Network Mapping Research Articles

Brain Network Localization of Gray Matter Atrophy and Neurocognitive and Social Cognitive Dysfunction in Schizophrenia

BackgroundNumerous studies have established the presence of gray matter atrophy and brain activation abnormalities during neurocognitive and social cognitive tasks in schizophrenia. Despite a growing consensus that diseases localize better to distributed brain networks than individual anatomical regions, relatively few studies have examined brain network localization of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia. MethodsTo address this gap, we initially identified brain locations of structural and functional abnormalities in schizophrenia from 301 published neuroimaging studies with 8712 individuals with schizophrenia and 9275 healthy control participants. By applying novel functional connectivity network mapping to large-scale resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 brain abnormality networks of schizophrenia. ResultsThe gray matter atrophy network of schizophrenia comprised a broadly distributed set of brain areas predominantly implicating the ventral attention, somatomotor, and default networks. The neurocognitive dysfunction network was also composed of widespread brain areas primarily involving the frontoparietal and default networks. By contrast, the social cognitive dysfunction network consisted of circumscribed brain regions mainly implicating the default, subcortical, and visual networks. ConclusionsOur findings suggest shared and unique brain network substrates of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia, which may not only refine the understanding of disease neuropathology from a network perspective but may also contribute to more targeted and effective treatments for impairments in different cognitive domains in schizophrenia.

Leveraging PLOAM Messaging for Environmental Temperature Mapping in Aerial-deployed TDMA PON Networks

Leveraging PLOAM Messaging for Environmental Temperature Mapping in Aerial-deployed TDMA PON Networks

Evaluation and development of the clean water distribution network in Pancang Village, North Sebatik District, Nunukan Regency

Background: Rapid population growth and the large number of residential developments go hand in hand with the increasing need for clean water, especially drinking water. Basically, a city's development plan is closely related to the availability of clean water in the area, because clean water is a basic human need that is very important to fulfill. However, several consumers in certain villages which are part of the PDAM North Sebatik branch pipe network service area do not receive a clean water supply. With this research, it is hoped that it can provide alternatives/solutions in solving clean water problems, especially for the Pancang Village area, North Sebatik District. Methods: The data needed in this study is data on the population of Sei Pancang Village, North Sebatik District for the last 5 years, data on PDAM customers for the last 5 years, data on clean water needs, data on clean water sources and reservoir volume, as well as map data on the clean water distribution network. The evaluation technique used in this study uses the method with the largest correlation calculation, namely the arithmetic method. The clean water distribution network map will be evaluated using epanet software. Findings: The results of this research are that the population of Sei Pancang Village, North Sebatik District in 2022 will be 5250 people and projections for the number of residents and customers for the next 10 years show that the population will increase to 7018 people. Conclusion: Water use in the development plan year, namely 2023, in the residential area of Sei Pancang Village, North Sebatik District, includes, among other things, an average daily need of 9,045 lt/s, a maximum daily need of 10,402 lt/s, and a peak hour need of 13,568 lt/s. Meanwhile, in 2032 water demand will increase, with an average daily demand of 11,697 lt/second, a maximum daily demand of 13,451 lt/second, and a peak hour demand of 20,177 lt/second. Novelty/Originality of this article: This study develops a predictive model integrating demographic, climate, and infrastructure data to project water demand and optimize distribution. This model can be applied across regions to improve water management efficiency.

EXPRESS: Advancing employees’ mental health and psychological wellbeing research in hospitality and tourism: Systematic review, critical reflections, and future prospects

This study conducts a systematic review and analysis of the research landscape concerning the mental health and psychological wellbeing (MHPW) of employees in the hospitality and tourism sector. Comprehensive bibliometric analysis, intellectual network mapping, and critical qualitative content analysis were performed utilizing a dataset of 184 relevant articles. From these analyses, eight critical reflections were derived, and future research agenda were outlined. These agenda include exploring positive enhancers of MHPW, advocating for holistic conceptualizations of MHPW, encouraging cross-cultural and interdisciplinary collaborations, assessing the impacts of technology on mental health, developing industry-specific assessments, diversifying research methodologies with multidisciplinary theories, conducting multi-level analyses of influencing factors, and investigating the pandemic's long-term impact on career perceptions and mental health reforms within the industry. Findings offer pivotal directions for future research and aim to advance the understanding and enhancement of MHPW in the global hospitality and tourism workforce through impactful scholarship.

Transcriptomic analysis of different intramuscular fat contents on the flavor of the longissimus dorsi tissues from Guangling donkey

BackgroundIn this study, researchers aimed to explore the impact of intramuscular fat (IMF) concentration on the flavor of donkey meat, specifically in the longissimus dorsi muscle of Guangling donkeys. The internal volatile organic compounds that cause the flavor differences between donkey muscles are not clear at present. Transcriptomic technologies were utilized to analyze gene expression and its relationship to donkey meat flavor. MethodThirty Guangling donkeys had their IMF content evaluated in the longissimus dorsi muscle. Based on IMF content, 16 donkeys of similar ages were divided into two groups: low-fat (L) and high-fat (H). Headspace solid-phase microextraction Gas chromatography-mass spectrometry (HS-SPME-GC–MS) and headspace solid phase microextraction mass spectrometry were used to identify potential flavor components that differed between the two groups. ResultsFive key volatile substances were identified, and WGCNA and KEGG analysis was conducted to analyze the genes associated with these substances. The results showed that pathways like PPAR signaling, nucleotide excision repair, glucagon signaling, arachidonic acid metabolism, and glycolysis/glycogenesis were involved in lipid deposition. Additionally, a gene-gene interaction network map was constructed, highlighting the importance of hub genes such as EEF2, DDX49, GAP43, SNAP25, NDUFS8, MRPS11, RNASEH2A, POLR2E, POLR2C and ALB in regulating key flavor substances. ConclusionThis study provided valuable insights into the regulation of genes and protein expression related to flavor substances in donkey meat. It also deepened understanding of the influence of IMF on flavor and laid a foundation for future molecular breeding improvements in Guangling donkeys.

Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning

In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure.

Resilience assessment of airport aircraft area network operations under thunderstorm weather

Thunderstorms can cause a decrease in airports' dynamic capacity, and taxiway runway capacity is a key element of airport airfield operations. In this paper, based on the cell transmission model (CTM), the meteorological operation parameters of thunderstorms are set according to the airport aircraft taxiing rules, and a CTM is proposed for evaluating the resilience of the airport aircraft area operation network under thunderstorm weather. Based on the CTM, the network performance parameters are obtained, and the number of aircraft waiting for the network and the network operation efficiency are used as the basic performance indicators to establish a resilience index that reflects the dynamic change in the resilience of the thunderstorm meteorological interference network. Finally, this paper collects and analyzes the operation data of Tianjin Binhai International Airport in 2019 to build a CTM for aircraft operation in the flight area. The results show that the model's simulated airport departure operation data is almost consistent with the trend of the actual departure operation data, which verifies that the model can be used to simulate traffic for the airport airfield operation network. In addition, based on the resilience index, the sensitivity analysis of different parameters to network performance under thunderstorm weather was also studied. Based on the evaluation results of different parameter scenarios and indicators, we drew the resilience map of the airport ground operation network, and found that the network shows different resilience levels in different thunderstorm level scenarios. This study provides a great method and evaluation index for modeling and resilience evaluation of aircraft operation networks in the airfield, which is expected to improve airport operations, reduce flight delays, and improve service quality.

Conceptualizing the social networks of children of parents with serious mental illness: a thematic analysis.

Social networks, defined as the set of active and significant ties surrounding an individual, influence the wellbeing of vulnerable children. The best evidenced mechanism through which this occurs is where networks act as a vehicle to access social support. Little is known about the content and function of social networks of children of parents with severe and enduring mental illness (COPMI). COPMI are a frequently under-identified vulnerable child population at risk of negative outcomes. This qualitative study investigates the structure, role and function of these children's networks. Researchers conducted 17 semi-structured egocentric social network interviews. Interviews incorporated personal network mapping as a data collection method. COPMI were recruited through third sector organizations and interviewed across three sites in England. Data was analyzed using an inductive thematic analysis. Five network features were identified (i) parents as primary providers of support (i) limited networks and diminished connections over time (iii) substitutable ties (formal and informal) (iv) peer connections as source of both support and strain (v) coping strategies: self-censorship, avoidance and animals. Children of parents with severe and enduring mental illness networks are structurally typical of vulnerable children in that they are limited, rely on parents as primary ties but allow for some substitution of support ties. COPMI-specific features included peer relationships at times as source of strain and network level coping strategies used to manage wellbeing, including pets. This latter reflects previous findings in vulnerable adult populations so far unevidenced in children. Little evidence as to the mechanistic effect at work within networks was collected. However, COPMI were clearly shown to be engaged in active management and strategising in network navigation approaches, indicating the need to engage with children in this capacity, rather than approaching them as passive recipients of support. As such, effective network level interventions for this group are likely to prioritize access to beneficial substitute ties when support is limited. Additionally, interventions that promote network navigation skills and help foster productive coping strategies can capitalize on the child's active management role within their network.

Qualitative social network analysis in practice: comparison of methods for network maps construction

This article is devoted to the application of qualitative network analysis on the example of studying the social interactions of young parents in migration. A conceptual description of qualitative network analysis, network visualization features, design of qualitative network research, comparison of two approaches to building network maps and an example of their analysis are presented. Qualitative network analysis is aimed at studying relationships in the network and consists of interpretative and structural components, which are implemented through interviews and building a network map. The two approaches to building network maps differ in that in one case, network maps are built by the researcher based on an interview, and in the other case, the construction of the map is delegated to the informant himself. The first method is limited by the interview data and is their structural visualization. The second method of building a network map by the informant himself creates additional resources for interpretation – contrasting narratives and visual structuring of the informant's interactions.

Self-organizing maps for exploration and classification of nuclear magnetic resonance spectra for untargeted metabolomics of breast cancer

Metabolomics has emerged as a powerful tool for identifying biomarkers of disease, and nuclear magnetic resonance (NMR) spectroscopy allows for the simultaneous detection of a wide range of metabolites. However, due to complex interactions within metabolic networks, metabolites often exhibit high correlation and collinearity. To address this challenge, self-organizing maps (SOMs) of Kohonen maps and counter propagation-artificial neural networks (CP-ANN) were employed in this study to model proton nuclear magnetic resonance spectroscopic (1HNMR) data from control samples and breast cancer (BC) patients. Blood serum samples from a control group (n=24) and BC patients (n=18) were used to extract metabolites using methanol and chloroform solvents in optimum extraction conditions. The 1HNMR data was preprocessed by performing phase, baseline, and shift corrections. Subsequently, the preprocessed data was modeled using Kohonen network as an unsupervised technique and CP-ANN as a supervised technique. In this regard, the model built with CP-ANN successfully distinguished between the two classes with an accuracy of 100 % for both group and sensitivity of 96 % and 100 % for control group and BC patients, respectively. Additionally, CP-ANN algorithm demonstrated predictive capabilities by accurately classifying test samples with 90 % sensitivity, 98 % specificity, and 96 % accuracy for control group and 100 % sensitivity, 90 % specificity, and 96 % accuracy for BC patients. Furthermore, analysis of the resulting topological map revealed 14 significant variables (biomarkers) such as sarcosine, lysine, trehalose, tryptophan, and betaine that effectively differentiated between healthy individuals and BC patients.

Technology empowerment in disability employment: a bibliometric and systematic review

PurposeWe examined the role of technology in employment and work for people with disabilities (PWD). We intended to identify publication trends, key terms, research areas and potential gaps in the topic.Design/methodology/approachWe scrutinized relevant articles from two major databases through bibliometric and systematic review analyses. The bibliometric data involved 256 articles analysed through the VOSviewer software to produce publication trends and network maps. We then scanned article abstracts and selected 114 articles for thematic analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline.FindingsThe findings revealed a dominance of authors from Western and European countries, particularly the United States of America. Three themes emerged as the crucial factors shaping technology utilization for PWD employment and workplace inclusion. These were identified as individual, environmental and societal, which shed light on the multifaceted influences surrounding PWD’s technology empowerment.Originality/valueThe hybrid review approach enables a thorough examination of the published work. Our study proposes a conceptual model for PWD employment and workplace inclusion. Founded on the theories of Work Adjustment, Human–Computer Interaction and the Social Model of Disability. The model provides a clear visualization to guide further research and relevant initiatives in this area.

Studi Literatur Pemanfaatan Teknologi Chat GPT dalam Pendidikan

The aim of the research is to analyze the development of publications related to the use of GPT Chat technology in the field of education. The research method is a literature study with bibliometric analysis. A total of 919 articles were used from the Google Scholar database. The results of research publications on the use of GPT Chat technology in education have increased from year to year in the 2018-2022 period. The presentation of research development is known to be 2018, namely 1%, 2019 4%, 2020 17%, 2021 33%, and 2022 45%. There are 6 clusters namely cluster 1 (15 items), cluster 2 (14 items), cluster 3 (13 items), cluster 4 (12 items), cluster 5 (10 items), and cluster 6 (6 items). GPT Chat research network mapping in education topics that are often researched are GPT, System and Chatbot, while the topics that become new themes are Xlnet, Elmo, NLP, Deep Learning, and Dialog Response Generation, so that the keywords with the new theme can be used as a reference in do further research.

Bibliometric analysis of the top 100 most-cited articles concerning the use of propolis in dentistry

Aim: The objective of this bibliometric analysis was to identify and analyze the key characteristics of the 100 most-cited articles related to the use of propolis in dentistry. Methods: The search was conducted in the Web of Science Core Collection, and two independent reviewers selected the articles, excluding meeting articles. The number of citations for each article was compared across the Scopus and Google Scholar databases. The extracted data included the number and density of citations, year of publication, journal and impact factor, study design and theme, country and continent, institution, keywords, and authors. VOSviewer software was employed to generate collaborative network maps. Spearman correlation and Poisson regression analyses were performed on the data. Results: The number of citations ranged from 26 to 247, with a citation density varying between 1.08 and 20.00. Articles were published between 1991 and 2021, with laboratory studies (70%) and antimicrobial activity (39%) being the most prevalent study design and theme, respectively. The most discussed dental specialty was microbiology (49%). Rosalen PL (27%) emerged as the author with the highest number of articles, and the University of Campinas (Brazil) was the most prolific institution. Poisson regression indicated a declining trend in citations over the years, though literature reviews exhibited higher citation performance. Brazil contributed the highest percentage of articles (41%). Conclusion: In conclusion, the 100 most-cited articles predominantly comprised laboratory studies investigating the antimicrobial activity of propolis, primarily originating from Brazil, with notable emphasis on the University of Campinas.

Utilizing connectome fingerprinting functional MRI models for motor activity prediction in presurgical planning: A feasibility study.

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post-operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre- and intra-operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non-invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre-existing deficits. Connectome fingerprinting (CF) is a machine-learning approach that learns associations between resting-state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting-state data. Here we utilized CF to train models on high-quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting-state fMRI (rs-fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%-100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task-related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out-of-sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. PRACTITIONER POINTS: Precision motor network prediction using connectome fingerprinting. Carefully trained models' performance limited by stability of task-fMRI data. Successful cross-scanner predictions and motor network mapping in patients with tumor.

The mapping of social networks and computer technology in the star wars universe in 1977-2023: a historical retrospective

The mapping of social networks and computer technology in the star wars universe in 1977-2023: a historical retrospective

Retraction: A network map of cytoskeleton‐associated protein 4 (CKAP4) mediated signaling pathway in cancer

Retraction: A network map of cytoskeleton‐associated protein 4 (CKAP4) mediated signaling pathway in cancer

Emerging technologies and higher education libraries: a bibliometric analysis of the global literature

PurposeThis paper explores emerging technologies in higher education libraries indexed in the Web of Science core collection and Scopus for the periods between 1994 and 2024 (January 18, 2024).Design/methodology/approachData from Web of Science (WoS) and Scopus databases were collected and combined using RStudio software. Once this was completed using RStudio, the combined Scopus and Web of Science Excel file was uploaded into Biblioshiny, an interactive web interface. The data was then categorized to illustrate contributions from authors, journals, countries and academic institutions across the globe.FindingsResults and findings related to publication growth trends, annual growth, core journals, impact and productivity of authors, most cited documents, collaborations, network mapping and country contributions on emerging technologies and higher education libraries are presented.Research limitations/implicationsThis topic requires further exploration as academic librarians are fast becoming integrated into the teaching and learning agenda of institutions globally.Practical implicationsHigher education libraries and library schools to promote the teaching identity of academic librarians by helping them improve their pedagogical and digital skills for online teaching.Social implicationsLibrary and information science schools need to fast track the integration of emerging technologies into the curriculum. Higher education libraries should consider the training of staff in order to keep abreast of emerging technologies. Library management must frame their policies and strategies in order to promote the use of emerging technologies in higher education libraries.Originality/valueThe current study is novel in that it explores emerging technologies and higher education libraries using combined data from Scopus and Web of Science. The topic of emerging technologies and higher education libraries is still developing and therefore is in its infancy. The research is useful for researchers, library management and library schools in the higher education sector globally.

Integrating Proteomics and Metabolomics Approaches to Elucidate the Mechanism of Responses to Combined Stress in the Bell Pepper (Capsicum annuum).

Bell pepper plants are sensitive to environmental changes and are significantly affected by abiotic factors such as UV-B radiation and cold, which reduce their yield and production. Various approaches, including omics data integration, have been employed to understand the mechanisms by which this crop copes with abiotic stress. This study aimed to find metabolic changes in bell pepper stems caused by UV-B radiation and cold by integrating omic data. Proteome and metabolome profiles were generated using liquid chromatography coupled with mass spectrometry, and data integration was performed in the plant metabolic pathway database. The combined stress of UV-B and cold induced the accumulation of proteins related to photosynthesis, mitochondrial electron transport, and a response to a stimulus. Further, the production of flavonoids and their glycosides, as well as affecting carbon metabolism, tetrapyrrole, and scopolamine pathways, were identified. We have made the first metabolic regulatory network map showing how bell pepper stems respond to cold and UV-B stress. We did this by looking at changes in proteins and metabolites that help with respiration, photosynthesis, and the buildup of photoprotective and antioxidant compounds.

Precision mapping of the default network reveals common and distinct (inter) activity for autobiographical memory and theory of mind.

The default network is widely implicated as a common neural substrate for self-generated thought, such as remembering one's past (autobiographical memory) and imagining the thoughts and feelings of others (theory of mind). Findings that the default network comprises subnetworks of regions, some commonly and some distinctly involved across processes, suggest that one's own experiences inform their understanding of others. With the advent of precision functional MRI (fMRI) methods, however, it is unclear if this shared substrate is observed instead due to traditional group analysis methods. We investigated this possibility using a novel combination of methodological strategies. Twenty-three participants underwent multi-echo resting-state and task fMRI. We used their resting-state scans to conduct cortical parcellation sensitive to individual variation while preserving our ability to conduct group analysis. Using multivariate analyses, we assessed the functional activation and connectivity profiles of default network regions while participants engaged in autobiographical memory, theory of mind, or a sensorimotor control condition. Across the default network, we observed stronger activity associated with both autobiographical memory and theory of mind compared to the control condition. Nonetheless, we also observed that some regions showed preferential activity to either experimental condition, in line with past work. The connectivity results similarly indicated shared and distinct functional profiles. Our results support that autobiographical memory and theory of mind, two theoretically important and widely studied domains of social cognition, evoke common and distinct aspects of the default network even when ensuring high fidelity to individual-specific characteristics.NEW & NOTEWORTHY We used cutting-edge precision functional MRI (fMRI) methods such as multi-echo fMRI acquisition and denoising, a robust experimental paradigm, and individualized cortical parcellation across 23 participants to provide evidence that remembering one's past experiences and imagining the thoughts and feelings of others share a common neural substrate. Evidence from activation and connectivity analyses indicate overlapping and distinct functional profiles of these widely studied episodic and social processes.

P-197 Current status and hotspots of in vitro oocyte maturation: a bibliometric study of the past two decades

Abstract Study question What is the current research status and hotspot of in vitro maturation? And how could we improve the efficiency of IVM technique? Summary answer Five hotspots in IVM field were found, including culturing system, supplementation, cooperation in the ovarian follicle, gene expression, and cryopreservation of oocytes. What is known already In vitro maturation (IVM) of oocytes is a promising technique among assisted reproductive technologies. Because ovarian stimulation is minimal or absent in an IVM cycle, IVM technique reduces the risk of ovarian hyperstimulation syndrome (OHSS) and captures the reproductive potential of immature oocytes. However, the developmental competence of IVM oocytes is generally lower than the oocytes matured in vivo. A recent study showed the cumulative live birth rate in IVM is still inferior to standard IVF. The protocol for IVM still needs to be improved in some details. Study design, size, duration The articles and reviews related to IVM in the Web of Science Core Collection (WoSCC) were retrieved on January 16th, 2023. Three bibliometric tools including VOSviewer 1.6.18, CiteSpace 6.1.R6, and Bibliometrix R package 4.1.0 were used to generate network maps and explore frontier knowledge and trends. Participants/materials, setting, methods The data was retrieved and downloaded from WoSCC using the search formula: TS = (“in vitro maturation” OR “IVM”) AND TS = (follic* OR oocyte*) NOT TS=“ivermectin”. The timespan was set from 2003 to 2022. The language was limited to English, and publication types to article or review. VOSviewer detected indicators, including country, institution, journal, and author. CiteSpace analyzed reference co-citation and identified emerging keywords. Bibliometrix explored trending topics in IVM field. Main results and the role of chance A total of 5133 publications about IVM were retrieved from WoSCC, including 4706 articles and 427 reviews. The number of publications in IVM has increased steadily. China and University of Adelaide were the most prolific country and institution in IVM. Theriogenology was the most published and cited journal. Thompson, Jeremy G was the most productive author in the field of IVM, and Eppig, JJ’s papers were cited most. Furthermore, five hotspots were summarized and introduced, including culturing system, supplementation, cooperation in the ovarian follicle, gene expression, and cryopreservation of oocytes. This study comprehensively and visually analyzed the hotspots and frontiers of IVM in the past two decades. In our conclusion, further study could concentrate on: (1) the mechanism of oocytes matured in vivo and in vitro, especially in energy metabolism and intercellular communications; (2) establishment of IVM culture system, including standardized biphasic IVM culturing system and standardized supplementation; (3) the genetic differences between oocytes matured in vivo and in vitro; (4) the mechanism of cryopreservation-inflicted damage and the solution. Limitations, reasons for caution Due to the limitations of the software, the data we used for bibliometric analysis was only retrieved from WoSCC. Also, while bibliometric analysis can help us understand a field visually, it cannot completely replace system retrieval. Wider implications of the findings In this review, we explored and analyzed the field of IVM from 2003 to 2022 comprehensively and visually. Possible trends and hotspots in future were predicted, which may pave the way for future study directions and developments. Trial registration number not applicable