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Whether subtle differences in the emotional context during threat perception can be detected by multi-voxel pattern analysis (MVPA) remains a topic of debate. To investigate this question, we compared the ability of pattern recognition analysis to discriminate between patterns of brain activity to a threatening versus a physically paired neutral stimulus in two different emotional contexts (the stimulus being directed towards or away from the viewer). The directionality of the stimuli is known to be an important factor in activating different defensive responses. Using multiple kernel learning (MKL) classification models, we accurately discriminated patterns of brain activation to threat versus neutral stimuli in the directed towards context but not during the directed away context. Furthermore, we investigated whether it was possible to decode an individual's subjective threat perception from patterns of whole-brain activity to threatening stimuli in the different emotional contexts using MKL regression models. Interestingly, we were able to accurately predict the subjective threat perception index from the pattern of brain activation to threat only during the directed away context. These results show that subtle differences in the emotional context during threat perception can be detected by MVPA. In the directed towards context, the threat perception was more intense, potentially producing more homogeneous patterns of brain activation across individuals. In the directed away context, the threat perception was relatively less intense and more variable across individuals, enabling the regression model to successfully capture the individual differences and predict the subjective threat perception.

Introduction: Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample., Methods: fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli., Results: The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions)., Conclusion: These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Ayahuasca is a beverage used in Amazonian traditional medicine and it has been part of the human experience for millennia as well as other different psychoactive plants. Although Ayahuasca has been proposed as potentially therapeutic as an anxiolytic and antidepressant, whilst no studies have been carried out so far investigating their direct effect on brain emotional processing. This study aimed to measure the emotional acute effect of Ayahuasca on brain response to implicit aversive stimulation using a face recognition task in functional magnetic resonance imaging (fMRI). Nineteen male experienced Ayahuasca users participated in this study in two fMRI sessions before and after 50 min of the Ayahuasca ingestion. Subjects were presented with pictures of neutral (A) and aversive (B) (fearful or disgusted) faces from the Pictures of Facial Affect Series. Subjects were instructed to identify the gender of the faces (gender discrimination task) while the emotional content was implicit. Subjective mood states were also evaluated before Ayahuasca intake and after the second fMRI session, using a visual analogue mood scale (VAMS). During the aversive stimuli, the activity in the bilateral amygdala was attenuated by Ayahuasca (qFDR<0.05). Furthermore, in an exploratory analysis of the effects after intake, Ayahuasca enhances the activation in the insular cortex bilaterally, as well as in the right dorsolateral prefrontal cortex (qFDR<0.05). In the psychometric VAMS scale, subjects reported attenuation of both anxiety and mental sedation (p < 0.01) during acute effects. Together, all reported results including neuroimaging, behavioral data and psychometric self-report suggest that Ayahuasca can promote an emotion regulation mechanism in response to aversive stimuli with corresponding improved cognition including reduced anxiety and mental sedation. [Display omitted] • Ayahuasca is a psychedelic of Amazonian origin with cerebral serotonergic action. • Ayahuasca use by experienced users reveal brain and behavioral changes. • Ayahuasca acts on emotional regulation that could explain its therapeutic potential. [ABSTRACT FROM AUTHOR]