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Re-directing attention to objects in working memory can enhance their representational fidelity. However, how this attentional enhancement of memory representations is implemented across distinct, sensory and cognitive-control brain network is unspecified. The present fMRI experiment leverages psychophysical modelling and multivariate auditory-pattern decoding as behavioral and neural proxies of mnemonic fidelity. Listeners performed an auditory syllable pitch-discrimination task and received retro-active cues to selectively attend to a to-be-probed syllable in memory. Accompanied by increased neural activation in fronto-parietal and cingulo-opercular networks, valid retro-cues yielded faster and more perceptually sensitive responses in recalling acoustic detail of memorized syllables. Information about the cued auditory object was decodable from hemodynamic response patterns in superior temporal sulcus (STS), fronto-parietal, and sensorimotor regions. However, among these regions retaining auditory memory objects, neural fidelity in the left STS and its enhancement through attention-to-memory best predicted individuals’ gain in auditory memory recall precision. Our results demonstrate how functionally discrete brain regions differentially contribute to the attentional enhancement of memory representations.

Dopamine underlies important aspects of cognition, and has been suggested to boost cognitive performance. However, how dopamine modulates the large-scale cortical dynamics during cognitive performance has remained elusive. Using functional MRI during a working memory task in healthy young human listeners (N=22), we investigated the effect of levodopa (L-dopa) on two aspects of cortical dynamics, blood oxygen-level-dependent (BOLD) signal variability and the functional connectome of large-scale cortical networks. We here show that enhanced dopaminergic signaling modulates the two potentially interrelated aspects of large-scale cortical dynamics during cognitive performance, and the degree of these modulations is able to explain inter-individual differences in L-dopa-induced behavioral benefits. Relative to placebo, L-dopa increased BOLD signal variability in task-relevant temporal, inferior frontal, parietal and cingulate regions. On the connectome level, however, L-dopa diminished functional integration across temporal and cingulo-opercular regions. This hypo-integration was expressed as a reduction in network efficiency and modularity in more than two thirds of the participants and to different degrees. Hypo-integration co-occurred with relative hyper-connectivity in paracentral lobule and precuneus, as well as posterior putamen. Both, L-dopa-induced BOLD signal variability modulation and functional connectome modulations proved predictive of an individual’s L-dopa-induced gain in behavioral performance, namely response speed and perceptual sensitivity. Lastly, L-dopa-induced modulations of BOLD signal variability were correlated with L-dopa-induced modulation of nodal connectivity and network efficiency. Our findings underline the role of dopamine in maintaining the dynamic range of, and communication between, cortical systems, and their explanatory power for inter-individual differences in benefits from dopamine during cognitive performance.

Theoretical models and preceding studies have described age-related alterations in neuronal activation of frontoparietal regions in a working memory (WM) load-dependent manner. However, to date, underlying neuronal mechanisms of these WM load-dependent activation changes in aging remain poorly understood. The aim of this study was to investigate these mechanisms in terms of effective connectivity by application of dynamic causal modeling with Bayesian Model Selection. Eighteen healthy younger (age: 20-32 years) and 32 older (60-75 years) participants performed an n-back task with 3 WM load levels during functional magnetic resonance imaging (fMRI). Behavioral and conventional fMRI results replicated age group by WM load interactions. Importantly, the analysis of effective connectivity derived from dynamic causal modeling, indicated an age- and performance-related reduction in WM load-dependent modulation of connectivity from dorsolateral prefrontal cortex to inferior parietal lobule. This finding provides evidence for the proposal that age-related WM decline manifests as deficient WM load-dependent modulation of neuronal top-down control and can integrate implications from theoretical models and previous studies of functional changes in the aging brain.

Schizophrenia is a heterogeneous spectrum disorder often associated with detrimental negative symptoms. In recent years, computational approaches to psychiatry have attracted growing attention. Negative symptoms have shown some overlap with general cognitive impairments and were also linked to impaired motivational processing in brain circuits implementing reward prediction. In this review, we outline how computational approaches may help to provide a better understanding of negative symptoms in terms of the potentially underlying behavioural and biological mechanisms. First, we describe the idea that negative symptoms could arise from a failure to represent reward expectations to enable flexible behavioural adaptation. It has been proposed that these impairments arise from a failure to use prediction errors to update expectations. Important previous studies focused on processing of so-called model-free prediction errors where learning is determined by past rewards only. However, learning and decision-making arise from multiple cognitive mechanisms functioning simultaneously, and dissecting them via well-designed tasks in conjunction with computational modelling is a promising avenue. Second, we move on to a proof-of-concept example on how generative models of functional imaging data from a cognitive task enable the identification of subgroups of patients mapping on different levels of negative symptoms. Combining the latter approach with behavioural studies regarding learning and decision-making may allow the identification of key behavioural and biological parameters distinctive for different dimensions of negative symptoms versus a general cognitive impairment. We conclude with an outlook on how this computational framework could, at some point, enrich future clinical studies.

Dopamine potentially unites two important roles: one in addiction, being involved in most substances of abuse including alcohol, and a second one in a specific type of learning, namely model-free temporal-difference reinforcement learning. Theories of addiction have long suggested that drugs of abuse may usurp dopamine's role in learning. Here, we briefly review the preclinical literature to motivate specific hypotheses about model-free temporal-difference learning and then review the imaging evidence in the drug of abuse with the most substantial societal consequences: alcohol. Despite the breadth of the literature, only a few studies have examined the predictions directly, and these provide at best inconclusive evidence for the involvement of temporal-difference learning alterations in alcohol dependence. We discuss the difficulties of testing the theory in humans, make specific suggestions, and close with a focus on the interaction with other learning mechanisms.

Background Generative models of neuroimaging data, such as dynamic causal models (DCMs), are commonly used for inferring effective connectivity from individual subject data. Recently introduced “generative embedding” approaches have used DCM-based connectivity parameters for supervised classification of individual patients or to find unknown subgroups in heterogeneous groups using unsupervised clustering methods. New method We present a novel framework which combines DCMs with finite mixture models into a single hierarchical model. This approach unifies the inference of connectivity parameters in individual subjects with inference on population structure, i.e. the existence of subgroups defined by model parameters, and allows for empirical Bayesian estimates of a subject’s connectivity based on subgroup-specific prior distributions. We introduce a Markov chain Monte Carlo sampling method for inversion of this hierarchical generative model. Results This paper formally introduces the idea behind our novel concept and demonstrates the face validity of the model in application to both simulated data as well as an empirical fMRI dataset from healthy controls and patients with schizophrenia. Comparison with existing method(s) The analysis of our empirical fMRI data demonstrates that our approach results in superior model evidence than the conventional non-hierarchical inversion of DCMs. Conclusions In this paper, we have presented a novel unified framework to jointly infer the effective connectivity parameters in DCMs for multiple subjects and, at the same time, discover connectivity-defined cluster structure of the whole population, using a mixture model approach.

In the present study, we explored possible alterations in the default mode network (DMN) and its functional connectivity in 41 schizophrenia patients and 42 age-matched healthy controls. Schizophrenia patients displayed reduced activation in the ventromedial prefrontal cortex, left superior temporal gyrus including auditory cortex and temporal pole. Psychophysiological interaction analysis revealed reduced connectivity between left superior temporal gyrus including auditory cortex and the left temporal pole in schizophrenia patients compared to healthy subjects.

It is suggested that acute stress shifts behavioral control from goal-directed, model-based toward habitual, model-free strategies. Recent findings indicate that interindividual differences in the cortisol stress response influence model-based decision-making. Although not yet investigated in humans, animal studies show that chronic stress also shifts decision-making toward more habitual behavior. Here, we ask whether acute stress and individual vulnerability factors, such as stress reactivity and previous exposure to stressful life events, impact the balance between model-free and model-based control systems. To test this, 39 male participants (21-30 years old) were exposed to a potent psychosocial stressor (Trier Social Stress Test) and a control condition in a within-subjects design before they performed a sequential decision-making task which evaluates the balance between the two systems. Physiological and subjective stress reactivity was assessed before, during, and after acute stress exposure. By means of computational modeling, we demonstrate that interindividual variability in stress reactivity predicts impairments in model-based decision-making. Whereas acute psychosocial stress did not alter model-based behavioral control, we found chronic and acute stress to interact in their detrimental effect on decision-making: subjects with high but not low chronic stress levels as indicated by stressful life events exhibited reduced model-based control in response to acute psychosocial stress. These findings emphasize that stress reactivity and chronic stress play an important role in mediating the relationship between stress and decision-making. Our results might stimulate new insights into the interplay between chronic and acute stress, attenuated model-based control, and the pathogenesis of various psychiatric diseases.

This proof-of-concept study examines the feasibility of defining subgroups in psychiatric spectrum disorders by generative embedding, using dynamical system models which infer neuronal circuit mechanisms from neuroimaging data. To this end, we re-analysed an fMRI dataset of 41 patients diagnosed with schizophrenia and 42 healthy controls performing a numerical n-back working-memory task. In our generative-embedding approach, we used parameter estimates from a dynamic causal model (DCM) of a visual–parietal–prefrontal network to define a model-based feature space for the subsequent application of supervised and unsupervised learning techniques. First, using a linear support vector machine for classification, we were able to predict individual diagnostic labels significantly more accurately (78%) from DCM-based effective connectivity estimates than from functional connectivity between (62%) or local activity within the same regions (55%). Second, an unsupervised approach based on variational Bayesian Gaussian mixture modelling provided evidence for two clusters which mapped onto patients and controls with nearly the same accuracy (71%) as the supervised approach. Finally, when restricting the analysis only to the patients, Gaussian mixture modelling suggested the existence of three patient subgroups, each of which was characterised by a different architecture of the visual–parietal–prefrontal working-memory network. Critically, even though this analysis did not have access to information about the patients' clinical symptoms, the three neurophysiologically defined subgroups mapped onto three clinically distinct subgroups, distinguished by significant differences in negative symptom severity, as assessed on the Positive and Negative Syndrome Scale (PANSS). In summary, this study provides a concrete example of how psychiatric spectrum diseases may be split into subgroups that are defined in terms of neurophysiological mechanisms specified by a generative model of network dynamics such as DCM. The results corroborate our previous findings in stroke patients that generative embedding, compared to analyses of more conventional measures such as functional connectivity or regional activity, can significantly enhance both the interpretability and performance of computational approaches to clinical classification., Graphical abstract, Highlights • Proof-of-concept study for defining subgroups in psychiatric spectrum diseases. • We re-analysed an fMRI dataset of 41 schizophrenic patients and 42 controls. • DCM parameters were highly predictive of diagnostic status (78% accuracy). • Unsupervised clustering of patients suggested three subgroups. • These groups were clinically distinct and differed in their negative symptom severity.

Cognitive deficits like working memory impairment in schizophrenia are of great importance for clinical outcome, but the underlying neurobiology is not fully understood. During working memory (WM) altered connectivity patterns in the fronto-parietal network are present in patients suffering from schizophrenia [1–3]. One candidate biochemical marker for the integrity of connectivity is glutamate [4]. Here we tested for group differences in fronto-parietal connectivity and it’s relation to possible glutamatergic influences. During a functional magnetic resonance imaging (fMRI) scan, a sample of 42 medicated patients (SZ) and 41 age and gender matched healthy controls (HC) were asked to perform a numeric n-back working memory task, which consisted of two conditions “2-back” and “0-back”. FMRI was conducted on a 3T Siemens Trio scanner with a 12 channel head coil using gradient-echo echo-planar imaging. Data were further preprocessed using the statistical parametric mapping (SPM 8; Welcome Department of Imaging Neuroscience, London, UK; htt://www.fil.ion.ucl.ac.uk/spm) in MATLAB 2009b. We performed dynamic causal modeling [5] (DCM) on a model space comprising regions of interest for a visual input, parietal (PC) and dorsolateral prefrontal cortex (DLPFC). We calculated Bayesian model averages to obtain weighted connectivity parameters. One-sample t-tests were calculated on parameters for PC->DLPFC and DLPFC->PC connectivity within each group in order to evaluate modulatory effects of working memory on the executive network. Glutamate levels were measured in left DLPFC using magnetic resonance spectroscopy. We used LCmodel (Linear Combination of Model spectra, a commercial spectral fitting package) to estimate local glutamate concentration. Absolute glutamate concentrations were adjusted for grey and white matter volume. We performed group comparisons (two-sample t-test) on connectivity parameters as well as on glutamate levels. Due to non-normality two-sided Spearman correlation analysis were calculated between connectivity parameters and glutamate levels. Working memory dependent connectivity effects (between PC and DLPFC) could be observed in the left hemisphere on backward connections (PC->DLPFC, HC: t=2.77, p=0.008; SZ: t=2.62, p=0.012), whereas no significant working memory effects were present on DLPFC->PC connectivity. We found no group difference in fronto-parietal connectivity parameters and no difference in glutamate levels. Nonetheless numerically controls show higher glutamate levels as compared to patients. To explore possible effects of local Glutamate levels on working memory dependent connectivity, we correlated parameters for PC->DLPFC connectivity (where we found significant working memory dependent effects) with glutamate levels. We found a significant negative association between parieto-frontal connectivity and glutamate in DLPFC in patients (rho=-0.47, p=0.0035). Controls did not show any significant association (rho=-0.12, p=0.53). Comparing correlation coefficients, we found a trend-wise significant difference between groups (z=1.55, p=0.06). Although our data neither showed a difference in connectivity nor in glutamate levels, our findings suggest that glutamate is differentially related to working memory dependent connectivity from parietal to frontal areas in patients as compared to controls.