Neurocomputational account of how the human brain decides when to have a break.
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ABSTRACT:
SUBMITTER: Meyniel F
PROVIDER: S-EPMC3574948 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAnxious hypervigilance is marked by sensitized sensory-perceptual processes and attentional biases to potential danger cues in the environment. How this is realized at the neurocomputational level is unknown but could clarify the brain mechanisms disrupted in psychiatric conditions such as posttraumatic stress disorder. Predictive coding, instantiated by dynamic causal models, provides a promising framework to ground these state-related changes in the dynamic interactions of reciprocally connected brain areas.MethodsAnxiety states were elicited in healthy participants (n = 19) by exposure to the threat of unpredictable, aversive shocks while undergoing magnetoencephalography. An auditory oddball sequence was presented to measure cortical responses related to deviance detection, and dynamic causal models quantified deviance-related changes in effective connectivity. Participants were also administered alprazolam (double-blinded, placebo-controlled crossover) to determine whether the cortical effects of threat-induced anxiety are reversed by acute anxiolytic treatment.ResultsDeviant tones elicited increased auditory cortical responses under threat. Bayesian analyses revealed that hypervigilant responding was best explained by increased postsynaptic gain in primary auditory cortex activity as well as modulation of feedforward, but not feedback, coupling within a temporofrontal cortical network. Increasing inhibitory gamma-aminobutyric acidergic action with alprazolam reduced anxiety and restored feedback modulation within the network.ConclusionsThreat-induced anxiety produced unbalanced feedforward signaling in response to deviations in predicable sensory input. Amplifying ascending sensory prediction error signals may optimize stimulus detection in the face of impending threats. At the same time, diminished descending sensory prediction signals impede perceptual learning and may, therefore, underpin some of the deleterious effects of anxiety on higher-order cognition.
| S-EPMC5584579 | biostudies-literature
Project description:BackgroundInflammation rapidly impairs mood and cognition and, when severe, can appear indistinguishable from major depression. These sickness responses are characterized by an acute reorientation of motivational state; pleasurable activities are avoided, and sensitivity to negative stimuli is enhanced. However, it remains unclear how these rapid shifts in behavior are mediated within the brain.MethodsHere, we combined computational modeling of choice behavior, experimentally induced inflammation, and functional brain imaging (functional magnetic resonance imaging) to describe these mechanisms. Using a double-blind, randomized crossover study design, 24 healthy volunteers completed a probabilistic instrumental learning task on two separate occasions, one 3 hours after typhoid vaccination and one 3 hours after saline (placebo) injection. Participants learned to select high probability reward (win £1) and avoid high probability punishment (lose £1) stimuli. An action-value learning algorithm was fit to the observed behavior, then used within functional magnetic resonance imaging analyses to identify neural coding of prediction error signals driving motivational learning.ResultsInflammation acutely biased behavior, enhancing punishment compared with reward sensitivity, through distinct actions on neural representations of reward and punishment prediction errors within the ventral striatum and anterior insula. Consequently, choice options leading to potential rewards were less behaviorally attractive, and those leading to punishments were more aversive.ConclusionsOur findings demonstrate the neural mediation of a rapid, state-dependent reorientation of reward versus punishment sensitivity during inflammation. This mechanism may aid the adaptive reallocation of metabolic resources during acute sickness but might also account for maladaptive, motivational changes that underpin the association between chronic inflammation and depression.
| S-EPMC4918729 | biostudies-literature
Project description:A new filling-in model is proposed in order to account for challenging brightness illusions, where inducing background elements are spatially separated from the gray target such as dungeon, cube and grating illusions, bullseye display and ring patterns. This model implements the simple idea that neural response to low-contrast contour is enhanced (facilitated) by the presence of collinear or parallel high-contrast contours in its wider neighborhood. Contour facilitation is achieved via dendritic inhibition, which enables the computation of maximum function among inputs to the node. Recurrent application of maximum function leads to the propagation of the neural signal along collinear or parallel contour segments. When a strong global-contour signal is accompanied with a weak local-contour signal at the same location, conditions are met to produce brightness assimilation within the Filling-in Layer. Computer simulations showed that the model correctly predicts brightness appearance in all of the aforementioned illusions as well as in White's effect, Benary's cross, Todorović's illusion, checkerboard contrast, contrast-contrast illusion and various variations of the White's effect. The proposed model offers new insights on how geometric factors (contour colinearity or parallelism), together with contrast magnitude contribute to the brightness perception.
| S-EPMC4333805 | biostudies-literature
Project description:Computational models of reinforcement learning have helped dissect discrete components of reward-related function and characterize neurocognitive deficits in psychiatric illnesses. Stimulus novelty biases decision-making, even when unrelated to choice outcome, acting as if possessing intrinsic reward value to guide decisions toward uncertain options. Heightened novelty seeking is characteristic of attention deficit hyperactivity disorder, yet how this influences reward-related decision-making is computationally encoded, or is altered by stimulant medication, is currently uncertain. Here we used an established reinforcement-learning task to model effects of novelty on reward-related behaviour during functional MRI in 30 adults with attention deficit hyperactivity disorder and 30 age-, sex- and IQ-matched control subjects. Each participant was tested on two separate occasions, once ON and once OFF stimulant medication. OFF medication, patients with attention deficit hyperactivity disorder showed significantly impaired task performance (P = 0.027), and greater selection of novel options (P = 0.004). Moreover, persistence in selecting novel options predicted impaired task performance (P = 0.025). These behavioural deficits were accompanied by a significantly lower learning rate (P = 0.011) and heightened novelty signalling within the substantia nigra/ventral tegmental area (family-wise error corrected P < 0.05). Compared to effects in controls, stimulant medication improved attention deficit hyperactivity disorder participants' overall task performance (P = 0.011), increased reward-learning rates (P = 0.046) and enhanced their ability to differentiate optimal from non-optimal novel choices (P = 0.032). It also reduced substantia nigra/ventral tegmental area responses to novelty. Preliminary cross-sectional evidence additionally suggested an association between long-term stimulant treatment and a reduction in the rewarding value of novelty. These data suggest that aberrant substantia nigra/ventral tegmental area novelty processing plays an important role in the suboptimal reward-related decision-making characteristic of attention deficit hyperactivity disorder. Compared to effects in controls, abnormalities in novelty processing and reward-related learning were improved by stimulant medication, suggesting that they may be disorder-specific targets for the pharmacological management of attention deficit hyperactivity disorder symptoms.
| S-EPMC5917772 | biostudies-literature
Project description:To make decisions based on the value of different options, we often have to combine different sources of probabilistic evidence. For example, when shopping for strawberries on a fruit stand, one uses their color and size to infer-with some uncertainty-which strawberries taste best. Despite much progress in understanding the neural underpinnings of value-based decision making in humans, it remains unclear how the brain represents different sources of probabilistic evidence and how they are used to compute value signals needed to drive the decision. Here, we use a visual probabilistic categorization task to show that regions in ventral temporal cortex encode probabilistic evidence for different decision alternatives, while ventromedial prefrontal cortex integrates information from these regions into a value signal using a difference-based comparator operation.
| S-EPMC2889112 | biostudies-literature
Project description:RationaleRepeated haloperidol treatment in rodents results in a day-to-day intensification of catalepsy (i.e., sensitization). Prior experiments suggest that this sensitization is context-dependent and resistant to extinction training.ObjectivesThe aim of this study was to provide a neurobiological mechanistic explanation for these findings.Materials and methodsWe use a neurocomputational model of the basal ganglia and simulate two alternative models based on the reward prediction error and novelty hypotheses of dopamine function. We also conducted a behavioral rat experiment to adjudicate between these models. Twenty male Sprague-Dawley rats were challenged with 0.25 mg/kg haloperidol across multiple days and were subsequently tested in either a familiar or novel context.ResultsSimulation results show that catalepsy sensitization, and its context dependency, can be explained by "NoGo" learning via simulated D2 receptor antagonism in striatopallidal neurons, leading to increasingly slowed response latencies. The model further exhibits a non-extinguishable component of catalepsy sensitization due to latent NoGo representations that are prevented from being expressed, and therefore from being unlearned, during extinction. In the rat experiment, context dependency effects were not dependent on the novelty of the context, ruling out the novelty model's account of context dependency.ConclusionsSimulations lend insight into potential complex mechanisms leading to context-dependent catalepsy sensitization, extinction, and renewal.
| S-EPMC3049926 | biostudies-literature
Project description:The xeroderma pigmentosum complementation group E (XP-E) gene product damaged-DNA binding protein 2 (DDB2) plays important roles in nucleotide excision repair (NER). Previously, we showed that DDB2 participates in NER by regulating the level of p21(Waf1/Cip1). Here we show that the p21(Waf1/Cip1) -regulatory function of DDB2 plays a central role in defining the response (apoptosis or arrest) to DNA damage. The DDB2-deficient cells are resistant to apoptosis in response to a variety of DNA-damaging agents, despite activation of p53 and the pro-apoptotic genes. Instead, these cells undergo cell cycle arrest. Also, the DDB2-deficient cells are resistant to E2F1-induced apoptosis. The resistance to apoptosis of the DDB2-deficient cells is caused by an increased accumulation of p21(Waf1/Cip1) after DNA damage. We provide evidence that DDB2 targets p21(Waf1/Cip1) for proteolysis. The resistance to apoptosis in DDB2-deficient cells also involves Mdm2 in a manner that is distinct from the p53-regulatory activity of Mdm2. Our results provide evidence for a new regulatory loop involving the NER protein DDB2, Mdm2, and p21(Waf1/Cip1) that is critical in deciding cell fate (apoptosis or arrest) upon DNA damage.
| S-EPMC2705559 | biostudies-literature
Project description:We present a hypothetical neurocomputational model that combines a set of neural circuits at the molecular, cellular, and system levels and accounts for several neurobiological and behavioral processes leading to nicotine addiction. We propose that combining changes in the nicotinic receptor response, expressed by mesolimbic dopaminergic neurons, with dopamine-gated learning in action-selection circuits, suffices to capture the acquisition of nicotine addiction. We show that an opponent process enhanced by persistent nicotine-taking renders self-administration rigid and habitual by inhibiting the learning process, resulting in long-term impairments in the absence of the drug. The model implies distinct thresholds on the dosage and duration for the acquisition and persistence of nicotine addiction. Our hypothesis unites a number of prevalent ideas on nicotine action into a coherent formal network for further understanding of compulsive drug addiction.
| S-EPMC1348008 | biostudies-literature
Project description:Changes in functional network connectivity following traumatic brain injury (TBI) have received increasing attention in recent neuroimaging literature. This study sought to understand how disrupted systems adapt to injury during resting and goal-directed brain states. Hyperconnectivity has been a common finding, and dedifferentiation (or loss of segregation of networks) is one possible explanation for this finding. We hypothesized that individuals with TBI would show dedifferentiation of networks (as noted in other clinical populations) and these effects would be associated with cognitive dysfunction.Graph theory was implemented to examine functional connectivity during periods of task and rest in 19 individuals with moderate/severe TBI and 14 healthy controls (HCs). Using a functional brain atlas derived from 83 functional imaging studies, graph theory was used to examine network dynamics and determine whether dedifferentiation accounts for changes in connectivity. Regions of interest were assigned to one of three groups: task-positive, default mode, or other networks. Relationships between these metrics were then compared with performance on neuropsychological tests.Hyperconnectivity in TBI was most commonly observed as increased within-network connectivity. Network strengths within networks that showed differences between TBI and HCs were correlated with performance on five neuropsychological tests typically sensitive to deficits commonly reported in TBI. Hyperconnectivity within the default mode network (DMN) during task was associated with better performance on Digit Span Backward, a measure of working memory [R2(18) = 0.28, p = 0.02]. In other words, increased differentiation of networks during task was associated with better working memory. Hyperconnectivity within the task-positive network during rest was not associated with behavior. Negative correlation weights were not associated with behavior.The primary hypothesis that hyperconnectivity occurs through increased segregation of networks, rather than dedifferentiation, was not supported. Instead, enhanced connectivity post injury was observed within network. Results suggest that the relationship between increased connectivity and cognitive functioning may be both state (rest or task) and network dependent. High-cost network hubs were identical for both rest and task, and cost was negatively associated with performance on measures of psychomotor speed and set-shifting.
| S-EPMC5512341 | biostudies-other
Project description:The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering--a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN - in terms of latency and amplitude--and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and--more generally--illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses.
| S-EPMC3820518 | biostudies-literature