Biology subjects

Graner, J. L.

Publications and source records attributed to Graner, J. L..

3 recordsLinked to original sources

bioRxiv2026

Decoding of arousal and valence from fMRI data obtained during emotion inductions

Arousal and valence are fundamental dimensions of affective experience signifying levels of activation and pleasantness, respectively. These dimensions play a crucial role in shaping emotional responses and behaviors, with significant implications for psychopathology. Previous machine learning studies had some success decoding these states from brain activation patterns observed during task-based functional magnetic resonance imaging (fMRI), but the results have varied across studies. Moreover, prior studies have often been limited by small sample sizes, weak decoding performance, and non-whole-brain analyses, leaving the neural representations of arousal and valence largely unresolved. Here we successfully decoded arousal and valence from whole-brain task-fMRI data collected from 132 participants during exposure to 300 unique emotional stimuli, including 150 movie clips and 150 text scenarios that reliably induced a wide range of arousal and valence states. Mass univariate general linear models identified block-level activation (emotion stimuli > washout) from all gray matter voxels. Multivariate regression analysis predicted arousal and valence ratings based on these gray matter activations. Patterns in the fMRI data underlying arousal and valence were robust, as they were successfully decoded across both induction modalities using five different linear multivariate regression models. Although significant, decoding from scenarios was less successful than from movies, likely due to their more imaginative nature. In particular, decoding arousal from scenarios only showed low predictive utility. Representations of arousal and valence were widespread throughout the brain, and we reveal cerebellar and brainstem contributions that have largely been absent in past fMRI decoding studies. These findings clarify the distributed neural basis of arousal and valence and provide a foundation for future clinical research on the role of these constructs in affective dysregulation.

neuroscience↗

bioRxiv2025

Representational Similarity and Pattern Classification of Fifteen Emotional States Induced by Movie Clips and Text Scenarios

How do different emotional states relate to each other and how are they represented in the human brain? These are important questions in the field of affective neuroscience, with profound scientific and vast clinical implications. Different theories of emotion tend to emphasize the relative importance of distinct psychological constructs, for example categorical labels (e.g., fear, joy, or sadness) versus dimensional ratings (e.g., valence and arousal), for understanding human emotions. To investigate whether categorical or dimensional constructs correspond better to patterns of brain activity associated with human affective experiences, we experimentally induced 15 emotions (spanning positive, negative, and neutral valence) in 136 participants using 150 short movie clips and 150 one- or two-sentence text scenarios, while their blood oxygenation-level dependent activity was recorded in a magnetic resonance imaging scanner. Results from our representational similarity analyses suggest participants brain activity significantly correlated with their categorical labeling of, but not their dimensional rating of, the movie clips and text scenarios. Subsequently, we were also able to decode the categorical labels of these emotional stimuli using whole-brain multi-voxel pattern classification, with important voxels found in many cortical, limbic, subcortical, cerebellar, and brainstem regions. Finally, we found similar clusters of emotions through exploratory hierarchical clustering analyses of participants categorical labeling of and brain responses to these stimuli. Taken together, these findings greatly advance our understanding of how a large set of human emotions are related to each other both in terms of the participants self-report and their brain activity. Significance StatementWe successfully decoded fifteen emotional states induced by movie clips and text scenarios based on participants brain responses (blood oxygenation-level dependent signals) to these stimuli using multi-voxel pattern classification (a supervised machine learning approach). We also found that participants brain responses correlated with their self-reported emotional experience, i.e. which emotion they felt while they were presented with these stimuli, using representative similarity analysis (an unsupervised approach). Finally, we found that these emotions are organized in very similar ways both in terms of participants categorical labeling and their brain responses to these stimuli. Together, these data-driven and computational modeling-based findings greatly advance our understanding of how a large set of emotions are organized and represented in the human brain.

neuroscience↗

bioRxiv2025

The elusive neural signature of emotion regulation capabilities: evidence from a large-scale consortium

Cognitive reappraisal is a fundamental emotion regulation strategy for mental and physical well-being, but how its neural mechanisms relate to individual differences remains poorly understood. In a consortium effort analyzing 40 fMRI datasets (N=2,175), we examined the relationship between neural activation during reappraisal tasks and three core individual difference indices of reappraisal capabilities: (1) trait questionnaires, (2) task-based affective ratings, and (3) amygdala down-regulation. Strikingly, there was no shared overlap across these three common indices. Only a very weak correlation emerged between amygdala down-regulation and task-based affective ratings. Whole-brain analyses revealed no reliable neural associations with trait questionnaires, and associations with task-based affective ratings fell outside canonical emotion regulation networks (e.g., prefrontal circuitry). Moreover, amygdala down-regulation, often interpreted as a stable individual marker, was confounded by person-specific whole-brain responses -- a limitation extending to fMRI research beyond the emotion regulation domain. These findings challenge the assumption that an individuals prefrontal activity is a valid indicator of their reappraisal capabilities and suggest that common trait, behavioral, and neural measures might capture distinct facets of emotion regulation. More broadly, our results highlight concrete methodological challenges for fMRI research on individual differences, with implications extending beyond emotion regulation to the neuroscience of personality, psychopathology, and general well-being.

neuroscience↗