scholarly journals Hierarchical Brain Network for Face and Voice Integration of Emotion Expression

2017 ◽  
Author(s):  
Jodie Davies-Thompson ◽  
Giulia V. Elli ◽  
Mohamed Rezk ◽  
Stefania Benetti ◽  
Markus van Ackeren ◽  
...  
Keyword(s):  
Emotional Expression ◽  
Brain Network ◽  
Causal Modeling ◽  
Emotional Expressions ◽  
Face Area ◽  
Sensory Modalities ◽  
The Face ◽  
The Right ◽  
The Brain ◽  
Posterior Superior Temporal Sulcus

ABSTRACTThe brain has separate specialized computational units to process faces and voices located in occipital and temporal cortices. However, humans seamlessly integrate signals from the faces and voices of others for optimal social interaction. How are emotional expressions, when delivered by different sensory modalities (faces and voices), integrated in the brain? In this study, we characterized the brains’ response to faces, voices, and combined face-voice information (congruent, incongruent), which varied in expression (neutral, fearful). Using a whole-brain approach, we found that only the right posterior superior temporal sulcus (rpSTS) responded more to bimodal stimuli than to face or voice alone but only when the stimuli contained emotional expression. Face-and voice-selective regions of interest extracted from independent functional localizers, similarly revealed multisensory integration in the face-selective rpSTS only; further, this was the only face-selective region that also responded significantly to voices. Dynamic Causal Modeling revealed that the rpSTS receives unidirectional information from the face-selective fusiform face area (FFA), and voice-selective temporal voice area (TVA), with emotional expression affecting the connection strength. Our study promotes a hierarchical model of face and voice integration, with convergence in the rpSTS, and that such integration depends on the (emotional) salience of the stimuli.

scholarly journals Hierarchical Brain Network for Face and Voice Integration of Emotion Expression

2018 ◽  
Vol 29 (9) ◽  
pp. 3590-3605 ◽  
Author(s):  
Jodie Davies-Thompson ◽  
Giulia V Elli ◽  
Mohamed Rezk ◽  
Stefania Benetti ◽  
Markus van Ackeren ◽  
...  
Keyword(s):  
Emotional Expression ◽  
Brain Network ◽  
Causal Modeling ◽  
Emotional Expressions ◽  
Face Area ◽  
Sensory Modalities ◽  
The Face ◽  
The Right ◽  
The Brain ◽  
Posterior Superior Temporal Sulcus

Abstract The brain has separate specialized computational units to process faces and voices located in occipital and temporal cortices. However, humans seamlessly integrate signals from the faces and voices of others for optimal social interaction. How are emotional expressions, when delivered by different sensory modalities (faces and voices), integrated in the brain? In this study, we characterized the brains’ response to faces, voices, and combined face–voice information (congruent, incongruent), which varied in expression (neutral, fearful). Using a whole-brain approach, we found that only the right posterior superior temporal sulcus (rpSTS) responded more to bimodal stimuli than to face or voice alone but only when the stimuli contained emotional expression. Face- and voice-selective regions of interest, extracted from independent functional localizers, similarly revealed multisensory integration in the face-selective rpSTS only; further, this was the only face-selective region that also responded significantly to voices. Dynamic causal modeling revealed that the rpSTS receives unidirectional information from the face-selective fusiform face area, and voice-selective temporal voice area, with emotional expression affecting the connection strength. Our study promotes a hierarchical model of face and voice integration, with convergence in the rpSTS, and that such integration depends on the (emotional) salience of the stimuli.

scholarly journals Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Dongya Wu ◽  
Xin Li ◽  
Jun Feng
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Face Area ◽  
The Face ◽  
The Individual ◽  
The Right ◽  
And Function ◽  
The Relationship ◽  
The Brain

Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA.

scholarly journals Multi-hops functional connectivity improves individual prediction of fusiform face activation via a graph neural network

2020 ◽  
Author(s):  
Dongya Wu ◽  
Xin Li ◽  
Jun Feng
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Face Area ◽  
The Face ◽  
The Individual ◽  
The Right ◽  
And Function ◽  
The Relationship ◽  
The Brain

AbstractBrain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, based on a preliminary analysis, this proposal is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face response of the right fusiform face area (rFFA) via a multi-layers graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the 2-layers graph neural network is the best in characterizing rFFA’s face response and revealed a hierarchical network for the face processing of rFFA.

scholarly journals Digitizing the moving face: asymmetries of emotion and gender

2009 ◽  
Author(s):  
Ashish Desai
Keyword(s):  
Gender Differences ◽  
Repeated Measures ◽  
Right Hemisphere ◽  
Emotional Expressions ◽  
Facial Movement ◽  
Lower Face ◽  
Expression Of Emotion ◽  
The Face ◽  
The Right ◽  
The Brain

In a previous study with dextral males, Richardson and Bowers (1999) digitized real time video signals and found movement asymmetries over the left lower face for emotional, but not non-emotional expressions. These findings correspond to observations, based on subjective ratings of static pictures, that the left side of the face is more intensely expressive than the right (Sackeim, 1978). From a neuropsychological perspective, one possible interpretation of these findings is that emotional priming of the right hemisphere of the brain results in more muscular activity over the contralateral left than ipsilateral right side of the lower face. The purpose of the present study was to use computer-imaging methodology to determine whether there were gender differences in movement asymmetries across the face. We hypothesized that females would show less evidence of facial movement asymmetries during the expression of emotion. This hypothesis was based on findings of gender differences in the degree to which specific cognitive functions may be lateralized in the brain (i.e., females less lateralized than males). Forty-eight normal dextral college students (25 females, 23 males) were videotaped while they displayed voluntary emotional expressions. A quantitative measure of movement change (called entropy) was computed by subtracting the values of corresponding pixel intensities between adjacent frames and summing their differences. The upper and lower hemiface regions were examined separately due to differences in the cortical enervation of facial muscles in the upper (bilateral) versus lower face (contralateral). Repeated measures ANOVA’s were used to analyze for the amount of overall facial movement and for facial asymmetries. Certain emotions were associated with significantly greater overall facial movement than others (p < .0001), beginning with surprise and followed by happy > fear > (angry = sad) > neutral. Both males and females showed this same pattern, with no gender differences in the total amount of facial movement under voluntary conditions. In males, movement asymmetries favoring the lower left side of the face occurred for most emotional expressions. For females, all emotions were symmetric over the lower face. Our findings with computer digitizing techniques support the hypothesis that there are gender differences in facial movement asymmetries during the expression of emotion. They further underscore the view that emotional processing may represent a more widely distributed system throughout the brain in women than in men, corresponding to previous reports that language processes are also less lateralized in women.

scholarly journals Neural Tuning for Face Wholes and Parts in Human Fusiform Gyrus Revealed by fMRI Adaptation

2010 ◽  
Vol 104 (1) ◽  
pp. 336-345 ◽  
Author(s):  
Alison Harris ◽  
Geoffrey Karl Aguirre
Keyword(s):  
Functional Mri ◽  
Holistic Processing ◽  
Fusiform Face Area ◽  
Single Population ◽  
Face Area ◽  
Neural Populations ◽  
The Face ◽  
Adaptation Response ◽  
The Right ◽  
Fmri Adaptation

Although the right fusiform face area (FFA) is often linked to holistic processing, new data suggest this region also encodes part-based face representations. We examined this question by assessing the metric of neural similarity for faces using a continuous carryover functional MRI (fMRI) design. Using faces varying along dimensions of eye and mouth identity, we tested whether these axes are coded independently by separate part-tuned neural populations or conjointly by a single population of holistically tuned neurons. Consistent with prior results, we found a subadditive adaptation response in the right FFA, as predicted for holistic processing. However, when holistic processing was disrupted by misaligning the halves of the face, the right FFA continued to show significant adaptation, but in an additive pattern indicative of part-based neural tuning. Thus this region seems to contain neural populations capable of representing both individual parts and their integration into a face gestalt. A third experiment, which varied the asymmetry of changes in the eye and mouth identity dimensions, also showed part-based tuning from the right FFA. In contrast to the right FFA, the left FFA consistently showed a part-based pattern of neural tuning across all experiments. Together, these data support the existence of both part-based and holistic neural tuning within the right FFA, further suggesting that such tuning is surprisingly flexible and dynamic.

scholarly journals Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Anna Lardone ◽  
Marianna Liparoti ◽  
Pierpaolo Sorrentino ◽  
Rosaria Rucco ◽  
Francesca Jacini ◽  
...  
Keyword(s):  
Resting State ◽  
Mindfulness Meditation ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Long Term Effects ◽  
Age Related ◽  
Functional Topology ◽  
The Right ◽  
The Brain

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

scholarly journals Looking beyond the face area: lesion network mapping of prosopagnosia

Brain ◽  
2019 ◽  
Vol 142 (12) ◽  
pp. 3975-3990 ◽  
Author(s):  
Alexander L Cohen ◽  
Louis Soussand ◽  
Sherryse L Corrow ◽  
Olivier Martinaud ◽  
Jason J S Barton ◽  
...  
Keyword(s):  
Face Recognition ◽  
Brain Network ◽  
Face Area ◽  
The Face ◽  
Network Mapping

Face blindness can occur after injury to a variety of brain locations, and yet the regions critical for face recognition remain unclear. Cohen et al. show that lesions that cause face blindness map to a specific brain network, and use this to predict subclinical deficits in an independent lesion cohort.

scholarly journals Association Between Trait Empathy and Resting Brain Activity in Women With Primary Dysmenorrhea During the Pain and Pain-Free Phases

2020 ◽  
Vol 11 ◽  
Author(s):  
Wanghuan Dun ◽  
Tongtong Fan ◽  
Qiming Wang ◽  
Ke Wang ◽  
Jing Yang ◽  
...  
Keyword(s):  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Brain Network ◽  
Primary Dysmenorrhea ◽  
Pain Experience ◽  
Current State ◽  
The Neural Network ◽  
The Right ◽  
The Brain

Empathy refers to the ability to understand someone else's emotions and fluctuates with the current state in healthy individuals. However, little is known about the neural network of empathy in clinical populations at different pain states. The current study aimed to examine the effects of long-term pain on empathy-related networks and whether empathy varied at different pain states by studying primary dysmenorrhea (PDM) patients. Multivariate partial least squares was employed in 46 PDM women and 46 healthy controls (HC) during periovulatory, luteal, and menstruation phases. We identified neural networks associated with different aspects of empathy in both groups. Part of the obtained empathy-related network in PDM exhibited a similar activity compared with HC, including the right anterior insula and other regions, whereas others have an opposite activity in PDM, including the inferior frontal gyrus and right inferior parietal lobule. These results indicated an abnormal regulation to empathy in PDM. Furthermore, there was no difference in empathy association patterns in PDM between the pain and pain-free states. This study suggested that long-term pain experience may lead to an abnormal function of the brain network for empathy processing that did not vary with the pain or pain-free state across the menstrual cycle.

Is Transient Global Amnesia a Network Disease?

2018 ◽  
Vol 80 (5-6) ◽  
pp. 345-354 ◽  
Author(s):  
Kang Min Park ◽  
Byung In Lee ◽  
Sung Eun Kim
Keyword(s):  
White Matter ◽  
Healthy Subjects ◽  
Transient Global Amnesia ◽  
Brain Network ◽  
Network Measures ◽  
Postcentral Gyrus ◽  
Frontal Operculum ◽  
Global Amnesia ◽  
The Right ◽  
The Brain

Background: We evaluated a brain network using graph theoretical analysis and microstructural abnormalities of the white matter in patients with transient global amnesia (TGA). Methods: Twenty patients with TGA and healthy control subjects were recruited, and they underwent diffusion tensor imaging (DTI) scans. Graph theory was applied to obtain network measures based on DTI data. We investigated the network measures and microstructural abnormalities of white matter using tract-based spatial statistics (TBSS) analysis in the patients with TGA. Results: Measures of global topology were not different between the patients with TGA and healthy subjects. However, there were significant differences of hubs organization; the strength of the right superior and inferior orbitofrontal, the right inferior frontal operculum, the left superior parietal, and left postcentral gyrus, the cluster coefficient of the right middle orbitofrontal and left inferior parietal gyrus, the betweenness centrality of the left angular gyrus, and the pagerank centrality of the right superior and inferior orbitofrontal, right inferior frontal operculum, left superior parietal, and left postcentral gyrus in the patients with TGA were significantly lower than those in healthy subjects. Regarding the analysis of the white matter microstructure with TBSS, there were no differences in the fractional anisotropy and mean diffusivity values between the 2 groups. Conclusions: We newly identify a reorganization of network hubs of the brain network in patients with TGA, especially in the regions of the default-mode network. These alterations of the brain network may play a role in the pathophysiologic mechanism underlying TGA and suggest that TGA is a network disease.

Multi-Item Discriminability Pattern to Faces in Developmental Prosopagnosia Reveals Distinct Mechanisms of Face Processing

2019 ◽  
Vol 30 (5) ◽  
pp. 2986-2996
Author(s):  
Xue Tian ◽  
Ruosi Wang ◽  
Yuanfang Zhao ◽  
Zonglei Zhen ◽  
Yiying Song ◽  
...  
Keyword(s):  
Face Processing ◽  
Alternative Hypothesis ◽  
Normal Population ◽  
Imaging Study ◽  
Multiple Sources ◽  
Neural Basis ◽  
Face Area ◽  
Normal Individuals ◽  
The Face ◽  
The Right

Abstract Previous studies have shown that individuals with developmental prosopagnosia (DP) show specific deficits in face processing. However, the mechanism underlying the deficits remains largely unknown. One hypothesis suggests that DP shares the same mechanism as normal population, though their faces processing is disproportionally impaired. An alternative hypothesis emphasizes a qualitatively different mechanism of DP processing faces. To test these hypotheses, we instructed DP and normal individuals to perceive faces and objects. Instead of calculating accuracy averaging across stimulus items, we used the discrimination accuracy for each item to construct a multi-item discriminability pattern. We found DP’s discriminability pattern was less similar to that of normal individuals when perceiving faces than perceiving objects, suggesting that DP has qualitatively different mechanism in representing faces. A functional magnetic resonance imaging study was conducted to reveal the neural basis and found that multi-voxel activation patterns for faces in the right fusiform face area and occipital face area of DP were deviated away from the mean activation pattern of normal individuals. Further, the face representation was more heterogeneous in DP, suggesting that deficits of DP may come from multiple sources. In short, our study provides the first direct evidence that DP processes faces qualitatively different from normal population.

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