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Vilaclara, L.

Publications and source records attributed to Vilaclara, L..

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Individual differences of cortical and subcortical emotion-informed functional gradients

The complex interplay between brain regions that support emotional experience and their link to individual differences is a topic of active research. Additionally, there has been growing interest in using functional gradients to investigate human cortical organization during both rest and film fMRI. Among these, several studies demonstrated improved brain fingerprinting performance, reflecting greater neural identification capability of film fMRI against rest fMRI despite higher subject synchronization during film-watching than in rest. Comparably, in this work we study the relation between individual differences, in particular, state anxiety and openness scores, and brain activity during the processing of various emotional scenes in films, through functional gradients. Next to including subcortical areas, we also propose a new approach of computing functional gradients based on a subset of frames selected using emotional annotation data of films, resulting in emotion-informed functional gradients. Then we evaluate the variance in emotion-informed gradients across subjects and employ these same gradients in the prediction of individual differences. For emotion-informed functional gradients, the highest predictability of state anxiety was found for scenes of negative valence and medium-high arousal, corresponding to the typical location of anxiety within the valence-arousal-power emotional space. Additionally, predictability of state anxiety was negatively correlated to inter-subject variability. In contrast, predictability of openness was found to be highest during scenes with low arousal and positively correlated to inter-subject variability. In essence, our results first show that macroscale brain organization is affected by emotional experience, and that frame selection based on the latter can be useful to remove non-subject-specific variability while extracting subject-specific information related to the emotion experience. It also demonstrates that frame selection increases inter-subject variability allowing the extraction of more subject-specific information. Thus, expanding on the idea of brain fingerprint in film fMRI, we argue that emotional experiences enhance disentanglement of various domain of individual differences. Moreover, depending on the individual difference of interest, fMRI acquired during more or less constrained paradigms would be more suitable to reveal different properties of brain function.

neuroscience↗

Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli

The extensive Emo-FilM dataset stands for Emotion research using Films and fMRI in healthy participants. This dataset includes detailed emotion annotations by 44 raters for 14 short films with a combined duration of over 2[1/2] hours, as well as recordings of respiration, heart rate, and functional magnetic resonance imaging (fMRI) from a different sample of 30 individuals watching the same films. The detailed annotations of experienced emotion evaluated 50 items including ratings of discrete emotions and emotion components from the domains of appraisal, motivation, motor expression, physiological response, and feeling. Quality assessment for the behavioural data shows a mean inter-rater agreement of 0.38. The parallel fMRI data was acquired at 3 Tesla in four sessions, accompanied with a high-resolution structural (T1) and resting state fMRI scans for each participant. Physiological recordings during fMRI included heart rate, respiration, and electrodermal activity (EDA). Quality assessment indicators confirm acceptable quality of the MRI data. This dataset is designed, but not limited, to studying the dynamic neural processes involved in emotion experience. A particular strength of this data is the high temporal resolution of behavioural annotations, as well as the inclusion of a validation study in the fMRI sample. This high-quality behavioural data in combination with continuous physiological and MRI measurements makes this dataset a treasure trove for researching human emotion in response to naturalistic stimulation in a multimodal framework.

neuroscience↗