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Persistent affective biases in human amygdala response following implicit priming with negative emotion concepts.
To what extent do past experiences shape our behaviors, perceptions, and thoughts even without explicit knowledge of these influences? Behavioral research has demonstrated that various cognitive processes can be influenced by conceptual representations implicitly primed during a preceding and unrelated task. Here we investigated whether emotion processing might also be influenced by prior incidental exposure to negative semantic material and which neural substrates would mediate these effects. During a first (priming) task, participants performed a variant of the hangman game with either negative or neutral emotion-laden words. Subsequently, they performed a second, unrelated visual task with fearful and neutral faces presented at attended or unattended locations. Participants were generally not aware of any relationships between the two tasks. We found that priming with emotional words enhanced amygdala sensitivity to faces in the subsequent visual task, while decreasing discriminative responses to threat. Furthermore, the magnitude of the induced bias in behavior and amygdala activation was predicted by the effectiveness of semantic access observed in the priming task. This demonstrates that emotional processing can be modulated by implicit influence of environmental information processed at an earlier time, independently of volitional control.
Brain systems underlying encounter expectancy bias in spider phobia.
Spider-phobic individuals are characterized by exaggerated expectancies to be faced with spiders (so-called encounter expectancy bias). Whereas phobic responses have been linked to brain systems mediating fear, little is known about how the recruitment of these systems relates to exaggerated expectancies of threat. We used fMRI to examine spider-phobic and control participants while they imagined visiting different locations in a forest after having received background information about the likelihood of encountering different animals (spiders, snakes, and birds) at these locations. Critically, imagined encounter expectancies modulated brain responses differently in phobics as compared with controls. Phobics displayed stronger negative modulation of activity in the lateral prefrontal cortex, precuneus, and visual cortex by encounter expectancies for spiders, relative to snakes or birds (within-participants analysis); these effects were not seen in controls. Between-participants correlation analyses within the phobic group further corroborated the hypothesis that these phobia-specific modulations may underlie irrationality in encounter expectancies (deviations of encounter expectancies from objective background information) in spider phobia; the greater the negative modulation a phobic participant displayed in the lateral prefrontal cortex, precuneus, and visual cortex, the stronger was her bias in encounter expectancies for spiders. Interestingly, irrationality in expectancies reflected in frontal areas relied on right rather than left hemispheric deactivations. Our data accord with the idea that expectancy biases in spider phobia may reflect deficiencies in cognitive control and contextual integration that are mediated by right frontal and parietal areas.
The influence of individual motor imagery ability on cerebral recruitment during gait imagery.
Motor imagery (MI) is often used in combination with neuroimaging techniques to study the cognitive control of gait. However, imagery ability (IA) varies widely across individuals, potentially influencing the pattern of cerebral recruitment during MI. The aim of the current study was to investigate this effect of IA on the neural correlates of gait control using functional magnetic resonance imaging (fMRI). Twenty healthy young subjects were subdivided into a good and bad imagers group, on the basis of their performance on two mental chronometry tests. For the whole group, MI activated a bilateral network of areas highly consistent with previous studies, encompassing primary motor cortex (BA 4), supplementary motor area, and other frontal and parietal areas, anterior insula, and cerebellum. Compared to bad imagers, good imagers showed higher activation in the right BA 4, left prefrontal cortex (BA 10), right thalamus, and bilateral cerebellum. Good imagers thus appear better able to recruit motor areas during MI, but also activate a prefrontal executive area (BA 10), which integrates information from the body and the environment and participates in higher-order gait control. These differences were found even though the two groups did not differ in other imagery abilities according to a standard questionnaire for vividness of motor and visual imagery. Future studies on MI should take into account these effects, and control for IA when comparing different populations, using appropriate measures. A better understanding of the neural mechanisms that underlie MI ability is crucial to accurately evaluate locomotor skills in clinical measures and neurorehabilitation techniques.
Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training.
Recent advances in neurofeedback based on real-time functional magnetic resonance imaging (fMRI) allow for learning to control spatially localized brain activity in the range of millimeters across the entire brain. Real-time fMRI neurofeedback studies have demonstrated the feasibility of self-regulating activation in specific areas that are involved in a variety of functions, such as perception, motor control, language, and emotional processing. In most of these previous studies, participants trained to control activity within one region of interest (ROI). In the present study, we extended the neurofeedback approach by now training healthy participants to control the interhemispheric balance between their left and right visual cortices. This was accomplished by providing feedback based on the difference in activity between a target visual ROI and the corresponding homologue region in the opposite hemisphere. Eight out of 14 participants learned to control the differential feedback signal over the course of 3 neurofeedback training sessions spread over 3 days, i.e., they produced consistent increases in the visual target ROI relative to the opposite visual cortex. Those who learned to control the differential feedback signal were subsequently also able to exert that control in the absence of neurofeedback. Such learning to voluntarily control the balance between cortical areas of the two hemispheres might offer promising rehabilitation approaches for neurological or psychiatric conditions associated with pathological asymmetries in brain activity patterns, such as hemispatial neglect, dyslexia, or mood disorders.
Negative emotions facilitate isometric force through activation of prefrontal cortex and periaqueductal gray.
Emotions are considered to modulate action readiness. Previous studies have demonstrated increased force production following exposure to emotionally arousing visual stimuli; however the neural mechanisms underlying how precise force output is controlled within varying emotional contexts remain poorly understood. To identify the neural correlates of emotion-modulated motor behaviour, twenty-two participants produced a submaximal isometric precision-grip contraction while viewing pleasant, unpleasant, neutral or blank images (without visual feedback of force output). Force magnitude was continuously recorded together with change in brain activity using functional magnetic resonance imaging. Viewing unpleasant images resulted in reduced force decay during force maintenance as compared with pleasant, neutral and blank images. Subjective valence and arousal ratings significantly predicted force production during maintenance. Neuroimaging revealed that negative valence and its interaction with force output correlated with increased activity in right inferior frontal gyrus (rIFG), while arousal was associated with amygdala and periaqueductal gray (PAG) activation. Force maintenance alone was correlated with cerebellar activity. These data demonstrate a valence-driven modulation of force output, mediated by a cortico-subcortical network involving rIFG and PAG. These findings are consistent with engagement of motor pathways associated with aversive motivation, eliciting defensive behaviour and action preparedness in response to negative emotional signals.
Tuned SQUID-MRI system with resonant frequency adjustment
We describe a low field, bench-top MRI system for small samples, based on a permanent magnet. Signals are received at 830 kHz using a tuned SQUID magnetometer, cooled in a modified liquid helium cryostat. The SQUID input circuit has an intrinsic Q -factor of 28,000, so this is damped by flux-locking electronics and an additional feedback loop to give an effective Q of between 40 and 200 for imaging. The resonant frequency is adjusted by a control rod coupled to a trimmer capacitor mounted in the liquid helium volume. Images of a test object acquired using the cooled receiver exhibit up to two-fold SNR gains in regions close to the sensor, compared to an equivalent room temperature coil, with the noise level dominated by losses coupled from the magnet pole faces. © 2007 IEEE.
First results of the CERN resonant weakly interacting sub-eV particle search (CROWS)
The CERN Resonant Weakly Interacting sub-eV Particle Search probes the existence of weakly interacting sub-eV particles like axions or hidden sector photons. It is based on the principle of an optical light shining through the wall experiment, adapted to microwaves. Critical aspects of the experiment are electromagnetic shielding, design and operation of low loss cavity resonators, and the detection of weak sinusoidal microwave signals. Lower bounds are set on the coupling constant g=4.5×10-8 GeV-1 for axionlike particles with a mass of ma=7.2 μeV. For hidden sector photons, lower bounds are set for the coupling constant χ=4.1×10-9 at a mass of mγ′=10.8 μeV. For the latter we are probing a previously unexplored region in the parameter space. © 2013 American Physical Society.
Maintenance of voluntary self-regulation learned through real-time fMRI neurofeedback
© 2017 Robineau, Meskaldji, Koush, Rieger, Mermoud, Morgenthaler, Van De Ville, Vuilleumier and Scharnowski. Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is an emerging technique that allows for learning voluntary control over brain activity. Such brain training has been shown to cause specific behavioral or cognitive enhancements, and even therapeutic effects in neurological and psychiatric patient populations. However, for clinical applications it is important to know if learned self-regulation can be maintained over longer periods of time and whether it transfers to situations without neurofeedback. Here, we present preliminary results from five healthy participants who successfully learned to control their visual cortex activity and who we re-scanned 6 and 14 months after the initial neurofeedback training to perform learned self-regulation. We found that participants achieved levels of self-regulation that were similar to those achieved at the end of the successful initial training, and this without further neurofeedback information. Our results demonstrate that learned self-regulation can be maintained over longer periods of time and causes lasting transfer effects. They thus support the notion that neurofeedback is a promising therapeutic approach whose effects can last far beyond the actual training period.
Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback.
Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity-neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top-down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self-regulated the top-down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity-based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity-based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders.
Fear Spreading Across Senses: Visual Emotional Events Alter Cortical Responses to Touch, Audition, and Vision.
Attention and perception are potentiated for emotionally significant stimuli, promoting efficient reactivity and survival. But does such enhancement extend to stimuli simultaneously presented across different sensory modalities? We used functional magnetic resonance imaging in humans to examine the effects of visual emotional signals on concomitant sensory inputs in auditory, somatosensory, and visual modalities. First, we identified sensory areas responsive to task-irrelevant tones, touches, or flickers, presented bilaterally while participants attended to either a neutral or a fearful face. Then, we measured whether these responses were modulated by the emotional content of the face. Sensory responses in primary cortices were enhanced for auditory and tactile stimuli when these appeared with fearful faces, compared with neutral, but striate cortex responses to the visual stimuli were reduced in the left hemisphere, plausibly as a consequence of sensory competition. Finally, conjunction and functional connectivity analyses identified 2 distinct networks presumably responsible for these emotional modulatory processes, involving cingulate, insular, and orbitofrontal cortices for the increased sensory responses, and ventrolateral prefrontal cortex for the decreased sensory responses. These results suggest that emotion tunes the excitability of sensory systems across multiple modalities simultaneously, allowing the individual to adaptively process incoming inputs in a potentially threatening environment.
Neuroscience: Intracranial Recordings of Value.
The role of orbitofrontal cortex in value-based choice is well-established from animal research, but there are challenges in relating neurophysiological recordings from animals to equivalent data from humans: a new study bridges this gap.
Impulsivity and predictive control are associated with suboptimal action-selection and action-value learning in regular gamblers.
Heightened impulsivity and cognitive biases are risk factors for gambling problems. However, little is known about precisely how these factors increase the risks of gambling-related harm in vulnerable individuals. Here, we modelled the behaviour of eighty-seven community-recruited regular, but not clinically problematic, gamblers during a binary-choice reinforcement-learning game, to characterise the relationships between impulsivity, cognitive biases, and the capacity to make optimal action selections and learn about action-values. Impulsive gamblers showed diminished use of an optimal (Bayesian-derived) probability estimate when selecting between candidate actions, and showed slower learning rates and enhanced non-linear probability weighting while learning action values. Critically, gamblers who believed that it is possible to predict winning outcomes (as 'predictive control') failed to use the game's reinforcement history to guide their action selections. Extensive evidence attests to the ease with which gamblers can erroneously perceive structure in the reinforcement history of games when there is none. Our findings demonstrate that the generic and specific risk factors of impulsivity and cognitive biases can interfere with the capacity of some gamblers to utilise structure when it is available in the reinforcement history of games, potentially increasing their risks of sustaining gambling-related harms.
Psychopathy-related traits and the use of reward and social information: a computational approach.
Psychopathy is often linked to disturbed reinforcement-guided adaptation of behavior in both clinical and non-clinical populations. Recent work suggests that these disturbances might be due to a deficit in actively using information to guide changes in behavior. However, how much information is actually used to guide behavior is difficult to observe directly. Therefore, we used a computational model to estimate the use of information during learning. Thirty-six female subjects were recruited based on their total scores on the Psychopathic Personality Inventory (PPI), a self-report psychopathy list, and performed a task involving simultaneous learning of reward-based and social information. A Bayesian reinforcement-learning model was used to parameterize the use of each source of information during learning. Subsequently, we used the subscales of the PPI to assess psychopathy-related traits, and the traits that were strongly related to the model's parameters were isolated through a formal variable selection procedure. Finally, we assessed how these covaried with model parameters. We succeeded in isolating key personality traits believed to be relevant for psychopathy that can be related to model-based descriptions of subject behavior. Use of reward-history information was negatively related to levels of trait anxiety and fearlessness, whereas use of social advice decreased as the perceived ability to manipulate others and lack of anxiety increased. These results corroborate previous findings suggesting that sub-optimal use of different types of information might be implicated in psychopathy. They also further highlight the importance of considering the potential of computational modeling to understand the role of latent variables, such as the weight people give to various sources of information during goal-directed behavior, when conducting research on psychopathy-related traits and in the field of forensic psychiatry.
Hierarchical competitions subserving multi-attribute choice.
Valuation is a key tenet of decision neuroscience, where it is generally assumed that different attributes of competing options are assimilated into unitary values. Such values are central to current neural models of choice. By contrast, psychological studies emphasize complex interactions between choice and valuation. Principles of neuronal selection also suggest that competitive inhibition may occur in early valuation stages, before option selection. We found that behavior in multi-attribute choice is best explained by a model involving competition at multiple levels of representation. This hierarchical model also explains neural signals in human brain regions previously linked to valuation, including striatum, parietal and prefrontal cortex, where activity represents within-attribute competition, competition between attributes and option selection. This multi-layered inhibition framework challenges the assumption that option values are computed before choice. Instead, our results suggest a canonical competition mechanism throughout all stages of a processing hierarchy, not simply at a final choice stage.
What are the neural origins of choice variability?
Two recent studies examine neural activity predictive of upcoming choices during value-guided choice. Their results may be cast in light of a competitive winner-take-all decision network. This viewpoint places certain decision variables not as features of the environment to be encoded, but as emergent properties of network activity.
The computation of social behavior.
Neuroscientists are beginning to advance explanations of social behavior in terms of underlying brain mechanisms. Two distinct networks of brain regions have come to the fore. The first involves brain regions that are concerned with learning about reward and reinforcement. These same reward-related brain areas also mediate preferences that are social in nature even when no direct reward is expected. The second network focuses on regions active when a person must make estimates of another person's intentions. However, it has been difficult to determine the precise roles of individual brain regions within these networks or how activities in the two networks relate to one another. Some recent studies of reward-guided behavior have described brain activity in terms of formal mathematical models; these models can be extended to describe mechanisms that underlie complex social exchange. Such a mathematical formalism defines explicit mechanistic hypotheses about internal computations underlying regional brain activity, provides a framework in which to relate different types of activity and understand their contributions to behavior, and prescribes strategies for performing experiments under strong control.
A distributed, hierarchical and recurrent framework for reward-based choice.
Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this Opinion article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organization of timescales for information processing across the cortex. This account also suggests that certain correlates of value are emergent rather than represented explicitly in the brain.