Although social neuroscience is concerned with understanding how the brain interacts with its social environment, prevailing research in the field has primarily considered the human brain in isolation, deprived of its rich social context. Emerging work in social neuroscience that leverages tools from network analysis has begun to advance knowledge of how the human brain influences and is influenced by the structures of its social environment. In this paper, we provide an overview of key theory and methods in network analysis (especially for social systems) as an introduction for social neuroscientists who are interested in relating individual cognition to the structures of an individual's social environments. We also highlight some exciting new work as examples of how to productively use these tools to investigate questions of relevance to social neuroscientists. We include tutorials to help with practical implementations of the concepts that we discuss. We conclude by highlighting a broad range of exciting research opportunities for social neuroscientists who are interested in using network analysis to study social systems.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8343567 | PMC |
http://dx.doi.org/10.1093/scan/nsaa069 | DOI Listing |
JMIR Form Res
January 2025
Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Background: Social media has become a widely used way for people to share opinions about health care and medical topics. Social media data can be leveraged to understand patient concerns and provide insight into why patients may turn to the internet instead of the health care system for health advice.
Objective: This study aimed to develop a method to investigate Reddit posts discussing health-related conditions.
JAMA Netw Open
January 2025
Department of Family Medicine, University of Michigan, Ann Arbor.
Importance: Cervical cancer screening is a crucial public health intervention, but screening disparities exist for women with physical disabilities (WWPD).
Objective: To explore the experiences of WWPD with both traditional speculum examination-based screening and at-home self-sampling for cervical cancer screening.
Design, Setting, And Participants: This qualitative study enrolled 56 WWPD to test self-sampling kits, provide feedback via a survey, and participate in a qualitative interview.
Sports Med
January 2025
Department of Nutrition, China Medical University, Taichung, Taiwan.
Background: Diastasis recti abdominis (DRA), commonly occurring in postpartum women, is not only an aesthetic issue but is also highly associated with functional impairments. Various conservative treatment modalities have been employed in clinical practice to alleviate DRA. However, the comparative efficacy of these non-surgical treatments for improving the inter-recti distance (IRD) remains to be determined.
View Article and Find Full Text PDFBioinformatics
January 2025
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning models primarily relied on primary or secondary protein structural and related properties, which have limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context around the target site.
View Article and Find Full Text PDFBioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Sichuan 611756, China.
Motivation: The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!