Sharing others' emotional states may facilitate understanding their intentions and actions. Here we show that networks of brain areas "tick together" in participants who are viewing similar emotional events in a movie. Participants' brain activity was measured with functional MRI while they watched movies depicting unpleasant, neutral, and pleasant emotions. After scanning, participants watched the movies again and continuously rated their experience of pleasantness-unpleasantness (i.e., valence) and of arousal-calmness. Pearson's correlation coefficient was used to derive multisubject voxelwise similarity measures [intersubject correlations (ISCs)] of functional MRI data. Valence and arousal time series were used to predict the moment-to-moment ISCs computed using a 17-s moving average. During movie viewing, participants' brain activity was synchronized in lower-and higher-order sensory areas and in corticolimbic emotion circuits. Negative valence was associated with increased ISC in the emotion-processing network (thalamus, ventral striatum, insula) and in the default-mode network (precuneus, temporoparietal junction, medial prefrontal cortex, posterior superior temporal sulcus). High arousal was associated with increased ISC in the somatosensory cortices and visual and dorsal attention networks comprising the visual cortex, bilateral intraparietal sulci, and frontal eye fields. Seed-voxel-based correlation analysis confirmed that these sets of regions constitute dissociable, functional networks. We propose that negative valence synchronizes individuals' brain areas supporting emotional sensations and understanding of another's actions, whereas high arousal directs individuals' attention to similar features of the environment. By enhancing the synchrony of brain activity across individuals, emotions may promote social interaction and facilitate interpersonal understanding.synchronization | feeling | empathy | somatosensation H uman emotions are highly contagious. Feelings of anger and hatred may spread rapidly throughout a peaceful protest demonstration and turn it into a violent riot, whereas intense feelings of excitement and joy can sweep promptly from players to spectators in an ever-so-important football final. It is well documented that observation of others in a particular emotional state rapidly and automatically triggers the corresponding behavioral and physiological representation of that emotional state in the observer (1-3). Neuroimaging studies also have revealed common neural activation for perception and experience of states such as pain (4-6), disgust (7), and pleasure (8). This automated mapping of others' emotional states in one's own body and brain has been proposed to support social interaction via contextual understanding: Sharing others' emotional states provides the observers with a somatosensory framework that facilitates understanding their intentions and actions and allows the observers to "tune in" or "sync" with other individuals (9-11).Recent evidence suggests that during social si...

Emotion plays a significant role in goal-directed behavior, yet its neural basis is yet poorly understood. In several psychological models the cardinal dimensions that characterize the emotion space are considered to be valence and arousal. Here 3T functional magnetic resonance imaging (fMRI) was used to reveal brain areas that show valence- and arousal-dependent blood oxygen level dependent (BOLD) signal responses. Seventeen healthy adults viewed pictures from the International Affective Picture System (IAPS) for brief 100 ms periods in a block design paradigm. In many brain regions BOLD signals correlated significantly positively with valence ratings of unpleasant pictures. Interestingly, partly in the same regions but also in several other regions BOLD signals correlated negatively with valence ratings of pleasant pictures. Therefore, there were several areas where the correlation across all pictures was of inverted U-shape. Such correlations were found bilaterally in the dorsolateral prefrontal cortex (DLPFC), dorsomedial prefrontal cortex (DMPFC) extending to anterior cingulate cortex (ACC), and insula. Self-rated arousal of those pictures which were evaluated to be unpleasant correlated with BOLD signal in the ACC, whereas for pleasant pictures arousal correlated positively with the BOLD signal strength in the right substantia innominata. We interpret our results to suggest a major division of brain mechanisms underlying affective behavior to those evaluating stimuli to be pleasant or unpleasant. This is consistent with the basic division of behavior to approach and withdrawal, where differentiation of hostile and hospitable stimuli is crucial.

Perceived emotional valence of sensory stimuli influences their processing in various cortical and subcortical structures. Recent evidence suggests that negative and positive valences are processed separately, not along a single linear continuum. Here, we examined how brain is activated when subjects are listening to auditory stimuli varying parametrically in perceived valence (very unpleasant-neutral-very pleasant). Seventeen healthy volunteers were scanned in 3 Tesla while listening to International Affective Digital Sounds (IADS-2) in a block design paradigm. We found a strong quadratic U-shaped relationship between valence and blood oxygen level dependent (BOLD) signal strength in the medial prefrontal cortex, auditory cortex, and amygdala. Signals were the weakest for neutral stimuli and increased progressively for more unpleasant or pleasant stimuli. The results strengthen the view that valence is a crucial factor in neural processing of emotions. An alternative explanation is salience, which increases with both negative and positive valences.

The aim of this work was to study the similarity network and hierarchical clustering of Finnish emotion concepts. Native speakers of Finnish evaluated similarity between the 50 most frequently used Finnish words describing emotional experiences. We hypothesized that methods developed within network theory, such as identifying clusters and specific local network structures, can reveal structures that would be difficult to discover using traditional methods such as multidimensional scaling (MDS) and ordinary cluster analysis. The concepts divided into three main clusters, which can be described as negative, positive, and surprise. Negative and positive clusters divided further into meaningful sub-clusters, corresponding to those found in previous studies. Importantly, this method allowed the same concept to be a member in more than one cluster. Our results suggest that studying particular network structures that do not fit into a low-dimensional description can shed additional light on why subjects evaluate certain concepts as similar. To encourage the use of network methods in analyzing similarity data, we provide the analysis software for free use (http://www.becs.tkk.fi/similaritynets/).