OCD has been conceptualized as a disorder arising from dysfunctional beliefs, such as overestimating threats or pathological doubts. Yet, how these beliefs lead to compulsions and obsessions remains ...unclear. Here, we develop a computational model to examine the specific beliefs that trigger and sustain compulsive behavior in a simple symptom-provoking scenario. Our results demonstrate that a single belief disturbance–a lack of confidence in the effectiveness of one’s preventive (harm-avoiding) actions–can trigger and maintain compulsions and is directly linked to compulsion severity. This distrust can further explain a number of seemingly unrelated phenomena in OCD, including the role of not-just-right feelings, the link to intolerance to uncertainty, perfectionism, and overestimation of threat, and deficits in reversal and state learning. Our simulations shed new light on which underlying beliefs drive compulsive behavior and highlight the important role of perceived ability to exert control for OCD.
It has been suggested that some psychotic symptoms reflect 'aberrant salience', related to dysfunctional reward learning. To test this hypothesis we investigated whether patients with schizophrenia ...showed impaired learning of task-relevant stimulus-reinforcement associations in the presence of distracting task-irrelevant cues.
We tested 20 medicated patients with schizophrenia and 17 controls on a reaction time game, the Salience Attribution Test. In this game, participants made a speeded response to earn money in the presence of conditioned stimuli (CSs). Each CS comprised two visual dimensions, colour and form. Probability of reinforcement varied over one of these dimensions (task-relevant), but not the other (task-irrelevant). Measures of adaptive and aberrant motivational salience were calculated on the basis of latency and subjective reinforcement probability rating differences over the task-relevant and task-irrelevant dimensions respectively.
Participants rated reinforcement significantly more likely and responded significantly faster on high-probability-reinforced relative to low-probability-reinforced trials, representing adaptive motivational salience. Patients exhibited reduced adaptive salience relative to controls, but the two groups did not differ in terms of aberrant salience. Patients with delusions exhibited significantly greater aberrant salience than those without delusions, and aberrant salience also correlated with negative symptoms. In the controls, aberrant salience correlated significantly with 'introvertive anhedonia' schizotypy.
These data support the hypothesis that aberrant salience is related to the presence of delusions in medicated patients with schizophrenia, but are also suggestive of a link with negative symptoms. The relationship between aberrant salience and psychotic symptoms warrants further investigation in unmedicated patients.
In this paper, we present a generic approach that can be used to infer how subjects make optimal decisions under uncertainty. This approach induces a distinction between a subject's perceptual model, ...which underlies the representation of a hidden "state of affairs" and a response model, which predicts the ensuing behavioural (or neurophysiological) responses to those inputs. We start with the premise that subjects continuously update a probabilistic representation of the causes of their sensory inputs to optimise their behaviour. In addition, subjects have preferences or goals that guide decisions about actions given the above uncertain representation of these hidden causes or state of affairs. From a Bayesian decision theoretic perspective, uncertain representations are so-called "posterior" beliefs, which are influenced by subjective "prior" beliefs. Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden) state of affair. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. Critically, this enables one to "observe the observer", i.e. identify (context- or subject-dependent) prior beliefs and utility-functions using psychophysical or neurophysiological measures. In this paper, we describe the main theoretical components of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions). In a companion paper ('Observing the observer (II): deciding when to decide'), we describe a concrete implementation of it and demonstrate its utility by applying it to simulated and real reaction time data from an associative learning task.
Although the formation of neutrophil (PMN) extracellular traps (NETs) has been detected during infection and sepsis, their role in vivo is still unclear. This study was performed in order to evaluate ...the influence of NETs depletion by administration of recombinant human (rh)DNase on bacterial spreading, PMN tissue infiltration and inflammatory response in a mouse model of polymicrobial sepsis.
In a prospective controlled double-armed animal trial, polymicrobial sepsis was induced by cecal ligation and puncture (CLP). After CLP, mice were treated with rhDNase or phosphate buffered saline, respectively. Survival, colony forming unit (CFU) counts in the peritoneal cavity, lung, liver and blood were determined. PMN and platelet counts, IL-6 and circulating free (cf)-DNA/NETs levels were monitored. PMN infiltration, as well as organ damage, was analyzed histologically in the lungs and liver. Capability and capacity of PMN to form NETs were determined over time.
cf-DNA/NETs were found to be significantly increased 6, 24, and 48 hours after CLP when compared to the levels determined in sham and naïve mice. Peak levels after 24 hours were correlated to enhanced capacity of bone marrow-derived PMN to form NETs after ex vivo stimulation with phorbol-12-myristate-13-acetate at the same time. rhDNase treatment of mice resulted in a significant reduction of cf-DNA/NETs levels 24 hours after CLP (P < 0.001). Although overall survival was not affected by rhDNase treatment, median survival after 24 hours was significantly lower when compared with the CLP group (P < 0.01). In mice receiving rhDNase treatment, CFU counts in the lung (P < 0.001) and peritoneal cavity (P < 0.05), as well as serum IL-6 levels (P < 0.001), were found to be already increased six hours after CLP. Additionally, enhanced PMN infiltration and tissue damage in the lungs and liver were found after 24 hours. In contrast, CFU counts in mice without rhDNase treatment increased later but more strongly 24 hours after CLP (P < 0.001). Similarly, serum IL-6 levels peaked after 24 hours (P < 0.01).
This study shows, for the first time, that depletion of NETs by rhDNase administration impedes the early immune response and aggravates the pathology that follows polymicrobial sepsis in vivo.
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.
Acetylcholine (ACh) is a neuromodulatory transmitter implicated in perception and learning under uncertainty. This study combined computational simulations and pharmaco-electroencephalography in ...humans, to test a formulation of perceptual inference based upon the free energy principle. This formulation suggests that ACh enhances the precision of bottom-up synaptic transmission in cortical hierarchies by optimizing the gain of supragranular pyramidal cells. Simulations of a mismatch negativity paradigm predicted a rapid trial-by-trial suppression of evoked sensory prediction error (PE) responses that is attenuated by cholinergic neuromodulation. We confirmed this prediction empirically with a placebo-controlled study of cholinesterase inhibition. Furthermore, using dynamic causal modeling, we found that drug-induced differences in PE responses could be explained by gain modulation in supragranular pyramidal cells in primary sensory cortex. This suggests that ACh adaptively enhances sensory precision by boosting bottom-up signaling when stimuli are predictable, enabling the brain to respond optimally under different levels of environmental uncertainty.
Atrial cardiomyocytes are essential for fluid homeostasis, ventricular filling, and survival, yet their cell biology and physiology are incompletely understood. It has become clear that the cell fate ...of atrial cardiomyocytes depends significantly on transcription programs that might control thousands of differentially expressed genes. Atrial muscle membranes propagate action potentials and activate myofilament force generation, producing overall faster contractions than ventricular muscles. While atria-specific excitation and contractility depend critically on intracellular Ca2+ signalling, voltage-dependent L-type Ca2+ channels and ryanodine receptor Ca2+ release channels are each expressed at high levels similar to ventricles. However, intracellular Ca2+ transients in atrial cardiomyocytes are markedly heterogeneous and fundamentally different from ventricular cardiomyocytes. In addition, differential atria-specific K+ channel expression and trafficking confer unique electrophysiological and metabolic properties. Because diseased atria have the propensity to perpetuate fast arrhythmias, we discuss our understanding about the cell-specific mechanisms that lead to metabolic and/or mitochondrial dysfunction in atrial fibrillation. Interestingly, recent work identified potential atria-specific mechanisms that lead to early contractile dysfunction and metabolic remodelling, suggesting highly interdependent metabolic, electrical, and contractile pathomechanisms. Hence, the objective of this review is to provide an integrated model of atrial cardiomyocytes, from tissue-specific cell properties, intracellular metabolism, and excitation–contraction (EC) coupling to early pathological changes, in particular metabolic dysfunction and tissue remodelling due to atrial fibrillation and aging. This article is part of a Special Issue entitled: Cardiomyocyte Biology: Integration of Developmental and Environmental Cues in the Heart edited by Marcus Schaub and Hughes Abriel.
•We highlight atria-specific regulatory mechanisms of ion channels and organelles relevant for excitation, contraction and metabolism.•Point out characteristics at the level of gene transcription, protein expression and intracellular trafficking.•In atria, Ca2+ transients are markedly heterogeneous and EC-coupling is significantly different from ventricles.•Atrial tissue dysregulation in rapid pacing models and atrial fibrillation identified potentially synergistic disease mechanisms.•Early disease remodelling and aging-associated mechanisms of atrial dysfunction established conceptual links to Ca2+ and redox metabolism.
Successful interaction with the environment requires flexible updating of our beliefs about the world. By estimating the likelihood of future events, it is possible to prepare appropriate actions in ...advance and execute fast, accurate motor responses. According to theoretical proposals, agents track the variability arising from changing environments by computing various forms of uncertainty. Several neuromodulators have been linked to uncertainty signalling, but comprehensive empirical characterisation of their relative contributions to perceptual belief updating, and to the selection of motor responses, is lacking. Here we assess the roles of noradrenaline, acetylcholine, and dopamine within a single, unified computational framework of uncertainty. Using pharmacological interventions in a sample of 128 healthy human volunteers and a hierarchical Bayesian learning model, we characterise the influences of noradrenergic, cholinergic, and dopaminergic receptor antagonism on individual computations of uncertainty during a probabilistic serial reaction time task. We propose that noradrenaline influences learning of uncertain events arising from unexpected changes in the environment. In contrast, acetylcholine balances attribution of uncertainty to chance fluctuations within an environmental context, defined by a stable set of probabilistic associations, or to gross environmental violations following a contextual switch. Dopamine supports the use of uncertainty representations to engender fast, adaptive responses.
•I study the effect of large oil field discoveries on education provision in the early 20th century United States.•Counties that discovered oil wealth experienced increases in tax renevues and ...education spending.•I find mixed evidence for improved education quality.
This paper studies the effect of oil wealth on the provision of education in the early 20th century United States. Using information on the location and discovery of major oil fields, I find that oil wealth increased local revenue and education spending. However, population increased, and as consequence, schooling quality did not improve across the board. Nominal teacher wages increased, and oil-rich counties were more likely to participate in the Rosenwald school building program for blacks. However, neither student-teacher ratios nor school attendance rates improved in the wake of oil discoveries.
Comparing families of dynamic causal models Penny, Will D; Stephan, Klaas E; Daunizeau, Jean ...
PLOS computational biology/PLoS computational biology,
03/2010, Letnik:
6, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one ...first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This "best model" approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data.
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