Your presence soothes me: a neural process model of aversive emotion regulation via social buffering

Abstract The reduction of aversive emotions by a conspecific’s presence—called social buffering—is a universal phenomenon in the mammalian world and a powerful form of human social emotion regulation. Animal and human studies on neural pathways underlying social buffering typically examined physiological reactions or regional brain activations. However, direct links between emotional and social stimuli, distinct neural processes and behavioural outcomes are still missing. Using data of 27 female participants, the current study delineated a large-scale process model of social buffering’s neural underpinnings, connecting changes in neural activity to emotional behaviour by means of voxel-wise multilevel mediation analysis. Our results confirmed that three processes underlie human social buffering: (i) social support-related reduction of activity in the orbitofrontal cortex, ventromedial and dorsolateral prefrontal cortices, anterior and mid-cingulate; (ii) downregulation of aversive emotion-induced brain activity in the superficial cortex-like amygdala and mediodorsal thalamus; and (iii) downregulation of reported aversive feelings. Results of the current study provide evidence for a distinct neural process model of aversive emotion regulation in humans by social buffering.

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On the Process Modeling of Software Crowdsourcing Based on Competitive Relation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.4708 ◽

2014 ◽

Vol 989-994 ◽

pp. 4708-4712 ◽

Cited By ~ 1

Author(s):

Xiao Lan Xu ◽

Ya Wang

Keyword(s):

Software Quality ◽

Process Modeling ◽

Process Model ◽

Large Scale ◽

Development Process ◽

Software Development Process ◽

System Software ◽

Software Cost ◽

Basic Framework ◽

Using Data

With the increase of the scale and complexity of software system software crowdsourcing development have gradually favored by the industry and scholars, to solve the large-scale, large system to develop effective solutions. In this background, this article first introduces the crowdsourcing the basic framework of the development process of software, the basic framework of crowdsourcing and then based on the software development process of put forward a comprehensive software Quality (Quality), the software task Cost (Cost) and software (Value) of a reward QCV model. Finally, this paper, by using data from the topcoder, the QCV process model is verified, it is concluded that the software quality, software cost and interaction relationship between reward.

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Choosing Money over Drugs: The Neural Underpinnings of Difficult Choice in Chronic Cocaine Users

Journal of Addiction ◽

10.1155/2014/189853 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 9

Author(s):

Michael J. Wesley ◽

Terry Lohrenz ◽

Mikhail N. Koffarnus ◽

Samuel M. McClure ◽

Richard De La Garza ◽

...

Keyword(s):

Intertemporal Choice ◽

Brain Activity ◽

Dorsal Striatum ◽

Revealed Preferences ◽

Chronic Cocaine ◽

Left Dlpfc ◽

Dorsolateral Prefrontal ◽

Cocaine Users ◽

Neural Underpinnings ◽

Choice Tasks

Addiction is considered a disorder that drives individuals to choose drugs at the expense of healthier alternatives. However, chronic cocaine users (CCUs) who meet addiction criteria retain the ability to choose money in the presence of the opportunity to choose cocaine. The neural mechanisms that differentiate CCUs from non-cocaine using controls (Controls) while executing these preferred choices remain unknown. Thus, therapeutic strategies aimed at shifting preferences towards healthier alternatives remain somewhat uninformed. This study used BOLD neuroimaging to examine brain activity as fifty CCUs and Controls performed single- and cross-commodity intertemporal choice tasks for money and/or cocaine. Behavioral analyses revealed preferences for each commodity type. Imaging analyses revealed the brain activity that differentiated CCUs from Controls while choosing money over cocaine. We observed that CCUs devalued future commodities more than Controls. Choices for money as opposed to cocaine correlated with greater activity in dorsal striatum of CCUs, compared to Controls. In addition, choices for future money as opposed to immediate cocaine engaged the left dorsolateral prefrontal cortex (DLPFC) of CCUs more than Controls. These data suggest that the ability of CCUs to execute choices away from cocaine relies on activity in the dorsal striatum and left DLPFC.

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The hidden cost of receiving favors: A theory of indebtedness

10.1101/2020.02.03.926295 ◽

2020 ◽

Author(s):

Xiaoxue Gao ◽

Eshin Jolly ◽

Hongbo Yu ◽

Huiying Liu ◽

Xiaolin Zhou ◽

...

Keyword(s):

Prefrontal Cortex ◽

Theoretical Model ◽

Dorsolateral Prefrontal Cortex ◽

Large Scale ◽

Brain Activity ◽

Ventromedial Prefrontal Cortex ◽

Utility Model ◽

Dorsomedial Prefrontal Cortex ◽

Dorsolateral Prefrontal ◽

Hidden Cost

AbstractReceiving help or a favor from another person can sometimes have a hidden cost. In this study, we explore these hidden costs by developing and validating a theoretical model of indebtedness across three studies that combine large-scale experience sampling, interpersonal games, computational modeling, and neuroimaging. Our model captures how individuals infer the altruistic and strategic intentions of the benefactor. These inferences produce distinct feelings of guilt and obligation that together comprise indebtedness and motivate reciprocity. Altruistic intentions convey care and concern and are associated with activity in the insula, dorsolateral prefrontal cortex and ventromedial prefrontal cortex, while strategic intentions convey expectations of future reciprocity and are associated with activation in the temporal parietal junction and dorsomedial prefrontal cortex. We further develop a neural utility model of indebtedness using multivariate patterns of brain activity that captures the tradeoff between these feelings and reliably predicts reciprocity behavior.

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Neural Correlates of Self-distraction from Anxiety and a Process Model of Cognitive Emotion Regulation

Journal of Cognitive Neuroscience ◽

10.1162/jocn.2006.18.8.1266 ◽

2006 ◽

Vol 18 (8) ◽

pp. 1266-1276 ◽

Cited By ~ 109

Author(s):

Raffael Kalisch ◽

Katja Wiech ◽

Katrin Herrmann ◽

Raymond J. Dolan

Keyword(s):

Emotion Regulation ◽

Process Model ◽

Brain Activity ◽

Cognitive Strategies ◽

Neural Correlates ◽

Emotional Reactions ◽

Imaging Studies ◽

Cognitive Emotion Regulation ◽

Monitoring Process ◽

Neural Processes

Cognitive strategies used in volitional emotion regulation include self-distraction and reappraisal (reinterpretation). There is debate as to what the psychological and neurobiological mechanisms underlying these strategies are. For example, it is unclear whether self-distraction and reappraisal, although distinct at a phenomenological level, are also mediated by distinct neural processes. This is partly because imaging studies on reappraisal and self-distraction have been performed in different emotional contexts and are difficult to compare. We have therefore investigated the neural correlates of self-distraction, as indexed by a thought suppression task, in an anticipatory anxiety paradigm previously employed by us to study reappraisal. Brain activity was measured by functional magnetic resonance imaging. We show that self-distraction recruits the left lateral prefrontal cortex. Based on a review of the existing data, we develop a process model of cognitive emotion regulation. The model posits that both self-distraction and reappraisal attenuate emotional reactions through replacement of emotional by neutral mental contents but achieve replacement in different ways. This is associated with a dependence of self-distraction on a left prefrontal production function, whereas reappraisal depends on a right prefrontal higher order monitoring process.

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Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks

Scientific Reports ◽

10.1038/s41598-021-02079-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Marina Krylova ◽

Stavros Skouras ◽

Adeel Razi ◽

Andrew A. Nicholson ◽

Alexander Karner ◽

...

Keyword(s):

Emotion Regulation ◽

Resting State ◽

Large Scale ◽

Brain Activity ◽

Self Regulation ◽

Learning Networks ◽

Neurofeedback Training ◽

Learning Mechanisms ◽

Social Emotion ◽

Target Network

AbstractNeurofeedback allows for the self-regulation of brain circuits implicated in specific maladaptive behaviors, leading to persistent changes in brain activity and connectivity. Positive-social emotion regulation neurofeedback enhances emotion regulation capabilities, which is critical for reducing the severity of various psychiatric disorders. Training dorsomedial prefrontal cortex (dmPFC) to exert a top-down influence on bilateral amygdala during positive-social emotion regulation progressively (linearly) modulates connectivity within the trained network and induces positive mood. However, the processes during rest that interleave the neurofeedback training remain poorly understood. We hypothesized that short resting periods at the end of training sessions of positive-social emotion regulation neurofeedback would show alterations within emotion regulation and neurofeedback learning networks. We used complementary model-based and data-driven approaches to assess how resting-state connectivity relates to neurofeedback changes at the end of training sessions. In the experimental group, we found lower progressive dmPFC self-inhibition and an increase of connectivity in networks engaged in emotion regulation, neurofeedback learning, visuospatial processing, and memory. Our findings highlight a large-scale synergy between neurofeedback and resting-state brain activity and connectivity changes within the target network and beyond. This work contributes to our understanding of concomitant learning mechanisms post training and facilitates development of efficient neurofeedback training.

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The Frustration Regulation Journal: A Reflective Framework for Educators

Relay Journal ◽

10.37237/relay/020205 ◽

2019 ◽

Author(s):

Sam Morris

Keyword(s):

Emotion Regulation ◽

Process Model ◽

Negative Impact ◽

Language Education ◽

Short Article ◽

Language Classroom ◽

As Stress ◽

And Control ◽

Important System ◽

Behavioural Strategies

Teachers and advisors involved in the emotional business of language education feel frustrated from time to time, and if such emotions are not managed healthily, they may lead to negative outcomes such as stress and burnout. One important system for taking control of frustration is emotion regulation, the cognitive and behavioural strategies through which individuals manage their emotions. In this short article, I define frustration and discuss its negative impact on the language classroom. I then introduce a structured reflective journaling tool, built upon Gross’s Process model of emotion regulation (Gross, 2014, 2015) which may help teachers and advisors develop greater awareness and control over experiences of frustration.

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Student Perceptions of Community: A Different Perspective

NASPA Journal ◽

10.2202/0027-6014.1064 ◽

1998 ◽

Vol 35 (4) ◽

Author(s):

Jackie Clark ◽

Joan Hirt

Keyword(s):

Student Perceptions ◽

Large Scale ◽

Research University ◽

Small Scale ◽

Social Environments ◽

Common Assumption ◽

Small Communities ◽

Public Research University ◽

The Common ◽

Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

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Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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Nebula: ultra-efficient mapping-free structural variant genotyper

Nucleic Acids Research ◽

10.1093/nar/gkab025 ◽

2021 ◽

Author(s):

Parsoa Khorsand ◽

Fereydoun Hormozdiari

Keyword(s):

Large Scale ◽

Structural Variants ◽

Sequencing Technologies ◽

Generic Framework ◽

Common Genetic Variants ◽

Order Of Magnitude ◽

Complex Events ◽

Comparable Accuracy ◽

Using Data ◽

Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

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Identifying a Common Functional Framework for Apathy Large-Scale Brain Network

Journal of Personalized Medicine ◽

10.3390/jpm11070679 ◽

2021 ◽

Vol 11 (7) ◽

pp. 679

Author(s):

Vincenzo Alfano ◽

Mariachiara Longarzo ◽

Giulia Mele ◽

Marcello Esposito ◽

Marco Aiello ◽

...

Keyword(s):

Large Scale ◽

Brain Magnetic Resonance Imaging ◽

Brain Network ◽

Neural Pathways ◽

Resonance Imaging ◽

Daily Life Activities ◽

Functional Differences ◽

Central Gyrus ◽

Magnetic Resonance Imaging Mri ◽

Disease Specific

Apathy is a neuropsychiatric condition characterized by reduced motivation, initiative, and interest in daily life activities, and it is commonly reported in several neurodegenerative disorders. The study aims to investigate large-scale brain networks involved in apathy syndrome in patients with frontotemporal dementia (FTD) and Parkinson’s disease (PD) compared to a group of healthy controls (HC). The study sample includes a total of 60 subjects: 20 apathetic FTD and PD patients, 20 non apathetic FTD and PD patients, and 20 HC matched for age. Two disease-specific apathy-evaluation scales were used to measure the presence of apathy in FTD and PD patients; in the same day, a 3T brain magnetic resonance imaging (MRI) with structural and resting-state functional (fMRI) sequences was acquired. Differences in functional connectivity (FC) were assessed between apathetic and non-apathetic patients with and without primary clinical diagnosis revealed, using a whole-brain, seed-to-seed approach. A significant hypoconnectivity between apathetic patients (both FTD and PD) and HC was detected between left planum polare and both right pre- or post-central gyrus. Finally, to investigate whether such neural alterations were due to the underlying neurodegenerative pathology, we replicated the analysis by considering two independent patients’ samples (i.e., non-apathetic PD and FTD). In these groups, functional differences were no longer detected. These alterations may subtend the involvement of neural pathways implicated in a specific reduction of information/elaboration processing and motor outcome in apathetic patients.

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