. This study shows that auditory perceptual learning is associated with repetition-dependent plasticity in the human brain. It is remarkable that distinct changes in intrinsic and extrinsic connections could be quantified so reliably and non-invasively using EEG.

An important and unresolved question is how the human brain processes speech for meaning after initial analyses in early auditory cortical regions. A variety of left-hemispheric areas have been identified that clearly support semantic processing, although a systematic analysis of directed interactions among these areas is lacking. We applied dynamic causal modeling of functional magnetic resonance imaging responses and Bayesian model selection to investigate, for the first time, experimentally induced changes in coupling among three key multimodal regions that were activated by intelligible speech: the posterior and anterior superior temporal sulcus (pSTS and aSTS, respectively) and pars orbitalis (POrb) of the inferior frontal gyrus. We tested 216 different dynamic causal models and found that the best model was a “forward” system that was driven by auditory inputs into the pSTS, with forward connections from the pSTS to both the aSTS and the POrb that increased considerably in strength (by 76 and 150%, respectively) when subjects listened to intelligible speech. Task-related, directional effects can now be incorporated into models of speech comprehension.

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.

Contains fulltext : 218335.pdf (Publisher’s version ) (Closed access) Models of effective connectivity characterize the influence that neuronal populations exert over each other. Additionally, some approaches, for example Dynamic Causal Modelling (DCM) and variants of Structural Equation Modelling, describe how effective connectivity is modulated by experimental manipulations. Mathematically, both are based on bilinear equations, where the bilinear term models the effect of experimental manipulations on neuronal interactions. The bilinear framework, however, precludes an important aspect of neuronal interactions that has been established with invasive electrophysiological recording studies; i.e., how the connection between two neuronal units is enabled or gated by activity in other units. These gating processes are critical for controlling the gain of neuronal populations and are mediated through interactions between synaptic inputs (e.g. by means of voltage-sensitive ion channels). They represent a key mechanism for various neurobiological processes, including top-down (e.g. attentional) modulation, learning and neuromodulation. This paper presents a nonlinear extension of DCM that models such processes (to second order) at the neuronal population level. In this way, the modulation of network interactions can be assigned to an explicit neuronal population. We present simulations and empirical results that demonstrate the validity and usefulness of this model. Analyses of synthetic data showed that nonlinear and bilinear mechanisms can be distinguished by our extended DCM. When applying the model to empirical fMRI data from a blocked attention to motion paradigm, we found that attention-induced increases in V5 responses could be best explained as a gating of the V1→V5 connection by activity in posterior parietal cortex. Furthermore, we analysed fMRI data from an event-related binocular rivalry paradigm and found that interactions amongst percept-selective visual areas were modulated by activity in the middle frontal gyrus. In both practical examples, Bayesian model selection favoured the nonlinear models over corresponding bilinear ones. 14 p.

Traditionally, anatomical and physiological descriptions of hemispheric specialization have focused on hemispheric asymmetries of local brain structure or local functional properties, respectively. This article reviews the current state of an alternative approach that aims at unraveling the causes and functional principles of hemispheric specialization in terms of asymmetries in connectivity. Starting with an overview of the historical origins of the concept of lateralization, we briefly review recent evidence from anatomical and developmental studies that asymmetries in structural connectivity may be a critical factor shaping hemispheric specialization. These differences in anatomical connectivity, which are found both at the intra- and inter-regional level, are likely to form the structural substrate of different functional principles of information processing in the two hemispheres. The main goal of this article is to describe how these functional principles can be characterized using functional neuroimaging in combination with models of functional and effective connectivity. We discuss the methodology of established models of connectivity which are applicable to data from positron emission tomography and functional magnetic resonance imaging and review published studies that have applied these approaches to characterize asymmetries of connectivity during lateralized tasks. Adopting a model-based approach enables functional imaging to proceed from mere descriptions of asymmetric activation patterns to mechanistic accounts of how these asymmetries are caused.

Contains fulltext : 157563.pdf (Publisher’s version ) (Closed access) High-resolution blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) at the sub-millimeter scale has become feasible with recent advances in MR technology. In principle, this would enable the study of layered cortical circuits, one of the fundaments of cortical computation. However, the spatial layout of cortical blood supply may become an important confound at such high resolution. In particular, venous blood draining back to the cortical surface perpendicularly to the layered structure is expected to influence the measured responses in different layers. Here, we present an extension of a hemodynamic model commonly used for analyzing fMRI data (in dynamic causal models or biophysical network models) that accounts for such blood draining effects by coupling local hemodynamics across layers. We illustrate the properties of the model and its inversion by a series of simulations and show that it successfully captures layered fMRI data obtained during a simple visual experiment. We conclude that for future studies of the dynamics of layered neuronal circuits with high-resolution fMRI, it will be pivotal to include effects of blood draining, particularly when trying to infer on the layer-specific connections in cortex - a theme of key relevance for brain disorders like schizophrenia and for theories of brain function such as predictive coding. 15 p.

Impairment in mismatch negativity (MMN) generation is a robust biological marker of schizophrenia. Understanding the physiological and pharmacological processes involved in its generation may therefore advance our understanding of this complex disorder. The present study tested if acute administration of nicotine modulates human auditory sensory memory as measured with MMN. ERP responses to tone duration deviants were recorded using a stimulation protocol with continuously changing (roving) standard stimuli in order to measure the effect of stimulus repetitions on encoding of new stimuli (MMN memory trace effect). Twenty healthy adult volunteers were randomly assigned to receive either a nicotine gum or placebo after a baseline ERP recording. Nicotine administration augmented MMN amplitude in the treatment group compared to the baseline recording, while no MMN change was found in the placebo group. The drug effect was due to a selective enhancement of a frontal positive potential to standard stimuli (from 80-200 ms post-stimulus), while the negativity to deviants remained unaffected. Furthermore, under nicotine stimulation this repetition positivity showed a more marked increase with stimulus repetition compared to baseline and placebo. These results have potential implications for schizophrenia by suggesting that nicotinic agonists could ameliorate patients' MMN deficits by improving stimulus encoding and sensory memory trace formation.

The anatomy of the corpus callosum has been described in considerable detail. Tracing studies in animals and human post-mortem experiments are currently complemented by diffusion-weighted imaging, which enables non-invasive investigations of callosal connectivity to be conducted. In contrast to the wealth of anatomical data, little is known about the principles by which inter-hemispheric integration is mediated by callosal connections. Most importantly, we lack insights into the mechanisms that determine the functional role of callosal connections in a context-dependent fashion. These mechanisms can now be disclosed by models of effective connectivity that explain neuroimaging data from paradigms which manipulate inter-hemispheric interactions. In this article, we demonstrate that Dynamic Causal Modeling (DCM), in conjunction with Bayesian model selection (BMS), is a powerful approach to disentangling the various factors that determine the functional role of callosal connections. We first review the theoretical foundations of DCM and BMS before demonstrating the application of these techniques to empirical data from a single subject.

Yawning is contagious: Watching another person yawn may trigger us to do the same. Here we studied brain activation with functional magnetic resonance imaging (fMRI) while subjects watched videotaped yawns. Significant increases in the blood oxygen level dependent (BOLD) signal, specific to yawn viewing as contrasted to viewing non-nameable mouth movements, were observed in the right posterior superior temporal sulcus (STS) and bilaterally in the anterior STS, in agreement with the high affinity of STS to social cues. However, no additional yawn-specific activation was observed in Broca's area, the core region of the human mirror-neuron system (MNS) that matches action observation and execution. Thus, activation associated with viewing another person yawn seems to circumvent the essential parts of the MNS, in line with the nature of contagious yawns as automatically released behavioural acts-rather than truly imitated motor patterns that would require detailed action understanding. The subjects' self-reported tendency to yawn covaried negatively with activation of the left periamygdalar region, suggesting a connection between yawn contagiousness and amygdalar activation.

This fMRI study investigates the differences between a blocked and event-related analysis in a cued target detection task, the so-called Posner paradigm, using a hybrid design. Validly and invalidly cued trials were presented intermingled in different blocks containing 50%, 75%, or 100% valid trials. Four analyses were conducted: (i) an event-related analysis comparing invalid and valid trials, (ii) a blocked analysis comparing blocks with 50% valid and invalid trials to blocks with 100% valid trials, (iii) a blocked analysis detecting differences between block models when modeled as epochs or chains of events, and (iv) a blocked analysis that modeled blocks as chains of events to scale regressors equally to the event-related analysis. Irrespective of the type of analysis (blocked or event related), significant activation of the right intraparietal sulcus was observed. A larger cluster size was evident in the blocked analysis, which can be attributed to higher efficiency. In addition to this common right parietal activation, the event-related analysis revealed activations in right superior parietal cortex and left intraparietal sulcus. In contrast, the blocked analysis yielded additional activity in the right occipitoparietal junction. No influences of the block model (epoch versus chain of events) were found in regions activated in the blocked or event-related analysis, respectively. In summary, using a hybrid design and both event-related and blocked analysis techniques, we show both sustained and transient neural processes underlying reorienting of visuospatial attention.

Functional magnetic resonance imaging (fMRI) is used to investigate where the neural implementation of specific cognitive processes occurs. The standard approach uses linear convolution models that relate experimentally designed inputs, through a haemodynamic response function, to observed blood oxygen level dependent (BOLD) signals. Such models are, however, blind to the causal mechanisms that underlie observed BOLD responses. Recent developments have focused on how BOLD responses are generated and include biophysical input-state-output models with neural and haemodynamic state equations and models of functional integration that explain local dynamics through interactions with remote areas. Forward models with parameters at the neural level, such as dynamic causal modelling, combine both approaches, modelling the whole causal chain from external stimuli, via induced neural dynamics, to observed BOLD responses.

The role of the prefrontal cortex remains controversial. Neuroimaging studies support modality-specific and process-specific functions related to working memory and attention. Its role may also be defined by changes in its influence over other brain regions including sensory and motor cortex. We used functional magnetic imaging (fMRI) to study the free selection of actions and colours. Control conditions used externally specified actions and colours. The prefrontal cortex was activated during free selection, regardless of modality, in contrast to modality-specific activations outside prefrontal cortex. Structural equation modelling (SEM) of fMRI data was used to test the hypothesis that although the same regions of prefrontal cortex may be active in tasks within different domains, there is task-dependent effective connectivity between prefrontal cortex and non-prefrontal cortex. The SEM included high-order interactions between modality, selection and regional activity. There was greater coupling between prefrontal cortex and motor cortex during free selection and action tasks, and between prefrontal cortex and visual cortex during free selection of colours. The results suggest that the functions of the prefrontal cortex may be defined not only by selection-specific rather than modality-specific processes, but also by changing patterns of effective connectivity from prefrontal cortex to motor and sensory cortices.

Altered connections between the thalamus and the prefrontal cortex are often implicated in theories concerning the pathogenesis of schizophrenia. Using a model based on anatomically realistic connection patterns between thalamus and prefrontal cortex, we analyzed changes of prefrontal activity resulting from stimulation of the mediodorsal thalamus. Here we demonstrate that the specific influence of thalamo-cortical projections is smaller than the importance of intra-prefrontal connectivity in shaping prefrontal activity patterns. These results might be helpful in explaining experimentally observed hypo- and hyperactive states of prefrontal cortex in schizophrenic patients.

A variety of functional studies have emphasized the importance of the medial temporal lobe (MTL) and prefrontal cortex (PFC) for episodic and working memory, respectively. We investigated the structural basis of information transfer from MTL to PFC in primate cerebral cortex using the CoCoMac database ( www.cocomac.org ), coordinate-independent mapping procedures, and multivariate statistics. In our meta-analysis, we found a clearly decreasing gradient of MTL projections to PFC: strongest from periallocortical regions and weakest from hippocampus proper. These projections roughly divide the PFC into medial and lateral groups of areas.

The neural basis of attention to action was studied in 12 patients with Parkinson's disease (Hoehn and Yahr grades II and III) and 12 healthy age-matched controls. The subjects were studied by functional MRI (fMRI) during performance of a simple paced overlearned motor sequence task, with and without an additional attentional task. For the attentional task, subjects were instructed to attend either to their actions or to a visual distractor task. Statistical parametric mapping was used to implement a random effects analysis of the regional task-related activations in patient and control populations. Structural equation modelling of fMRI time series was used to measure effective connectivity among prefrontal and premotor areas. In both groups, the motor task was associated with activation of a distributed network including the premotor, motor and parietal cortex, striatum and cerebellum. In control subjects, but not patients, attention to action (relative to execution of an overlearned sequence) was associated with further activation of prefrontal, parietal and paracingulate cortex, and the supplementary motor area (SMA). Patients with Parkinson's disease showed greater than normal activation of the SMA during execution of the simple overlearned motor sequence, but less augmentation when attending to their actions. In control subjects, attention to action, but not attention to the visual distractor task, increased the effective connectivity between prefrontal cortex and both the lateral premotor cortex and the SMA. This represents a specific increase in effective connectivity. Attentional modulation of effective connectivity between the prefrontal, premotor cortex and SMA was not observed in patients. This deficit indicates a context-specific functional disconnection between the prefrontal cortex and the supplementary and premotor cortex in Parkinson's disease.

The ability to form long-term memories for novel events depends on information processing within the hippocampus (HC) and entorhinal cortex (EC). The HC–EC circuitry shows a quantitative segregation of anatomical directionality into different neuronal layers. Whereas superficial EC layers mainly project to dentate gyrus (DG), CA3 and apical CA1 layers, HC output is primarily sent from pyramidal CA1 layers and subiculum to deep EC layers. Here we utilize this directionality information by measuring encoding activity within HC/EC subregions with 7 T high resolution functional magnetic resonance imaging (fMRI). Multivariate Bayes decoding within HC/EC subregions shows that processing of novel information most strongly engages the input structures (superficial EC and DG/CA2–3), whereas subsequent memory is more dependent on activation of output regions (deep EC and pyramidal CA1). This suggests that while novelty processing is strongly related to HC–EC input pathways, the memory fate of a novel stimulus depends more on HC–EC output., Nature Communications, 5, ISSN:2041-1723