Philip A. Kragel

Neurobehavioral mechanisms of human fear generalization.

While much research has elucidated the neurobiology of fear learning, the neural systems supporting the generalization of learned fear are unknown. Using functional magnetic resonance imaging (fMRI), we show that regions involved in the acquisition of fear support the generalization of fear to stimuli that are similar to a learned threat, but vary in fear intensity value. Behaviorally, subjects retrospectively misidentified a learned threat as a more intense stimulus and expressed greater skin conductance responses (SCR) to generalized stimuli of high intensity. Brain activity related to intensity-based fear generalization was observed in the striatum, insula, thalamus/periacqueductal gray, and subgenual cingulate cortex. The psychophysiological expression of generalized fear correlated with amygdala activity, and connectivity between the amygdala and extrastriate visual cortex was correlated with individual differences in trait anxiety. These findings reveal the brain regions and functional networks involved in flexibly responding to stimuli that resemble a learned threat. These regions may comprise an intensity-based fear generalization circuit that underlies retrospective biases in threat value estimation and overgeneralization of fear in anxiety disorders.

Dynamic neural networks supporting memory retrieval

How do separate neural networks interact to support complex cognitive processes such as remembrance of the personal past? Autobiographical memory (AM) retrieval recruits a consistent pattern of activation that potentially comprises multiple neural networks. However, it is unclear how such large-scale neural networks interact and are modulated by properties of the memory retrieval process. In the present functional MRI (fMRI) study, we combined independent component analysis (ICA) and dynamic causal modeling (DCM) to understand the neural networks supporting AM retrieval. ICA revealed four task-related components consistent with the previous literature: 1) medial prefrontal cortex (PFC) network, associated with self-referential processes, 2) medial temporal lobe (MTL) network, associated with memory, 3) frontoparietal network, associated with strategic search, and 4) cingulooperculum network, associated with goal maintenance. DCM analysis revealed that the medial PFC network drove activation within the system, consistent with the importance of this network to AM retrieval. Additionally, memory accessibility and recollection uniquely altered connectivity between these neural networks. Recollection modulated the influence of the medial PFC on the MTL network during elaboration, suggesting that greater connectivity among subsystems of the default network supports greater re-experience. In contrast, memory accessibility modulated the influence of frontoparietal and MTL networks on the medial PFC network, suggesting that ease of retrieval involves greater fluency among the multiple networks contributing to AM. These results show the integration between neural networks supporting AM retrieval and the modulation of network connectivity by behavior.

Regional Brain Differences in the Effect of Distraction During the Delay Interval of a Working Memory Task

Working memory (WM) comprises operations whose coordinated action contributes to our ability to maintain focus on goal-relevant information in the presence of distraction. The present study investigated the nature of distraction upon the neural correlates of WM maintenance operations by presenting task-irrelevant distracters during the interval between the memoranda and probes of a delayed-response WM task. The study used a region of interest (ROIs) approach to investigate the role of anterior (e.g., lateral and medial prefrontal cortex--PFC) and posterior (e.g., parietal and fusiform cortices) brain regions that have been previously associated with WM operations. Behavioral results showed that distracters that were confusable with the memorandum impaired WM performance, compared to either the presence of non-confusable distracters or to the absence of distracters. These different levels of distraction led to differences in the regional patterns of delay interval activity measured with event-related functional magnetic resonance imaging (fMRI). In the anterior ROIs, dorsolateral PFC activation was associated with WM encoding and maintenance, and in maintaining a preparatory state, and ventrolateral PFC activation was associated with the inhibition of distraction. In the posterior ROIs, activation of the posterior parietal and fusiform cortices was associated with WM and perceptual processing, respectively. These findings provide novel evidence concerning the neural systems mediating the cognitive and behavioral responses during distraction, and places frontal cortex at the top of the hierarchy of the neural systems responsible for cognitive control.

Aversive Learning Modulates Cortical Representations of Object Categories

Experimental studies of conditioned learning reveal activity changes in the amygdala and unimodal sensory cortex underlying fear acquisition to simple stimuli. However, real-world fears typically involve complex stimuli represented at the category level. A consequence of category-level representations of threat is that aversive experiences with particular category members may lead one to infer that related exemplars likewise pose a threat, despite variations in physical form. Here, we examined the effect of category-level representations of threat on human brain activation using 2 superordinate categories (animals and tools) as conditioned stimuli. Hemodynamic activity in the amygdala and category-selective cortex was modulated by the reinforcement contingency, leading to widespread fear of different exemplars from the reinforced category. Multivariate representational similarity analyses revealed that activity patterns in the amygdala and object-selective cortex were more similar among exemplars from the threat versus safe category. Learning to fear animate objects was additionally characterized by enhanced functional coupling between the amygdala and fusiform gyrus. Finally, hippocampal activity co-varied with object typicality and amygdala activation early during training. These findings provide novel evidence that aversive learning can modulate category-level representations of object concepts, thereby enabling individuals to express fear to a range of related stimuli.

Multivariate neural biomarkers of emotional states are categorically distinct

Understanding how emotions are represented neurally is a central aim of affective neuroscience. Despite decades of neuroimaging efforts addressing this question, it remains unclear whether emotions are represented as distinct entities, as predicted by categorical theories, or are constructed from a smaller set of underlying factors, as predicted by dimensional accounts. Here, we capitalize on multivariate statistical approaches and computational modeling to directly evaluate these theoretical perspectives. We elicited discrete emotional states using music and films during functional magnetic resonance imaging scanning. Distinct patterns of neural activation predicted the emotion category of stimuli and tracked subjective experience. Bayesian model comparison revealed that combining dimensional and categorical models of emotion best characterized the information content of activation patterns. Surprisingly, categorical and dimensional aspects of emotion experience captured unique and opposing sources of neural information. These results indicate that diverse emotional states are poorly differentiated by simple models of valence and arousal, and that activity within separable neural systems can be mapped to unique emotion categories.

Multivariate Pattern Classification Reveals Autonomic and Experiential Representations of Discrete Emotions

Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression.

Medial prefrontal pathways for the contextual regulation of extinguished fear in humans

The maintenance of anxiety disorders is thought to depend, in part, on deficits in extinction memory, possibly due to reduced contextual control of extinction that leads to fear renewal. Animal studies suggest that the neural circuitry responsible fear renewal includes the hippocampus, amygdala, and dorsomedial (dmPFC) and ventromedial (vmPFC) prefrontal cortex. However, the neural mechanisms of context-dependent fear renewal in humans remain poorly understood. We used functional magnetic resonance imaging (fMRI), combined with psychophysiology and immersive virtual reality, to elucidate how the hippocampus, amygdala, and dmPFC and vmPFC interact to drive the context-dependent renewal of extinguished fear. Healthy human participants encountered dynamic fear-relevant conditioned stimuli (CSs) while navigating through 3-D virtual reality environments in the MRI scanner. Conditioning and extinction were performed in two different virtual contexts. Twenty-four hours later, participants were exposed to the CSs without reinforcement while navigating through both contexts in the MRI scanner. Participants showed enhanced skin conductance responses (SCRs) to the previously-reinforced CS+ in the acquisition context on Day 2, consistent with fear renewal, and sustained responses in the dmPFC. In contrast, participants showed low SCRs to the CSs in the extinction context on Day 2, consistent with extinction recall, and enhanced vmPFC activation to the non-reinforced CS-. Structural equation modeling revealed that the dmPFC fully mediated the effect of the hippocampus on right amygdala activity during fear renewal, whereas the vmPFC partially mediated the effect of the hippocampus on right amygdala activity during extinction recall. These results indicate dissociable contextual influences of the hippocampus on prefrontal pathways, which, in turn, determine the level of reactivation of fear associations.

Development and validation of an unsupervised scoring system (Autonomate) for skin conductance response analysis

The skin conductance response (SCR) is increasingly being used as a measure of sympathetic activation concurrent with neuroscience measurements. We present a method of automated analysis of SCR data in the contexts of event-related cognitive tasks and nonspecific responding to complex stimuli. The primary goal of the method is to accurately measure the classical trough-to-peak amplitude of SCR in a fashion closely matching manual scoring. To validate the effectiveness of the method in event-related paradigms, three archived datasets were analyzed by two manual raters, the fully-automated method (Autonomate), and three alternative software packages. Further, the ability of the method to score non-specific responses to complex stimuli was validated against manual scoring. Results indicate high concordance between fully-automated and computer-assisted manual scoring methods. Given that manual scoring is error prone, subject to bias, and time consuming, the automated method may increase the efficiency and accuracy of SCR data analysis.

Decoding Spontaneous Emotional States in the Human Brain

Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems.

Neural networks supporting autobiographical memory retrieval in posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) affects the functional recruitment and connectivity between neural regions during autobiographical memory (AM) retrieval that overlap with default and control networks. Whether such univariate changes relate to potential differences in the contributions of the large-scale neural networks supporting cognition in PTSD is unknown. In the present functional MRI study, we employed independent-component analysis to examine the influence of the engagement of neural networks during the recall of personal memories in a PTSD group (15 participants) as compared to non-trauma-exposed healthy controls (14 participants). We found that the PTSD group recruited similar neural networks when compared to the controls during AM recall, including default-network subsystems and control networks, but group differences emerged in the spatial and temporal characteristics of these networks. First, we found spatial differences in the contributions of the anterior and posterior midline across the networks, and of the amygdala in particular, for the medial temporal subsystem of the default network. Second, we found temporal differences within the medial prefrontal subsystem of the default network, with less temporal coupling of this network during AM retrieval in PTSD relative to controls. These findings suggest that the spatial and temporal characteristics of the default and control networks potentially differ in a PTSD group versus healthy controls and contribute to altered recall of personal memory.