Special Issue "Best Practices in Social Neuroscience"

A special issue of Brain Sciences (ISSN 2076-3425).

Deadline for manuscript submissions: closed (1 July 2017) | Viewed by 39881

Special Issue Editor

Dr. Stephanie Cacioppo
E-Mail Website1 Website2
Guest Editor
Department of Psychiatry and Behavioral Neuroscience, Biological Sciences Division, The University of Chicago Pritzker School of Medicine, Chicago, IL, 60637, USA
Interests: social neuroscience; methods; neuroimaging; social connection; mirror neuron system; successful interpersonal relationships

Special Issue Information

Dear Colleagues,

Introduced in 1992, social neuroscience seeks to specify the neural, hormonal, cellular, and genetic mechanisms underlying social structures and processes. The past twenty-four years have seen, not only the acceptance of the multi-level doctrine of social neuroscience, but also its tremendous growth as an integrative, rigorous, and interdisciplinary field. In addition to traditional physiological measures (e.g., facial electromyography, impedance cardiography, electrocardiography, eye-tracking, electrodermal activity), cutting edge methods, such as those developed in genomics, pharmacology, animal models, and contemporary neuroimaging (such as positron emission tomography, PET; functional magnetic resonance imaging, fMRI; electroencephalogram, EEG; event-related potentials, ERPs; magneto-encephalography, MEG; or transcranial magnetic stimulations, TMS) offer unprecedented access to the biological basis of social behaviors. With such a fast development, there is a crucial need for the current and next generation of social neuroscientists to stay up-to-date with the best practices in data collection and cutting-edge analytic tools and procedures associated with the study the social brain and its dynamics. This Special Issue aims to address this need by calling for best practice papers in various fields of research in social neuroscience, including (but not restricted to) social genomics, functional and electrical neuroimaging, simulatenous hyperscanning, transcranial magnetic stimulation, and neuropharmacology. Papers that focus on best practices for data collection and analyses associated with either human or animal research in social neuroscience research are welcome.

Stephanie Cacioppo, Ph.D.
Guest Editor

Manuscript Submission Information

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Keywords

  • Social neuroscience
  • Best practices
  • Social brain
  • Neuroimaging
  • Genomics
  • Neuropharmacology
  • Human
  • Animal models

Published Papers (8 papers)

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Research

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Article
Using Machine Learning to Discover Latent Social Phenotypes in Free-Ranging Macaques
Brain Sci. 2017, 7(7), 91; https://doi.org/10.3390/brainsci7070091 - 21 Jul 2017
Cited by 6 | Viewed by 3966
Abstract
Investigating the biological bases of social phenotypes is challenging because social behavior is both high-dimensional and richly structured, and biological factors are more likely to influence complex patterns of behavior rather than any single behavior in isolation. The space of all possible patterns [...] Read more.
Investigating the biological bases of social phenotypes is challenging because social behavior is both high-dimensional and richly structured, and biological factors are more likely to influence complex patterns of behavior rather than any single behavior in isolation. The space of all possible patterns of interactions among behaviors is too large to investigate using conventional statistical methods. In order to quantitatively define social phenotypes from natural behavior, we developed a machine learning model to identify and measure patterns of behavior in naturalistic observational data, as well as their relationships to biological, environmental, and demographic sources of variation. We applied this model to extensive observations of natural behavior in free-ranging rhesus macaques, and identified behavioral states that appeared to capture periods of social isolation, competition over food, conflicts among groups, and affiliative coexistence. Phenotypes, represented as the rate of being in each state for a particular animal, were strongly and broadly influenced by dominance rank, sex, and social group membership. We also identified two states for which variation in rates had a substantial genetic component. We discuss how this model can be extended to identify the contributions to social phenotypes of particular genetic pathways. Full article
(This article belongs to the Special Issue Best Practices in Social Neuroscience)
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Article
Brain Interaction during Cooperation: Evaluating Local Properties of Multiple-Brain Network
Brain Sci. 2017, 7(7), 90; https://doi.org/10.3390/brainsci7070090 - 21 Jul 2017
Cited by 34 | Viewed by 3523
Abstract
Subjects’ interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental [...] Read more.
Subjects’ interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental effort required by subjects while facing a situation that is getting harder, that is, mental workload, to define an objective measure based on how and if team members are interacting is not so straightforward. In this study, behavioral, subjective and synchronized electroencephalographic data were collected from couples involved in a cooperative task to describe the relationship between task difficulty and team coordination, in the sense of interaction aimed at cooperatively performing the assignment. Multiple-brain connectivity analysis provided information about the whole interacting system. The results showed that averaged local properties of a brain network were affected by task difficulty. In particular, strength changed significantly with task difficulty and clustering coefficients strongly correlated with the workload itself. In particular, a higher workload corresponded to lower clustering values over the central and parietal brain areas. Such results has been interpreted as less efficient organization of the network when the subjects’ activities, due to high workload tendencies, were less coordinated. Full article
(This article belongs to the Special Issue Best Practices in Social Neuroscience)
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Article
Transgenerational Social Stress Alters Immune–Behavior Associations and the Response to Vaccination
Brain Sci. 2017, 7(7), 89; https://doi.org/10.3390/brainsci7070089 - 21 Jul 2017
Cited by 7 | Viewed by 3441
Abstract
Similar to the multi-hit theory of schizophrenia, social behavior pathologies are mediated by multiple factors across generations, likely acting additively, synergistically, or antagonistically. Exposure to social adversity, especially during early life, has been proposed to induce depression symptoms through immune mediated mechanisms. Basal [...] Read more.
Similar to the multi-hit theory of schizophrenia, social behavior pathologies are mediated by multiple factors across generations, likely acting additively, synergistically, or antagonistically. Exposure to social adversity, especially during early life, has been proposed to induce depression symptoms through immune mediated mechanisms. Basal immune factors are altered in a variety of neurobehavioral models. In the current study, we assessed two aspects of a transgenerational chronic social stress (CSS) rat model and its effects on the immune system. First, we asked whether exposure of F0 dams and their F1 litters to CSS changes basal levels of IL-6, TNF, IFN-γ, and social behavior in CSS F1 female juvenile rats. Second, we asked whether the F2 generation could generate normal immunological responses following vaccination with Mycobacterium bovis Bacillus Calmette–Guérin (BCG). We report several changes in the associations between social behaviors and cytokines in the F1 juvenile offspring of the CSS model. It is suggested that changes in the immune–behavior relationships in F1 juveniles indicate the early stages of immune mediated disruption of social behavior that becomes more apparent in F1 dams and the F2 generation. We also report preliminary evidence of elevated IL-6 and impaired interferon-gamma responses in BCG-vaccinated F2 females. In conclusion, transgenerational social stress alters both immune–behavior associations and responses to vaccination. It is hypothesized that the effects of social stress may accumulate over generations through changes in the immune system, establishing the immune system as an effective preventative or treatment target for social behavior pathologies. Full article
(This article belongs to the Special Issue Best Practices in Social Neuroscience)
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Article
How Does Psychosocial Behavior Contribute to Cognitive Health in Old Age?
Brain Sci. 2017, 7(6), 56; https://doi.org/10.3390/brainsci7060056 - 23 May 2017
Cited by 20 | Viewed by 4281
Abstract
With the aging of the U.S. population, the number of cognitively disabled persons is expected to substantially increase in coming decades, underscoring the urgent need for effective interventions. Here, we review the current evidence linking psychosocial factors to late-life cognitive loss and consider [...] Read more.
With the aging of the U.S. population, the number of cognitively disabled persons is expected to substantially increase in coming decades, underscoring the urgent need for effective interventions. Here, we review the current evidence linking psychosocial factors to late-life cognitive loss and consider the study design needed to illuminate the biologic bases of the associations. We then examine an ongoing study that includes several of the key design elements, the Rush Memory and Aging Project. In this longitudinal clinical-pathological cohort study, indicators of personality, social connectedness, and psychological well-being were shown to predict late-life cognitive outcomes. Participants who died underwent a uniform neuropathologic examination to quantify common dementia-related pathologies. Some psychosocial indicators were associated with cerebral infarction; some indicators modified the association of neurodegenerative pathologies with cognitive loss; and the association of some indicators with cognitive outcomes appears to be independent of the pathologies traditionally associated with late-life dementia. These findings suggest that psychosocial behavior influences late-life cognitive health through multiple neurobiologic mechanisms. A better understanding of these mechanisms may lead to novel strategies for preserving cognitive health in old age. Full article
(This article belongs to the Special Issue Best Practices in Social Neuroscience)
Article
Virtual Reality for Research in Social Neuroscience
Brain Sci. 2017, 7(4), 42; https://doi.org/10.3390/brainsci7040042 - 16 Apr 2017
Cited by 75 | Viewed by 7523
Abstract
The emergence of social neuroscience has significantly advanced our understanding of the relationship that exists between social processes and their neurobiological underpinnings. Social neuroscience research often involves the use of simple and static stimuli lacking many of the potentially important aspects of real [...] Read more.
The emergence of social neuroscience has significantly advanced our understanding of the relationship that exists between social processes and their neurobiological underpinnings. Social neuroscience research often involves the use of simple and static stimuli lacking many of the potentially important aspects of real world activities and social interactions. Whilst this research has merit, there is a growing interest in the presentation of dynamic stimuli in a manner that allows researchers to assess the integrative processes carried out by perceivers over time. Herein, we discuss the potential of virtual reality for enhancing ecological validity while maintaining experimental control in social neuroscience research. Virtual reality is a technology that allows for the creation of fully interactive, three-dimensional computerized models of social situations that can be fully controlled by the experimenter. Furthermore, the introduction of interactive virtual characters—either driven by a human or by a computer—allows the researcher to test, in a systematic and independent manner, the effects of various social cues. We first introduce key technical features and concepts related to virtual reality. Next, we discuss the potential of this technology for enhancing social neuroscience protocols, drawing on illustrative experiments from the literature. Full article
(This article belongs to the Special Issue Best Practices in Social Neuroscience)

Review

Jump to: Research

Review
Assessing the Effectiveness of Neurofeedback Training in the Context of Clinical and Social Neuroscience
Brain Sci. 2017, 7(8), 95; https://doi.org/10.3390/brainsci7080095 - 07 Aug 2017
Cited by 18 | Viewed by 6074
Abstract
Social neuroscience benefits from the experimental manipulation of neuronal activity. One possible manipulation, neurofeedback, is an operant conditioning-based technique in which individuals sense, interact with, and manage their own physiological and mental states. Neurofeedback has been applied to a wide variety of psychiatric [...] Read more.
Social neuroscience benefits from the experimental manipulation of neuronal activity. One possible manipulation, neurofeedback, is an operant conditioning-based technique in which individuals sense, interact with, and manage their own physiological and mental states. Neurofeedback has been applied to a wide variety of psychiatric illnesses, as well as to treat sub-clinical symptoms, and even to enhance performance in healthy populations. Despite growing interest, there persists a level of distrust and/or bias in the medical and research communities in the USA toward neurofeedback and other functional interventions. As a result, neurofeedback has been largely ignored, or disregarded within social neuroscience. We propose a systematic, empirically-based approach for assessing the effectiveness, and utility of neurofeedback. To that end, we use the term perturbative physiologic plasticity to suggest that biological systems function as an integrated whole that can be perturbed and guided, either directly or indirectly, into different physiological states. When the intention is to normalize the system, e.g., via neurofeedback, we describe it as self-directed neuroplasticity, whose outcome is persistent functional, structural, and behavioral changes. We argue that changes in physiological, neuropsychological, behavioral, interpersonal, and societal functioning following neurofeedback can serve as objective indices and as the metrics necessary for assessing levels of efficacy. In this chapter, we examine the effects of neurofeedback on functional connectivity in a few clinical disorders as case studies for this approach. We believe this broader perspective will open new avenues of investigation, especially within social neuroscience, to further elucidate the mechanisms and effectiveness of these types of interventions, and their relevance to basic research. Full article
(This article belongs to the Special Issue Best Practices in Social Neuroscience)
Review
From the Brain to the Field: The Applications of Social Neuroscience to Economics, Health and Law
Brain Sci. 2017, 7(8), 94; https://doi.org/10.3390/brainsci7080094 - 28 Jul 2017
Cited by 10 | Viewed by 3927
Abstract
Social neuroscience aims to understand the biological systems that underlie people’s thoughts, feelings and actions in light of the social context in which they operate. Over the past few decades, social neuroscience has captured the interest of scholars, practitioners, and experts in other [...] Read more.
Social neuroscience aims to understand the biological systems that underlie people’s thoughts, feelings and actions in light of the social context in which they operate. Over the past few decades, social neuroscience has captured the interest of scholars, practitioners, and experts in other disciplines, as well as the general public who more and more draw upon the insights and methods of social neuroscience to explain, predict and change behavior. With the popularity of the field growing, it has become increasingly important to consider the validity of social neuroscience findings as well as what questions it can and cannot address. In the present review article, we examine the contribution of social neuroscience to economics, health, and law, three domains with clear societal relevance. We address the concerns that the extrapolation of neuroscientific results to applied social issues raises within each of these domains, and we suggest guidelines and good practices to circumvent these concerns. Full article
(This article belongs to the Special Issue Best Practices in Social Neuroscience)
Review
A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies
Brain Sci. 2017, 7(6), 58; https://doi.org/10.3390/brainsci7060058 - 31 May 2017
Cited by 76 | Viewed by 6658
Abstract
Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and [...] Read more.
Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed. Full article
(This article belongs to the Special Issue Best Practices in Social Neuroscience)
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