scholarly journals How Neuroimaging Can Aid the Interpretation of Art

2021 ◽  
Vol 15 ◽  
Author(s):  
Ladislav Kesner ◽  
Petr Adámek ◽  
Dominika Grygarová
Keyword(s):  
Brain Imaging ◽  
Cognitive Neuroscience ◽  
Behavioral Response ◽  
Strong Sense ◽  
Supportive Evidence ◽  
Intended Meaning ◽  
Historical Interpretation ◽  
Art Works ◽  
Neuroimaging Data ◽  
Averted Gaze

Cognitive neuroscience of art continues to be criticized for failing to provide interesting results about art itself. In particular, results of brain imaging experiments have not yet been utilized in interpretation of particular works of art. Here we revisit a recent study in which we explored the neuronal and behavioral response to painted portraits with a direct versus an averted gaze. We then demonstrate how fMRI results can be related to the art historical interpretation of a specific painting. The evidentiary status of neuroimaging data is not different from any other extra-pictorial facts that art historians uncover in their research and relate to their account of the significance of a work of art. They are not explanatory in a strong sense, yet they provide supportive evidence for the art writer’s inference about the intended meaning of a given work. We thus argue that brain imaging can assume an important role in the interpretation of particular art works.

scholarly journals Cognitive Network Neuroscience

2015 ◽  
Vol 27 (8) ◽  
pp. 1471-1491 ◽  
Author(s):  
John D. Medaglia ◽  
Mary-Ellen Lynall ◽  
Danielle S. Bassett
Keyword(s):  
Cognitive Neuroscience ◽  
Network Science ◽  
Structural Connectivity ◽  
Cognitive Network ◽  
Network Characteristics ◽  
Time Dynamics ◽  
Neuroimaging Data ◽  
Control Learning ◽  
And Function ◽  
Network Neuroscience

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

Are cognitive representations of friends' request refusals implemented in the orbitofrontal and dorsolateral prefrontal cortices? A cognitive neuroscience approach to ‘theory of mind’ in relationships

2006 ◽  
Vol 23 (2) ◽  
pp. 249-265 ◽  
Author(s):  
Alan D. Heisel ◽  
Michael J. Beatty
Keyword(s):  
Theory Of Mind ◽  
Brain Imaging ◽  
Social Relationships ◽  
Cognitive Neuroscience ◽  
Process Model ◽  
Critical Role ◽  
Brain Wave ◽  
Cognitive Representations ◽  
Dorsolateral Prefrontal ◽  
Prefrontal Cortices

Formulating cognitive representations of others' mental states when interpreting behavior (i.e., ‘theory of mind’) rather than merely focusing on the behavior is considered a distinctly human trait which both interpersonal scholars and cognitive neuroscientists agree plays a critical role in the development and maintenance of social relationships. Although brain-imaging studies have led to huge advances in the understanding of memory and language, theories of social relationships remain relatively uninformed by cognitive neuroscience. In the present study, hypotheses regarding the implementation of theory of mind in a relationship context are (a) derived from extant theory and research, and (b) tested via brain-imaging technology. Specifically, spectrum analyses were conducted using brain wave recordings collected by an electroencephalograph (EEG) monitoring oscillations in the gamma range for the orbitofrontal and dorsolateral prefrontal cortices while participants attempted to construct cognitive representations regarding a friend's request refusal. Results indicated statistically greater electrical activity in both cortical regions for participants engaged in the task than for participants in the control condition. The implications of the findings for building a fully elaborated sequential process model of cognitive representations in interpersonal contexts, among other theoretical endeavors, are discussed.

Social Empathy

2014 ◽  
Author(s):  
Elizabeth A. Segal
Keyword(s):  
Social Work ◽  
Brain Imaging ◽  
Cognitive Neuroscience ◽  
Policy Making ◽  
Brain Activity ◽  
Work Practice ◽  
Social Work Practice ◽  
Neuroscience Research ◽  
Social Empathy ◽  
The Relationship

This article defines and explains the concept and trait of social empathy and the relationship to interpersonal empathy. Both concepts are explained using the latest cognitive neuroscience research on brain activity. Through brain imaging, the components that together make up the full array of empathy have been identified and are discussed in relation to social work practice. The application of social empathy in the policy-making arena is described, and the implications for social work practice to enhance empathy are discussed.

scholarly journals Is there a problem with methods skills in cognitive neuroscience? Evidence from an online survey

2018 ◽  
Author(s):  
Olaf Hauk
Keyword(s):  
Data Analysis ◽  
Linear Algebra ◽  
Cognitive Neuroscience ◽  
Signal Analysis ◽  
Online Survey ◽  
Complex Data ◽  
Multiple Choice Questions ◽  
Neuroimaging Data ◽  
The Difference ◽  
Training Programmes

AbstractCognitive neuroscience increasingly relies on complex data analysis methods. Researchers in this field come from highly diverse scientific backgrounds, such as psychology, engineering and medicine. This poses challenges with respect to acquisition of appropriate scientific computing and data analysis skills, as well as communication among researchers with different knowledge and skills sets. Are researchers in cognitive neuroscience adequately equipped to address these challenges? Here, we present evidence from an online survey of methods skills. Respondents (n=305) mainly comprised students and post-doctoral researchers working in the cognitive neurosciences. Multiple choice questions addressed a variety of basic and fundamental aspects of neuroimaging data analysis, such as signal analysis, linear algebra, and statistics. We analysed performance with respect to the following factors: undergraduate degree (grouped into Psychology, Methods, Biology), current researcher status (undergraduate student, PhD student, post-doctoral researcher), gender, and self-rated expertise levels. Overall accuracy was 72%. Not surprisingly, the Methods group performed best (87%), followed by Biology (73%) and Psychology (66%). Accuracy increased from undergraduate (59%) to PhD (74%) level, but not from PhD to post-doctoral (74%) level. The difference in performance for the Methods versus non-methods (Psychology/Biology) groups was particularly striking for questions related to signal analysis and linear algebra, two areas especially relevant to neuroimaging research. Self-rated methods expertise was not strongly predictive of performance. The majority of respondents (93%) indicated they would like to receive at least some additional training on the topics covered in this survey. In conclusion, methods skills among junior researchers in cognitive neuroscience can be improved, researchers are aware of this, and there is strong demand for more skills-oriented training opportunities. We hope that this survey will provide an empirical basis for the development of bespoke skills-oriented training programmes in cognitive neuroscience institutions.

scholarly journals COINSTAC: Decentralizing the future of brain imaging analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1512 ◽  
Author(s):  
Jing Ming ◽  
Eric Verner ◽  
Anand Sarwate ◽  
Ross Kelly ◽  
Cory Reed ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Data Sharing ◽  
Large Scale ◽  
Data Transfer ◽  
Imaging Data ◽  
Sensitive Data ◽  
Large Scale Data ◽  
Decentralized Algorithms ◽  
Neuroimaging Data ◽  
Brain Imaging Data

In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.

Brain Imaging Revisited

2007 ◽  
Vol 13 (6) ◽  
pp. 1071-1072
Author(s):  
Cheryl L. Grady
Keyword(s):  
Brain Imaging ◽  
Cognitive Neuroscience ◽  
Rapid Growth ◽  
Functional Neuroimaging ◽  
Previous Edition ◽  
History Of ◽  
Cognitive Areas

Handbook of Functional Neuroimaging of Cognition, Second Edition. 2006. Roberto Cabeza and Alan Kingstone (Eds.), Cambridge, MA, The MIT Press, 480 pp., $65.00 (HB)The first edition of the Handbook of Functional Neuroimaging of Cognition, edited by Roberto Cabeza and Alan Kingstone, was a welcome addition to the cognitive neuroscience field when it was published in 2001. There were chapters on the history of neuroimaging and analysis, and all the major cognitive areas that had been studied at the time, written by senior people in their respective areas. That a second edition has appeared so soon after the first is a testament to the rapid growth of the cognitive neuroscience field, which is both gratifying and somewhat daunting to those of us who vainly attempt to keep up with this burgeoning literature. The same authors as in the previous edition write some chapters, but many have been penned by different authors, equally well known in the field, which is also a sign that the field is healthy and growing.

Neuroscience and Meditation: Help or Hindrance?

2021 ◽  
Author(s):  
Ron Purser ◽  
David J. Lewis
Keyword(s):  
Molecular Biology ◽  
Brain Imaging ◽  
Cognitive Neuroscience ◽  
Brain Activity ◽  
Scientific Study ◽  
Self Concept ◽  
Meditation Practice ◽  
Mental Life ◽  
Post Modernism ◽  
Style Of Thought

In recent years a style of thought has emerged that privileges molecular biology, in the form of cognitive neuroscience, as the preferred or even only valid foundation for the scientific study of mind and mental life. Despite the lack of progress and honest positive prognosis, neuroscience has managed to create a false but pervasive sense of achievement and meaning that dominates debate not only in scientific circles, but also in the popular domain. This chapter examines how this has happened and spells out the limitations of this approach. It analyzes how neuroscience communications, including popular fMRI brain imaging, function as persuasive discursive formations giving rise to a popular conception that mind is simply a function of brain activity. The implications for meditation practice are considered using the example of Madhyamaka Buddhism. This analysis makes use of concepts developed in post-modernism, especially in the thought of Michel Foucault. Post-modernism has some parallels with and differences from Madhyamaka, and these are explored. It is arguable that the neuronal-self concept strengthens the sense of ultimate materiality of mind and self and thereby impedes meditative realization of emptiness.

scholarly journals OpenNeuro: An open resource for sharing of neuroimaging data

2021 ◽  
Author(s):  
Christopher J Markiewicz ◽  
Krzysztof Jacek Gorgolewski ◽  
Franklin Feingold ◽  
Ross Blair ◽  
Yaroslav O Halchenko ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Open Science ◽  
Imaging Data ◽  
Data Types ◽  
Public Investments ◽  
Neuroimaging Data ◽  
Share Data ◽  
Brain Imaging Data ◽  
Multiple Species ◽  
The Impact

The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure (BIDS) standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 500 datasets including data from more than 18,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.

scholarly journals BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure

2022 ◽  
Vol 15 ◽  
Author(s):  
Marcel Peter Zwiers ◽  
Stefano Moia ◽  
Robert Oostenveld
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Data Set ◽  
Source Data ◽  
Neuroimaging Data ◽  
Programming Skills ◽  
Brain Imaging Data ◽  
Set Up ◽  
User Friendly

Analyses of brain function and anatomy using shared neuroimaging data is an important development, and have acquired the potential to be scaled up with the specification of a new Brain Imaging Data Structure (BIDS) standard. To date, a variety of software tools help researchers in converting their source data to BIDS but often require programming skills or are tailored to specific institutes, data sets, or data formats. In this paper, we introduce BIDScoin, a cross-platform, flexible, and user-friendly converter that provides a graphical user interface (GUI) to help users finding their way in BIDS standard. BIDScoin does not require programming skills to be set up and used and supports plugins to extend their functionality. In this paper, we show its design and demonstrate how it can be applied to a downloadable tutorial data set. BIDScoin is distributed as free and open-source software to foster the community-driven effort to promote and facilitate the use of BIDS standard.

Network Neuroscience: An Introduction to Graph Theory Network-Based Techniques for Music and Brain Imaging Research

2018 ◽  
pp. 121-144
Author(s):  
Robin W. Wilkins
Keyword(s):  
Graph Theory ◽  
Brain Imaging ◽  
Brain Connectivity ◽  
Musical Training ◽  
Functional Brain ◽  
Neuroimaging Data ◽  
Functional Brain Networks ◽  
Imaging Research ◽  
Main Components ◽  
Network Neuroscience

This chapter provides an introduction to network neuroscience techniques for music and brain imaging research. Key to this chapter is a background to the field of network science more broadly, as an approach to the study of complex systems, in addition to the more currently accepted graph theory techniques and applied analysis methods within network neuroscience. The focus of the chapter is on two main components. First, an introduction to network-based techniques that may be successfully applied to neuroimaging data for understanding structural and functional brain connectivity. Second, some of the more recent results and implications from the application of these techniques to fMRI data for advancing our understanding of the effects of music and musical training on structural and functional brain networks. Ultimately, the promising evidence resulting from the application of network-based techniques may help resolve fundamental questions surrounding the effects of music on the brain.

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