scholarly journals Inter-subject phase synchronization differentiates neural networks underlying physical pain empathy

2019 ◽  
Author(s):  
Lei Xu ◽  
Taylor Bolt ◽  
Jason S. Nomi ◽  
Jialin Li ◽  
Xiaoxiao Zheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Social Cognitive ◽  
Phase Synchronization ◽  
Fmri Data ◽  
Physical Pain ◽  
Neural Basis ◽  
Robust Network ◽  
Affective Pain ◽  
Face Stimuli ◽  
Pain Experiences

AbstractRecent approaches for understanding the neural basis of pain empathy emphasize the dynamic construction of neural networks underlying this multifaceted social cognitive process. Inter-subject phase synchronization (ISPS) is an approach for exploratory analysis of task-based fMRI data that reveals brain networks dynamically synchronized to task-features across participants. We applied ISPS to task-fMRI data assessing vicarious pain empathy in a large sample of healthy participants (n=238). The task employed physical (limb) and affective (faces) painful and corresponding non-painful visual stimuli. ISPS revealed two distinct networks synchronized during physical pain observation, one encompassing anterior insula and midcingulate regions strongly engaged in (vicarious) pain, and another encompassing parietal and inferior frontal regions associated with social cognitive processes which may further modulate and support the physical pain empathic response. No robust network synchronization was observed while processing affective pain, possibly reflecting high inter-individual variation in response to socially transmitted pain experiences. ISPS also revealed networks related to task onset or general processing of physical (limb) or affective (face) stimuli which encompassed networks engaged in object manipulation or face processing, respectively. Together, the ISPS approach permits segregation of networks engaged in different psychological processes, providing additional insight into shared neural mechanisms of empathy for physical pain, but not affective pain, across individuals.

scholarly journals Inter-subject phase synchronization differentiates neural networks underlying physical pain empathy

2020 ◽  
Vol 15 (2) ◽  
pp. 225-233 ◽  
Author(s):  
Lei Xu ◽  
Taylor Bolt ◽  
Jason S Nomi ◽  
Jialin Li ◽  
Xiaoxiao Zheng ◽  
...  
Keyword(s):  
Social Cognitive ◽  
Phase Synchronization ◽  
Fmri Data ◽  
Physical Pain ◽  
Neural Basis ◽  
Robust Network ◽  
Affective Pain ◽  
Face Stimuli ◽  
Pain Experiences ◽  
Insight Into

Abstract Recent approaches for understanding the neural basis of pain empathy emphasize the dynamic construction of networks underlying this multifaceted social cognitive process. Inter-subject phase synchronization (ISPS) is an approach for exploratory analysis of task-fMRI data that reveals brain networks dynamically synchronized to task-features across participants. We applied ISPS to task-fMRI data assessing vicarious pain empathy in healthy participants (n = 238). The task employed physical (limb) and affective (face) painful and corresponding non-painful visual stimuli. ISPS revealed two distinct networks synchronized during physical pain observation, one encompassing anterior insula and midcingulate regions strongly engaged in (vicarious) pain and another encompassing parietal and inferior frontal regions associated with social cognitive processes which may modulate and support the physical pain empathic response. No robust network synchronization was observed for affective pain, possibly reflecting high inter-individual variation in response to socially transmitted pain experiences. ISPS also revealed networks related to task onset or general processing of physical (limb) or affective (face) stimuli which encompassed networks engaged in object manipulation or face processing, respectively. Together, the ISPS approach permits segregation of networks engaged in different psychological processes, providing additional insight into shared neural mechanisms of empathy for physical pain, but not affective pain, across individuals.

scholarly journals What binds us? Inter-brain neural synchronization and its implications for theories of human consciousness

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ana Lucía Valencia ◽  
Tom Froese
Keyword(s):  
Large Scale ◽  
Social Connectedness ◽  
Social Cognitive ◽  
Phase Synchronization ◽  
Functional Integration ◽  
Human Cognition ◽  
Human Consciousness ◽  
Neural Basis ◽  
Standard View ◽  
The Right

Abstract The association between neural oscillations and functional integration is widely recognized in the study of human cognition. Large-scale synchronization of neural activity has also been proposed as the neural basis of consciousness. Intriguingly, a growing number of studies in social cognitive neuroscience reveal that phase synchronization similarly appears across brains during meaningful social interaction. Moreover, this inter-brain synchronization has been associated with subjective reports of social connectedness, engagement, and cooperativeness, as well as experiences of social cohesion and ‘self-other merging’. These findings challenge the standard view of human consciousness as essentially first-person singular and private. We therefore revisit the recent controversy over the possibility of extended consciousness and argue that evidence of inter-brain synchronization in the fastest frequency bands overcomes the hitherto most convincing sceptical position. If this proposal is on the right track, our understanding of human consciousness would be profoundly transformed, and we propose a method to test this proposal experimentally.

scholarly journals Exploring Relations Among Traumatic, Posttraumatic, and Physical Pain Experiences in Methadone-Maintained Patients

2011 ◽  
Vol 12 (1) ◽  
pp. 22-28 ◽  
Author(s):  
Declan T. Barry ◽  
Mark Beitel ◽  
Christopher J. Cutter ◽  
Brian Garnet ◽  
Dipa Joshi ◽  
...  
Keyword(s):  
Physical Pain ◽  
Pain Experiences

scholarly journals Opinion on monoaminergic contributions to traits and temperament

2018 ◽  
Vol 373 (1744) ◽  
pp. 20170153 ◽  
Author(s):  
T. W. Robbins
Keyword(s):  
Neural Networks ◽  
Individual Differences ◽  
Social Functioning ◽  
Personality Factors ◽  
Monoamine Neurotransmitters ◽  
Neural Basis ◽  
Theme Issue ◽  
Experimental Animals ◽  
Neuroscience Research ◽  
Definition Of

This article critically reviews evidence relating temperamental traits and personality factors to the monoamine neurotransmitters, especially dopamine and serotonin. The genetic evidence is not yet considered to be conclusive and it is argued that basic neuroscience research on the neural basis of behaviour in experimental animals should be taken more into account. While questionnaire and lexical methodology including the ‘Five Factor’ theory has been informative (mostly for the traits relevant to social functioning, i.e. personality), biologically oriented approaches should be employed with more objective, theoretically grounded measures of cognition and behaviour, combined with neuroimaging and psychopharmacology, where appropriate. This strategy will enable specific functions of monoamines and other neuromodulators such as acetylcholine and neuropeptides (such as orexin) to be defined with respect to their roles in modulating activity in specific neural networks—leading to a more realistic definition of their interactive roles in complex, biologically based traits (i.e. temperament). This article is part of the theme issue ‘Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences’.

scholarly journals A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Jinlong Hu ◽  
Yuezhen Kuang ◽  
Bin Liao ◽  
Lijie Cao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Imaging ◽  
Motor Task ◽  
Data Classification ◽  
Fmri Data ◽  
Imaging Data ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Brain Imaging Data ◽  
3D Cnn

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.

Social cognitive neural networks during in-group and out-group interactions

NeuroImage ◽  
2008 ◽  
Vol 41 (4) ◽  
pp. 1447-1461 ◽  
Author(s):  
James K. Rilling ◽  
Julien E. Dagenais ◽  
David R. Goldsmith ◽  
Andrea L. Glenn ◽  
Giuseppe Pagnoni
Keyword(s):  
Neural Networks ◽  
Social Cognitive ◽  
Group Interactions

scholarly journals Stimulus context alters neural representations of faces in inferotemporal cortex

2017 ◽  
Vol 117 (1) ◽  
pp. 336-347 ◽  
Author(s):  
Behrad Noudoost ◽  
Neda Nategh ◽  
Kelsey Clark ◽  
Hossein Esteky
Keyword(s):  
Visual Environment ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Visual Response ◽  
Viewing Angle ◽  
Visual Responses ◽  
Stimulus Information ◽  
Neural Basis ◽  
Face Images ◽  
Face Stimuli

One goal of our nervous system is to form predictions about the world around us to facilitate our responses to upcoming events. One basis for such predictions could be the recently encountered visual stimuli, or the recent statistics of the visual environment. We examined the effect of recently experienced stimulus statistics on the visual representation of face stimuli by recording the responses of face-responsive neurons in the final stage of visual object recognition, the inferotemporal (IT) cortex, during blocks in which the probability of seeing a particular face was either 100% or only 12%. During the block with only face images, ∼30% of IT neurons exhibit enhanced anticipatory activity before the evoked visual response. This anticipatory modulation is followed by greater activity, broader view tuning, more distributed processing, and more reliable responses of IT neurons to the face stimuli. These changes in the visual response were sufficient to improve the ability of IT neurons to represent a variable property of the predictable face images (viewing angle), as measured by the performance of a simple linear classifier. These results demonstrate that the recent statistics of the visual environment can facilitate processing of stimulus information in the population neuronal representation. NEW & NOTEWORTHY Neurons in inferotemporal (IT) cortex anticipate the arrival of a predictable stimulus, and visual responses to an expected stimulus are more distributed throughout the population of IT neurons, providing an enhanced representation of second-order stimulus information (in this case, viewing angle). The findings reveal a potential neural basis for the behavioral benefits of contextual expectation.

ADVANCED IMAGE PROCESSING CELLULAR NEURAL NETWORKS

2007 ◽  
Vol 17 (04) ◽  
pp. 1109-1150 ◽  
Author(s):  
MAKOTO ITOH ◽  
LEON O. CHUA
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Networks ◽  
Visual Illusions ◽  
Cognitive Engineering ◽  
Neural Basis ◽  
Brain Functions ◽  
Texture Generation ◽  
High Level ◽  
Neural Field Models

Many useful and well-known image processing templates for cellular neural networks (CNN's) can be derived from neural field models, thereby providing a neural basis for the CNN paradigm. The potential ability of multitasking image processing is investigated by using these templates. Many visual illusions are simulated via CNN image processing. The ability of the CNN to mimic such high-level brain functions suggests possible applications of the CNN in cognitive engineering. Furthermore, two kinds of painting-like image processings, namely, texture generation and illustration style transformation are investigated.

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