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SFN 2016 Abstract Submitted

Investigating the influence of priors in human fear generalization using a Bayesian model

Kampermann L, Büchel C & Onat S

When organisms form associations through learning, responses often generalize to stimuli that bare resemblance to the initially reinforced stimulus (CS+). During generalization, shifts in maximal responses away from the actual reinforced stimulus are commonly observed when tested with multiple stimuli along a similarity continuum, a phenomenon known as peak shift. We have developed a Bayesian framework that can account for peak shifts observed in behavioral ratings by postulating a group-level prior that is common to all participants over the range of stimuli forming the similarity continuum. The interaction of the prior distribution with a generalization component centered on the CS+ can act as a “magnet”, resulting in shifts of maximal responses depending on which stimuli was used as CS+. This formalism allows us to reverse-engineer subjects’ latent prior distributions by evaluating to what extent a specific hypothesis on priors can explain the observed shifts.

We employed a fear conditioning procedure using 8 faces organized along a circular similarity continuum varying in gender and identity dimensions. Two opposite faces were randomly selected as the CS+ and CS– for each participant (n = 141). Following aversive conditioning, we obtained fear generalization gradients by asking participants for explicit UCS expectancy ratings, thus obtaining a two-sided generalization gradient ranging from CS+ to CS–. We compared the performance of the Bayesian model to fits carried out on a single-subject basis using two versions of Gaussian functions and subsequently tested different hypotheses on priors for the Bayesian model.

As expected a flexible Gaussian model with 2 parameters (width and location parameters) fitted the data significantly better than a simple Gaussian model (only width parameter) centered on the CS+ face (r = .91 vs. r = .74) underlining the presence of peak shifts in behavioral ratings. On the other hand, the Bayesian model performed significantly better than the simple Gaussian model, despite being based on only two more free parameters (flexible Gaussian: 2n parameters; simple Gaussian: n parameters; Bayesian model: n+2 parameters). Furthermore the Bayesian model explained behavioral gradients best, when a bimodal prior distribution peaking at both gender prototypes was used (r = .84). Testing different gender categories individually, a unimodal prior centered on the male category explained as much variance as one centered on female category. Overall, the predominance of a bimodal prior indicates that peak shifts can result from “magnet” effects of categorical face representatives instead of adversity attributions to a specific gender.

Poster Presented in ABIM

OHBM Hamburg 2014 Abstract: Precision of Neuronal Representations during Fear Generalization

Precision of Neuronal Representations during Fear Generalization

Onat S., Büchel C.

Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany

Introduction

Fear generalization is usually conceived as resulting from a lack of precision in the neuronal representations about the aversive stimulus. Therefore the lack of certainty is believed to be the source of generalization that is observed on the behavioral level. Alternatively, fear generalization might be the result of an active neuronal process, whereby the nervous system tries to optimally control behavior based on what is already known. This scenario predicts that hyper-precise neuronal representations about the aversive stimulus would co-exist with a broader behavioral tuning. Using an event-related fMRI paradigm, we analyzed the precision of neuronal signals in different brain regions and compared these with different behavioral measurements.

Methods

We created 8 computer generated faces that were organized along a circular similarity gradient (Fig. 1, dashed line). A maximum-likelihood based multi-dimensional scaling method (Maloney et al., 2003) was used to confirm the circularity of the perceptual organization of these stimuli (Fig. 1, solid line). This gradual change in stimulus similarity was translated to an aversiveness gradient using a classical Pavlovian conditioning paradigm. To this end, one randomly selected face (CS+) was partially associated with an aversive electric shock. The most dissimilar face was kept as neutral (CS-). BOLD responses were recorded before and after the conditioning phase together with changes in skin conductance, as well as aversiveness ratings (n = 29).

Results

We identified a set of neuronal clusters that were significantly modulated as a function of increasing dissimilarity to the CS+ face. The average amplitude of evoked responses by the CS+, CS- and all intermediate faces is shown in Fig. 2 (mean ± SEM, red for CS+, cyan for CS-) for two clusters located in hippocampus and insula. The effect of conditioning is clearly seen as a modulation of responses centered on the CS+ face following the conditioning (bottom panels). These responses were fit with a Gaussian function, yielding parameterized fear-tuning profiles (Fig. 2, black curves), where alpha (𝛼) and sigma (𝜎) parameters characterized the strength and the width of the tuning profiles, respectively (Fig. 2).

Almost all clusters within this identified fear generalization network including a set of prefrontal, cingular, hippocampal, and face selective sensory sites exhibited a strong deactivation in response to CS+ face (Fig. 3, left panel). The right insula was the sole exception to this pattern (p < 0.001), showing a fear-tuning profile that was characterized by a net activation (Fig. 3, left panel, top bar). Among all the clusters investigated, the insula showed the sharpest fear tuning (Fig. 3, right panel, bottom bar). We next compared, the precision of insular aversive tuning to the fear tuning of skin conductance and aversiveness ratings. The width of insular tuning was even sharper than the tuning of any behavioral measure i.e. ratings (t(28)= -2.67, p = 0.0062) and skin conductance (t(28)= -2.23, p = 0.017) responses (Fig. 4).

Conclusion

These results show that the representation of the aversive stimulus is present in a hyper-precise manner within the neuronal networks responsible for fear-generalization. The imprecision of the tuning that is observed in other neuronal sites and at the behavior level seems to be mediated by a mechanism that actively “blurs” the source of the aversive event, rather than resulting from a lack of precision in the neuronal representations. Our results therefore suggest that a controlled imprecision rather than an imprecision in the control, is responsible for the generalization of fear in the healthy humans.

FENS Poster: Increased influence of low-level stimulus features on fixations in neglect patients

SFN 2013: Investigation of Fear Generalization with fMRI Using Multivariate Pattern Similarity Analysis

Investigation of Fear Generalization with fMRI Using Multivariate Pattern Similarity Analysis

Onat S., Büchel C.

Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf

Generalization of aversive events to previously neutral events relates to problems in anxiety regulation. This is generally investigated by using simplified stimuli that are parametrically modified to span a similarity space with respect to the aversive event and measuring the tuning width of autonomic responses. However on the neuronal level our knowledge about which brain regions underpin this generalization is scarce. Multivariate pattern analysis of BOLD responses offer a valuable tool to investigate brain regions which are responsible in controlling the fear generalization.

We investigated fear generalization using faces as ecologically relevant stimuli using an event-related fMRI design using healthy humans. These stimuli were organized along a circular similarity continuum and two opposing faces were randomly selected as the aversive and neutral face for each participant. Autonomic measures of anxiety such as skin conductance and pupil size showed a gradual decrease in amplitude with increasing distance to the aversive face. These fear tuning profiles were well-captured with a circular Gaussian function that showed a large natural variability in the width of the generalization profiles across participants.

Using multi-variate similarity analysis at the single subject level, we evaluated which brain regions exhibited a similarity gradient with respect to the neuronal activity patterns evoked by the aversive face. In cingulate cortex, putamen, insular cortex and frontal regions, including medial frontal and orbito-frontal cortex, the similarity of neuronal activity patterns decayed with increasing distance from the aversive face and reached lowest values for the neutral face.

Our current results extend our current knowledge about fear generalization and most importantly provide new tools to investigate neuronal sites that are responsible for anxiety regulation.