Identifying the landscape of Alzheimer’s disease research with network and content analysis

Global biomarker trends in Alzheimer's research: a bibliometric analysis

Alzheimer's disease (AD) has no effective treatment, although antibody drugs targeting beta-amyloid, mainly aducanumab, have produced useful clinical results. Biomarkers can be used to determine drug regimens effectively and to monitor the effects of drugs. A concept in which biomarkers reflect disease states is emerging. Although several AD biomarker studies have been reported, measurement methods and target molecules are still being validated, and various biomarkers are being explored. This study analyzed trends in research on AD biomarkers using bibliometric methods, revealing an exponential increase in research reports in this field, with the US most active in research. Analysis of the 'Burst' biomarkers using CiteSpace revealed that networks centered on authors, rather than networks among countries, drive new research trends in this area.

A Bibliometric Review on Gut Microbiome and Alzheimer’s Disease Between 2012 and 2021

Research on the microbiome has drawn an increasing amount of attention over the past decade. Even more so for its association with disease. Neurodegenerative diseases, such as Alzheimer’s disease (AD) have been a subject of study for a long time with slow success in improving diagnostic accuracy or identifying a possibility for treatment. In this work, we analyze past and current research on microbiome and its positive impact on AD treatment and diagnosis. We present a bibliometric analysis from 2012 to 2021 with data retrieved on September 2, 2021, from the Scopus database. The query includes “Gut AND (Microbiota OR Microbiome) AND Alzheimer*” within the article title, abstract, and keywords for all kinds of documents in the database. Compared with 2016, the number of publications (NPs) on the subject doubled by 2017. Moreover, we observe an exponential growth through 2020, and with the data presented, it is almost certain that it will continue this trend and grow even further in the upcoming years. We identify key journals interested in the subject and discuss the articles with most citations, analyzing trends and topics for future research, such as the ability to diagnose the disease and complement the cognitive test with other clinical biomarkers. According to the test, mild cognitive impairment (MCI) is normally considered an initial stage for AD. This test, combined with the role of the gut microbiome in early stages of the disease, may improve the diagnostic accuracy. Based on our findings, there is emerging evidence that microbiota, perhaps more specifically gut microbiota, plays a key role in the pathogenesis of diseases, such as AD.

Analysis of E-mental health research: mapping the relationship between information technology and mental healthcare

BACKGROUND

E-mental healthcare is the convergence of digital technologies with mental health services. It has been developed to fill a gap in healthcare for people who need mental wellbeing support that may not otherwise receive psychological treatment. With an increasing number of e-mental healthcare and research, this study aimed to investigate the trends of an e-mental health research field that integrates interdisciplinary fields and to examine the information technologies is being used in mental healthcare. To achieve the research objectives, bibliometric analysis, information extraction, and network analysis were applied to analyze e-mental health research data.

METHODS

E-mental health research data were obtained from 3663 bibliographic records from the Web of Science (WoS) and 3172 full-text articles from PubMed Central (PMC). The text mining techniques used for this study included bibliometric analysis, information extraction, and visualization.

RESULTS

The e-mental health research topic trends primarily involved e-health care services and medical informatics research. The clusters of research comprised 16 clusters, which refer to mental sickness, e-health, diseases, information technology (IT), and self-management. The information extraction analysis revealed a triple relation with IT and biomedical domains. Betweenness centrality was used as a measure of network graph centrality, based on the shortest path to rank the important entities and triple relation; nodes with higher betweenness centrality had greater control over the network because more information passes through that node. The IT entity-relations of "mobile" had the highest score at 0.043466. The top pairs were related to depression, mobile health, and text message.

CONCLUSIONS

E-mental related publications were associated with various research fields, such as nursing, psychology, medical informatics, computer science, telecommunication, and healthcare innovation. We found that trends in e-mental health research are continually rising. These trends were related to the internet of things (IoT) and mobile applications (Apps), which were applied for mental healthcare services. Moreover, producing AI and machine learning for e-mental healthcare were being studied. This work supports the appropriate approaches and methods of e-mental health research that can help the researcher to identify important themes and choose the best fit with their own survey work.

Science maps for exploration, navigation, and reflection-A graphic approach to strategic thinking

The world of science is growing at an unprecedented speed with more and more scholarly papers produced each year. The scientific landscape is constantly changing as research specialties evolve, merge or become obsolete. It is difficult for researchers, research managers and the public alike to keep abreast with these changes and maintain a true and fair overview of the world of science. Such an overview is necessary to stimulate scientific progress, to maintain flexible and responsive research organizations, and to secure collaboration and knowledge exchange between different research specialties and the wider community. Although science mapping is applied to a wide range of scientific areas, examples of their practical use are sparse. This paper demonstrates how to use a topical, scientific reference maps to understand and navigate in dynamic research landscapes and how to utilize science maps to facilitate strategic thinking. In this study, the research domain of biology at Aarhus University serves as an example. All scientific papers authored by the current, permanent staff were extracted (6,830 in total). These papers were used to create a semantic cognitive map of the research field using a co-word analysis based on keywords and keyword phrases. A workflow was written in Python for easy and fast retrieval of information for topic maps (including tokens from keywords section and title) to generate intelligible research maps, and to visualize the distribution of topics (keywords), papers, journal categories, individual researchers and research groups on any scale. The resulting projections revealed new insights into the structure of the research community and made it possible to compare researchers or research groups to describe differences and similarities, to find scientific overlaps or gaps, and to understand how they relate and connect. Science mapping can be used for intended (top-down) as well as emergent (bottom-up) strategy development. The paper concludes that science maps provide alternative views of the intricate structures of science to supplement traditional bibliometric information. These insights may help strengthen strategic thinking and boost creativity and thus contribute to the progress of science.

The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis

BACKGROUND

The Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications.

OBJECTIVE

Despite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years.

METHODS

PubMed, ACM Digital Library, and the Nursing & Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

A total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6%), information retrieval (125/975, 12.8%), terminology study (90/975, 9.2%), ontology and modeling (80/975, 8.2%), medical subdomains (76/975, 7.8%), other language studies (53/975, 5.4%), artificial intelligence tools and applications (46/975, 4.7%), patient care (35/975, 3.6%), data mining and knowledge discovery (25/975, 2.6%), medical education (20/975, 2.1%), degree-related theses (13/975, 1.3%), digital library (5/975, 0.5%), and the UMLS itself (150/975, 15.4%), as well as the UMLS for other purposes (27/975, 2.8%).

CONCLUSIONS

The UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions.

Research on Emotion Recognition and Dementias: Foundations and Prospects

BACKGROUND

The study of emotion recognition could be crucial for detecting alterations in certain cognitive areas or as an early sign of neurological disorders.

OBJECTIVE

The main objective of the study is to characterize research development on emotion recognition, identifying the intellectual structure that supports this area of knowledge, and the main lines of research attracting investigators' interest.

METHODS

We identified publications on emotion recognition and dementia included in the Web of Science Core Collection, analyzing the scientific output and main disciplines involved in generating knowledge in the area. A co-citation analysis and an analysis of the bibliographic coupling between the retrieved documents elucidated the thematic orientations of the research and the reference works that constitute the foundation for development in the field.

RESULTS

A total of 345 documents, with 24,282 bibliographic references between them, were included. This is an emerging research area, attracting the interest of investigators in Neurosciences, Psychology, Clinical Neurology, and Psychiatry, among other disciplines. Four prominent topic areas were identified, linked to frontotemporal dementia, autism spectrum disorders, Alzheimer's disease, and Parkinson's and Huntington disease. Many recent papers focus on the detection of mild cognitive impairment.

CONCLUSION

Impaired emotion recognition may be a key sign facilitating the diagnosis and early treatment of different neurodegenerative diseases as well as for triggering the necessary provision of social and family support, explaining the growing research interest in this area.

Evolution of the Research Literature and the Scientific Community of Alzheimer's Disease from 1983-2017: A 35-Year Survey

This study surveys the development of Alzheimer's disease (AD) in the research literature, the scientific community, and the journals containing AD papers over a 35-year period. Research papers on AD published from 1983 to 2017 in journals indexed in the Web of Science were analyzed in seven five-year periods. The number of AD papers increased from 1,095 in 1983-1987 to 50,532 by 2013-2017 and in the same time period, the number of participating countries went from 27 to 152. The US was the most prolific country throughout, followed by several European countries, Canada, Australia, and Japan. Asian countries have emerged and by 2013-2017, China surpassed all but the US in productivity. Countries in Latin America and Africa have also contributed to AD research. Additionally, several new non-governmental institutions (e.g., ADNI, ADI) have emerged and now play a key role in the fight against AD. Likewise the AD scientific publishing universe evolved in various aspects: an increase in number of journals containing AD papers (227 journals in 1983-1987 to 3,257 in 2013-2017); appearance of several AD-focused journals, e.g., Alzheimer's & Dementia, Journal of Alzheimer's Disease; and the development of special issues dedicated to AD. Our paper complements the numerous extant papers on theoretical and clinical aspects of AD and provides a description of the research landscape of the countries and journals contributing papers related to AD.

Using Machine Reading to Understand Alzheimer’s and Related Diseases from the Literature

Purpose

This paper aims to better understand a large number of papers in the medical domain of Alzheimer’s disease (AD) and related diseases using the machine reading approach.

Design/methodology/approach

The study uses the topic modeling method to obtain an overview of the field, and employs open information extraction to further comprehend the field at a specific fact level.

Findings

Several topics within the AD research field are identified, such as the Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS), which can help answer the question of how AIDS/HIV and AD are very different yet related diseases.

Research limitations

Some manual data cleaning could improve the study, such as removing incorrect facts found by open information extraction.

Practical implications

This study uses the literature to answer specific questions on a scientific domain, which can help domain experts find interesting and meaningful relations among entities in a similar manner, such as to discover relations between AD and AIDS/HIV.

Originality/value

Both the overview and specific information from the literature are obtained using two distinct methods in a complementary manner. This combination is novel because previous work has only focused on one of them, and thus provides a better way to understand an important scientific field using data-driven methods.

A path toward understanding neurodegeneration

A focus on cell biology may accelerate progress in disease prevention and cures

Mapping the landscape of cerebral amyloid angiopathy research: an informetric analysis perspective

To quantitatively analyse the research output and major trends in the field of cerebral amyloid angiopathy (CAA) over six decades, from 1954 to 2014, using advanced informetrics methods, we systematically identified CAA-related articles from PubMed, collected metadata and performed productivity analysis, copublication analysis, and network and content analysis over defined time periods. Linear regression was used to investigate these relationships. Changes in CAA research themes (2000-2014) were defined using a topic modelling technique. A total of 2340 CAA papers were published between 1954 and 2014. The mean number (3.03; 95% CI 2.62 to 3.45; p<0.0001) and mean rate (0.13%; 95% CI 0.11% to 0.15%; p<0.0001) of CAA publications increased yearly. Analysis of copublication networks over 5-year periods from 1990 to 2014, revealed a great increase in the total number of connected investigators publishing on CAA (coefficient 16.74; 95% CI 14 to 19.49; p<0.0001) as well as the interactions between them (coefficient 73.53; 95% CI 52.03 to 89.03; p<0.0001). Further analysis of the network characteristics showed that in the past 15 years, copublication networks became not only larger, but also more connected and coherent. Content analysis identified 16 major CAA research themes and their differential evolution in the past 15 years, with the following main trends: (A) limited focus on vascular cognitive impairment; (B) a shift in emphasis towards neuroimaging, cerebral microbleeds and diagnostic aspects and away from pathological aspects; and (3) a reduced emphasis on basic biology apart from an increased focus on mouse models and perivascular drainage. Our study reveals the rapidly developing nature of the CAA research landscape, providing a novel quantitative and objective basis for identifying unmet needs and new directions. Our findings support the idea of a collaborative culture in the field, encouraging international research initiatives.