In order to reach a more detailed level of understanding of the literature review of the Neuroinformatics bibliometric material, we develop a graphical mapping of this data using the VOSviewer software (Van Eck and Waltman 2010). For more information regarding the VOSviewer and how to use it, see Van Eck and Waltman (2023), and the webpage of the software: https://www.vosviewer.com.

First, we analyse co-citation of cited journals in Neuroinformatics, which we find when two published articles receive a citation in a third paper published in Neuroinformatics (Small 1973). Figure 4 presents a cocitation network of journals in Neuroinformatics, constructed using VOSviewer with a minimum citation threshold of 20 and 100 links between the journals. This visualization highlights the interconnectedness of various scientific publications cited by and frequently co-cited alongside Neuroinformatics.

One of the key observations from the network is the central role of Neuroimage, which appears as the most prominent node. This indicates its dominant position in neuroimaging and computational neuroscience research. Neuroimage exhibits strong co-citation links with a variety of other journals, reflecting its broad influence across medical imaging, brain mapping, and related neuroscience disciplines.

In contrast, Neuroinformatics holds a significant, though somewhat smaller, position in the network. It is connected to important journals like Frontiers in Neuroinformatics, PLOS One, and Scientific Reports, underscoring its relevance in computational biology, bioinformatics, and data analysis. The proximity of these journals suggests a close relationship between Neuroinformatics and fields that leverage computational tools for neuroscience research.

The network reveals distinct clusters of related fields. The green cluster, centred around Neuroimage, focuses on journals in neuroimaging and medical imaging, such as Magnetic Resonance Imaging and Human Brain Mapping. Meanwhile, the red cluster highlights core neuroscience journals like Journal of Neuroscience, Nature Neuroscience, and Neuron, indicating the fundamental role of these publications in the development of neuroscience. A blue cluster surrounding Neuroinformatics includes journals related to computational neuroscience and bioinformatics, showcasing the journal’s contributions to bridging neuroscience with computational methods.

Prominent general science and neuroscience journals, such as Nature, Science, and Proceedings of the National Academy of Sciences (PNAS), also have strong co-citation ties with Neuroinformatics. This emphasizes the journal’s integration into high-impact, cutting-edge research across multiple disciplines.

Overall, Figure 5 demonstrates the interdisciplinary reach of Neuroinformatics. Its close ties with journals in computer science, engineering, and bioinformatics—like IEEE Transactions on Medical Imaging and PLOS Computational Biology—reflect the journal’s pivotal role in advancing computational approaches in brain science. The cocitation patterns underscore how Neuroinformatics acts as a bridge between neuroscience, computational tools, and data-driven methodologies, contributing significantly to the field’s growth and development.

Co-citation of journals in Neuroinformatics: minimum citation threshold of 20 and 100 links

Figure 6 illustrates the co-citation network of journals related to Neuroinformatics articles from North America, constructed using VOSviewer with a minimum citation threshold of 20 and 100 links. This network visualizes the relationships between journals frequently co-cited alongside Neuroinformatics, highlighting the field's interdisciplinary nature and collaborative ties.

One of the most prominent features of the network is the dominance of Neuroimage, which is the largest and most central node. Neuroimage plays a pivotal role in the neuroimaging and neuroscience communities, evidenced by its strong co-citation links with numerous journals. These links include key publications like Human Brain Mapping, Magnetic Resonance in Medicine, and Radiology, all of which emphasize the central importance of imaging research in neuroscience and its frequent collaboration with other medical imaging fields.

Neuroinformatics also holds a significant position within the network, though it is smaller in comparison to Neuroimage. It is tightly linked to journals such as PLOS One, Frontiers in Neuroinformatics, Bioinformatics, and Scientific Reports, demonstrating its role in the computational and bioinformatics domains. These connections suggest that Neuroinformatics plays a crucial role in the development of computational tools, data analysis, and bioinformatics in neuroscience. 

The network is composed of distinct clusters that reveal different areas of focus. The red cluster around Neuroimage is heavily centred on neuroimaging, featuring journals like Human Brain Mapping and Magnetic Resonance Imaging, while the green cluster represents foundational neuroscience journals such as Journal of Neuroscience, Nature Neuroscience, and Proceedings of the National Academy of Sciences (PNAS). The blue cluster focuses on computational and bioinformatics journals, with Neuroinformatics linking to Bioinformatics, IEEE Transactions on Medical Imaging, and other computational science journals, highlighting its interdisciplinary nature.

Additionally, journals like Nature, Science, and PNAS show strong connections within the network, suggesting that Neuroinformatics and related journals are frequently co-cited with high-impact, general science publications. This underscores the journal’s broad interdisciplinary reach, bridging both fundamental neuroscience research and applied computational methods.

The network also reflects a strong connection between computational and clinical fields. Journals like PLOS Computational Biology and IEEE Transactions on Medical Imaging are closely linked to clinical publications such as Radiology and Neurology, illustrating the dynamic relationship between computational advancements and their practical applications in clinical neuroscience and medical imaging.

In conclusion, Figure 6 highlights the broad and interdisciplinary influence of Neuroinformatics within the North American research community. Neuroimage emerges as a central player in neuroimaging, while Neuroinformatics bridges the computational and bioinformatics fields with neuroscience. The co-citation patterns reveal the journal’s significant role in advancing both computational tools and clinical applications, emphasizing its importance in a wide array of scientific fields.

Co-citation of journals in Neuroinformatics: North America (minimum citation threshold of 20 and 100 links)

Figure 7 provides a visualization of the co-citation network among journals involved in Neuroinformatics research across Europe, generated using VOSviewer with a minimum citation threshold of 20 and 100 links. This network illustrates the relationships between journals frequently co-cited alongside Neuroinformatics, offering insight into the interdisciplinary nature of the field within European research.

One of the most prominent observations is the centrality of Neuroimage, which once again stands out as the most dominant node in the network. Neuroimage plays a crucial role in neuroimaging and neuroscience research, demonstrating strong co-citation links with journals such as Human Brain Mapping, Magnetic Resonance Imaging, and IEEE Transactions on Medical Imaging. These connections emphasize the vital role of imaging and radiology in European neuroscience, with Neuroimage acting as a bridge between medical, radiological, and computational journals.

Neuroinformatics occupies a smaller yet significant role in the network, particularly in the computational and bioinformatics domains. Journals such as Frontiers in Neuroinformatics, PLOS One, and Bioinformatics are closely linked to Neuroinformatics, indicating its importance in computational neuroscience and interdisciplinary collaboration within Europe. These connections underscore the growing reliance on data analysis, bioinformatics, and computational tools in neuroscience research.

The network reveals several distinct clusters of journals. The green cluster centres around Neuroimage and focuses primarily on neuroimaging, featuring journals like Magnetic Resonance Imaging and IEEE Transactions on Medical Imaging, which highlight the integration of advanced imaging techniques into neuroscience research. The red cluster includes foundational neuroscience journals such as Journal of Neuroscience, Nature Neuroscience, Neuron, and PLOS Computational Biology, which are essential for advancing basic neuroscience knowledge. The blue cluster comprises computational and informaticsfocused journals like Bioinformatics and Lecture Notes in Computer Science, reflecting the significance of computational methodologies in European Neuroinformatics research.

The interdisciplinary nature of the field is further emphasized by the presence of high-impact general science journals such as Nature, Science, and Proceedings of the National Academy of Sciences (PNAS). These journals frequently appear alongside core neuroscience and computational publications, indicating that Neuroinformatics research bridges the gap between fundamental neuroscience and applied computational innovations.

Additionally, the network reveals strong connections between clinical journals, such as Neurology and Stroke, and computational publications like PLOS Computational Biology. This suggests a dynamic relationship between computational advancements and their applications in clinical neuroscience within European research, demonstrating how computational tools are directly impacting clinical practices.

Co-citation of journals in Neuroinformatics: Europe (minimum citation threshold of 20 and 100 links)

Figure 8 displays the co-citation network of journals related to Neuroinformatics research from the "Rest of the World" (excluding Europe and North America), created using VOSviewer with a minimum citation threshold of 20 and 100 links. This network visualizes how journals frequently co-cited with Neuroinformatics connect, offering a glimpse into the interdisciplinary collaborations and research contributions from regions outside the traditional Western academic hubs.

Neuroimage emerges as the most prominent and central node, reflecting its influential role in global neuroimaging research. Its extensive cocitation links with journals like Human Brain Mapping, Magnetic Resonance Imaging, and IEEE Transactions on Medical Imaging emphasize its strong association with both neuroimaging and related medical technologies. Serving as a major hub, Neuroimage connects various domains within neuroimaging, medical imaging, and radiology.

Neuroinformatics holds a key position within the network, particularly through its connections to journals that specialize in computational neuroscience and bioinformatics. Journals such as Frontiers in Neuroinformatics, Bioinformatics, PLOS Computational Biology, and PLOS One are closely linked, illustrating Neuroinformatics’ integral role in advancing computational tools, data analysis methods, and bioinformatics approaches in neuroscience research on a global scale.

Distinct clusters appear in the network, each highlighting different areas of focus. The green cluster revolves around Neuroimage and includes journals like Human Brain Mapping and Neurology, primarily centred on neuroimaging and radiology. Meanwhile, the red cluster focuses on foundational neuroscience and computational journals, including Journal of Neuroscience, Neuron, Nature Neuroscience, and PLOS Computational Biology, showcasing the interplay between computational methods and core neuroscience research. The blue cluster highlights the connection between computational technologies and neuroscience, featuring journals like IEEE Transactions on Medical Imaging, Medical Image Analysis, and Lecture Notes in Computer Science.

The interdisciplinary reach of Neuroinformatics in this region is further demonstrated by the strong co-citation links between high-impact, general science journals such as Nature, Science, and Proceedings of the National Academy of Sciences (PNAS) and core neuroscience and computational publications. This suggests that computational neuroscience extends beyond traditional neuroscience into broader scientific and technological fields.

Moreover, there are significant co-citation relationships between clinical journals like Neurology and Radiology and computational journals such as Bioinformatics and PLOS Computational Biology. This highlights a close connection between computational advances and their practical applications in clinical neuroscience and medical practices across regions outside of Europe and North America.

Co-citation of journals in Neuroinformatics: Rest of the World (minimum citation threshold of 20 and 100 links)

Table 12 provides a detailed global and temporal analysis of the cocitation trends within Neuroinformatics, ranking the most frequently cited journals across four time periods: the overall global analysis, 2019–2023, 2014–2018, and 2003–2013. This breakdown offers insights into the evolving influence of key journals within the field of computational neuroscience, neuroimaging, and related disciplines.

Across the entire period, Neuroimage consistently ranks first, with a total of 3,287 citations, underscoring its enduring importance in neuroimaging research. This reflects Neuroimage's central role in the development of imaging technologies and methods used in neuroscience. Neuroinformatics holds a strong second position globally, with 880 citations, affirming its pivotal role in computational neuroscience and the development of data-driven approaches. Other significant journals include IEEE Transactions on Medical Imaging (546 citations) and Human Brain Mapping (538 citations), highlighting the continuous relevance of imaging and brain mapping technologies across the global research landscape. Foundational journals like Journal of Neuroscience (537 citations) and Proceedings of the National Academy of Sciences (PNAS) (472 citations) demonstrate their broad influence in neuroscience research.

In the most recent period (2019–2023), Neuroimage remains the dominant journal, with 1,467 citations, continuing its trend of high influence in neuroimaging. Neuroinformatics retains its importance, accumulating 254 citations, while Human Brain Mapping (237 citations) and PLOS One (237 citations) also show robust citation counts, illustrating the growing trend towards interdisciplinary and open-access research. A notable shift is the rise of Frontiers in Neuroinformatics, which garnered 216 citations during this period, reflecting an increasing interest in interdisciplinary approaches to computational neuroscience. Emerging journals like Neuron (192 citations) and Scientific Reports (124 citations) also show significant citation activity, indicating their expanding influence in cutting-edge neuroscience research.

During 2014–2018, Neuroimage continued to dominate with 931 citations, demonstrating its sustained leadership in neuroimaging studies. Neuroinformatics remained a key journal with 271 citations, reflecting its continued role in advancing computational methodologies. IEEE Transactions on Medical Imaging (187 citations) and Lecture Notes in Computer Science (158 citations) highlight the growing integration of computational techniques into neuroscience. This period also saw the increased influence of PLOS One (150 citations) and the emergence of Med Image Analysis (82 citations) as critical journals for researchers focusing on image processing in neuroscience.

In the earliest period of analysis (2003–2013), Neuroimage had 744 citations, affirming its foundational role in shaping the neuroimaging field. Neuroinformatics played a critical role in establishing computational approaches in neuroscience with 339 citations during this time. Traditional neuroscience journals like Journal of Neuroscience (218 citations) and PNAS (185 citations) were also highly influential, contributing to the theoretical and methodological advancements in early Neuroinformatics research. Key neuroscience journals like Cerebral Cortex (139 citations) and Neuron (112 citations) were instrumental in driving interdisciplinary research between computational tools and core neuroscience topics. 

Across all periods, Neuroimage and Neuroinformatics maintain their status as the leading journals, reflecting the essential role of neuroimaging and computational approaches in the broader field of neuroscience. Recent periods (2019–2023) have shown a growing emphasis on interdisciplinary collaboration, as indicated by the rising influence of open-access journals like PLOS One and the increased citations for Frontiers in Neuroinformatics. In earlier periods (2003–2013), foundational neuroscience journals like Journal of Neuroscience and PNAS played a key role in shaping early Neuroinformatics research, highlighting the enduring impact of these traditional journals in the field.

The co-citation network of documents in Neuroinformatics shown in Figure 9 was created using VOSviewer with a minimum citation threshold of 10 and 100 links between documents. This network highlights how frequently key papers are co-cited in Neuroinformatics, offering insights into the most influential works and their thematic connections within the field.

One prominent observation is the dominance of papers focused on methodological advancements, particularly in neuroimaging and computational neuroscience. Papers authored by Smith et al. (2002), Fischl et al. (2002) and Fischl (2012), which discuss widely-used neuroimaging tools such as FSL and FreeSurfer, are central in this network. These methodological papers are foundational to the development of imaging processing in neuroscience and form strong cocitation connections with other important works related to neuroimaging data analysis.

The network reveals several distinct clusters representing different research themes. The red cluster is heavily centred around neuroimaging methodologies, featuring key papers by Smith, Fischl, and Ashburner. These works are highly influential in the field, particularly for their contributions to the development of imaging software and analysis platforms like FreeSurfer. The blue cluster includes documents related to statistical and computational methods, such as Otsu (1979) on image processing techniques and Tibshirani (1996) on the Lasso regression method. These computational techniques have been widely applied in neuroimaging research and data analysis, contributing to their frequent co-citation with imaging-focused papers.

Another important area within the network is represented by the green cluster, which focuses on brain connectivity, neuron tracing, and bioinformatics tools. Influential papers in this group include works by Peng et al. (2010, 2015) on neuron tracing and brain connectivity. These contributions highlight the growing integration of computational tools into research on neural networks and connectivity, bridging the gap between traditional neurobiology and advanced data-driven analysis techniques.

The network underscores the interconnectedness of neuroimaging research, with strong links between papers on imaging tools and software. For example, documents by Jenkinson et al. (2012) and Cox (1996) are frequently co-cited, reflecting their central roles in developing neuroimaging processing pipelines. These co-citation patterns indicate that imaging software, such as FSL and AFNI, is integral to neuroscience research, facilitating large-scale data analysis and processing.

In addition, several key papers within the green and yellow clusters focus on bioinformatics and Neuroinformatics platforms. Papers by Gardner et al. (2008) and Bowden and Dubach (2003) explore Neuroinformatics data platforms, highlighting the importance of data-sharing systems in neuroscience research. These connections demonstrate the interdisciplinary nature of the field, where computational tools and bioinformatics resources are essential for advancing neuroscience discoveries.

Co-citation of documents in Neuroinformatics: minimum citation threshold of 10 and 100 links

The co-citation network of key authors in Neuroinformatics is depicted in Figure 10, generated using VOSviewer with a minimum citation threshold of 15 and 100 links. This network shows how often certain authors are cited together, offering insights into major contributors and thematic areas within the field.

Several clusters emerge, each representing different areas of research. The green cluster is dominated by authors like Smith, Fischl, and Jenkinson, who are well-known for their work in neuroimaging and the development of tools such as FSL and FreeSurfer. These authors are frequently co-cited due to their contributions to the core methodologies used in neuroimaging analysis.

The yellow cluster features authors like Sporns and Bullmore, who are prominent in the study of brain connectivity. Their research focuses on mapping brain networks and understanding how different regions interact, making their work foundational in the field of network neuroscience.

In the blue cluster, authors such as Peng and Ascoli are key figures. Their work is centred around neuron tracing and the development of computational tools for analysing brain structures. These contributions have advanced the field of computational neuroanatomy, and they are frequently co-cited in studies focused on neural data processing.

The red cluster includes Friston, who has made significant contributions to statistical modeling in neuroimaging, particularly in the analysis of fMRI data. His work is widely cited, reflecting its importance in the development of statistical approaches to understanding brain function.

Co-citation of authors in Neuroinformatics: minimum citation threshold of 15 and 100 links

The bibliographic coupling network of documents in Neuroinformatics is depicted in Figure 11, using a minimum threshold of 20 citations and 100 links. This network reveals how research papers are connected through shared references, providing a detailed view of thematic connections and the influence of various publications within the field. 

A standout feature in the network is the dominant position of Yan (2016), which is highly connected to other papers across multiple research areas. This indicates its significant influence, particularly in advancing research on brain imaging and data processing. The central role of Yan (2016) in the network highlights its broad application and its foundational contributions to the ongoing development of Neuroinformatics.

The network also identifies several distinct research clusters. In the green cluster, key works by Laird (2005) focus on neuroimaging techniques such as brain mapping and imaging analysis. These papers contribute significantly to the refinement of imaging methodologies and are frequently linked through shared references. Meanwhile, the orange cluster, featuring documents like Mwangi (2014) and Hanke (2009), centres on advanced neuroimaging techniques, especially the use of multivariate pattern analysis in fMRI data, further enriching the field of neuroimaging.

In the blue and purple clusters, we see a focus on brain connectivity and data sharing, with influential contributions from Sporns (2004) and Kötter (2004). Their work on mapping the brain's structural and functional networks has had a profound impact on the field, reflected in the strong coupling between their research and other studies in Neuroinformatics platforms.

The red cluster emphasizes computational neuroscience, with papers like Wang (2011) and Chothani (2011) driving advancements in machine learning and data analysis for brain data. These works highlight the growing trend of integrating computational methods to analyse largescale neural datasets.

Bibliographic coupling of documents published in Neuroinformatics: minimum threshold of 20 citations and 100 links

Figure 12 provides a bibliographic coupling network of authors publishing in Neuroinformatics, based on a minimum threshold of 3 documents and 100 links. This visualization showcases how authors are connected through shared references in their published works, offering valuable insights into collaboration patterns and research focus areas within the field.

A number of central figures stand out in the network, including Vince D. Calhoun, Bennett A. Landman, and Dinggang Shen. These authors are prominent in the areas of neuroimaging and computational neuroscience, with extensive connections to other researchers in the field. Their strong presence in the network reflects their critical role in advancing imaging techniques and brain mapping tools, and their wide-ranging collaborations indicate their influence in shaping research directions in these areas.

The network also reveals distinct clusters of collaboration among various groups of researchers. One notable cluster includes Calhoun and Landman, who are linked with other influential figures like Randy Gollub and Tonya White. This suggests a concentrated focus on neuroimaging and brain connectivity research within this group. Another important cluster centres around Giorgio A. Ascoli, who collaborates closely with Hanchuan Peng and other researchers, reflecting their work on neuron tracing and computational neuroanatomy.

The timeline-based colour gradient in the network highlights emerging trends in the field. Authors such as Dinggang Shen and Daoqiang Zhang appear in more recent publications, indicating their involvement in cutting-edge advancements in neuroimaging and computational neuroscience. Similarly, Hanchuan Peng has been active in recent years, contributing significantly to the development of neuron tracing tools and bioinformatics platforms, reflecting the evolving landscape of Neuroinformatics research.

Furthermore, interdisciplinary collaborations are evident in the network, showcasing how researchers from different fields come together to address complex challenges in neuroscience. For instance, authors like Jeffrey S. Grethe, Maryann E. Martone, and Gordon M. Shepherd are part of a cluster that bridges Neuroinformatics with data-sharing platforms. Their work underscores the importance of interdisciplinary efforts in advancing computational methods and fostering innovation in neuroscience.

Bibliographic coupling of authors publishing in Neuroinformatics: minimum threshold of 3 documents and 100 links

Figure 13 presents the bibliographic coupling network of institutions publishing in Neuroinformatics, with a minimum publication threshold of 3 documents and 100 links. The network illustrates how various institutions are connected based on shared references in their research publications, shedding light on collaboration patterns and the influence of different institutions within the field.

Several key institutions emerge as central figures in the network, notably University of California San Diego, Massachusetts General Hospital, Chinese Academy of Sciences, and Harvard University. These institutions are heavily involved in Neuroinformatics research, and their extensive connections to other organizations underscore their significant role in shaping the field through collaborative research and shared thematic interests.

The network also reveals distinct clusters of collaboration between institutions. For example, University of California San Diego, Yale University, and Stanford University form a strong collaborative group, which appears to focus on advanced neuroimaging and computational neuroscience. Another notable cluster includes Harvard University, Cornell University, and University of Arkansas Medical Sciences, suggesting shared research efforts, likely centred on computational techniques and medical applications of Neuroinformatics. 

International collaborations are another key feature of the network, with European institutions like University College London (UCL), Université Paris-Saclay, and University of Barcelona closely linked to U.S. institutions such as Johns Hopkins University and University of North Carolina. This global interconnectedness highlights the international scope of Neuroinformatics research. Additionally, Asian institutions like Chinese Academy of Sciences and Korea University are well integrated into this collaborative network, indicating the growing involvement of these regions in cutting-edge computational neuroscience.

The colour gradient in the network reflects the timeline of research activity, with more recent collaborations and research contributions coming from institutions like McGill University, University Carlos III Madrid, and Shanghai Jiao Tong University. These institutions are at the forefront of recent advancements in Neuroinformatics, particularly in areas like data processing, brain mapping, and neuroimaging techniques.

Bibliographic coupling of institutions publishing in Neuroinformatics: minimum publication threshold of 3 documents and 100 links

The bibliographic coupling network of countries involved in Neuroinformatics research is shown in Figure 14, using a minimum threshold of 1 document and 100 links. This visualization highlights how countries are connected through shared references in their publications, offering insights into the global research landscape and collaborations within the field of Neuroinformatics.

At the centre of the network, the USA and People’s Republic of China stand out as major hubs. These two countries have the most connections to other nations, underscoring their dominant roles in shaping global research efforts in Neuroinformatics. The USA, in particular, is highly interconnected, forming collaborative links with nearly every country in the network, highlighting its leadership in fostering international research collaborations.

European countries like England, Netherlands, Spain, France, and Italy also feature prominently in the network, reflecting their active participation in Neuroinformatics research. These nations show strong cross-border collaborations with each other as well as with the USA and China, making Europe a key player in global research efforts. In Asia, South Korea, Japan, and Singapore emerge as important contributors, demonstrating the growing role of East Asia in computational neuroscience and Neuroinformatics. 

The network also reveals emerging collaborations from countries like India, Australia, and Brazil. These nations show increased research activity in recent years, as indicated by the colour gradient in the visualization, reflecting their growing involvement in global Neuroinformatics research. Their connections to well-established research hubs suggest a rising influence in the field.

Though North America, Europe, and East Asia dominate the network,other regions are also beginning to contribute more significantly. Countries such as Mexico, South Africa, Argentina, and Turkey are part of the broader research landscape, indicating the expanding geographic reach of Neuroinformatics research. While they may not be as central as the leading countries, their inclusion in the network demonstrates the growing international diversity in the field.

Bibliographic coupling of countries publishing in Neuroinformatics: minimum publication threshold of 1 document and 100 links

The co-occurrence network of author keywords in Neuroinformatics is depicted in Figure 15, using a minimum occurrence threshold of 3 and 100 links. This visualization illustrates the most frequently used keywords in research articles and their interconnections, providing valuable insights into the main research themes and emerging trends within the field.

At the core of the network is neuroimaging, which is closely associated with other significant topics such as fMRI, machine learning, and magnetic resonance imaging (MRI). This central position reflects the prominence of neuroimaging, particularly MRI and fMRI (Filippi, 2025), in Neuroinformatics research, with machine learning playing a pivotal role in processing and analysing neuroimaging data.

A notable trend in the network is the increasing integration of deep learning and image segmentation techniques, particularly in the context of Alzheimer’s disease and brain MRI. This highlights the growing use of advanced machine learning methods to analyse complex brain data, with a strong focus on applying these technologies to study neurodegenerative disorders.

The terms functional connectivity and brain networks also appear prominently, emphasizing research aimed at understanding the interactions between different regions of the brain. This focus is further supported by related terms such as tractography and diffusion MRI, indicating a strong interest in mapping brain connectivity using advanced imaging techniques. Additionally, keywords such as data sharing, informatics, and database point to the increasing importance of open data practices and the use of databases in Neuroinformatics. These terms underscore the collaborative nature of the field, particularly in large-scale brain mapping and neuroimaging studies.

Another important research area involves neuron morphology, 3D neuron reconstruction, and neuron tracing, which are associated with efforts to map the physical structure of neurons. These terms highlight the critical role of Neuroinformatics tools in anatomical neuroscience and neuron-level analysis. Other research areas, such as schizophrenia, epilepsy, and mild cognitive impairment, are also represented in the network, reflecting ongoing studies into neurological and psychiatric disorders. These terms are linked to keywords like functional connectivity and machine learning, indicating a computational approach to understanding these conditions.

Co-occurrence of author keywords in Neuroinformatics: minimum occurrence threshold of 3 and 100 links

The co-occurrence network of author keywords in Neuroinformatics research from North America is displayed in Figure 16, with a minimum,occurrence threshold of 2 and 100 links. This visualization highlights the most frequently used keywords in publications and how these terms are interconnected, providing insights into the main research themes and emerging trends in the region.

At the centre of the network, neuroimaging dominates as the primary research focus, with strong connections to terms like fMRI, magnetic resonance imaging (MRI), and functional connectivity. This emphasis underscores the significant role that imaging technologies play in understanding brain function and structure in North American Neuroinformatics research.

The integration of machine learning is evident, particularly in its links to neuroimaging, schizophrenia, and Alzheimer's disease. This reflects the growing use of computational techniques to analyse large neuroimaging datasets and study complex neurological disorders. Keywords like deep learning and image processing further demonstrate the adoption of advanced machine learning methods to enhance brain imaging analysis. The terms functional connectivity and brain networks are crucial in the network, especially about fMRI and functional neuroimaging. This area of research focuses on understanding how different brain regions interact and contribute to overall brain function. The connection to psychiatric and neurological conditions like schizophrenia and Alzheimer's disease highlights the importance of this area in disease studies.

Another critical theme in the network is data sharing and database, reflecting the emphasis on open access to large datasets and collaborative research efforts. The presence of these terms, along with Neuroinformatics, underscores the importance of data management and sharing in advancing the field across North America

Research into neuron morphology and neuron reconstruction also plays a prominent role, with terms like 3D neuron reconstruction emphasizing the importance of anatomical studies. These areas focus on mapping and analysing neuron structure to better understand brain function at the cellular level. Additionally, emerging trends in meta-analysis and data mining are highlighted, showing the increasing use of these techniques to synthesize data and identify patterns across multiple studies. This trend reflects a growing interest in leveraging large datasets to draw broader conclusions and gain new insights into brain function and disorders.

Co-occurrence of author keywords in Neuroinformatics (North America): minimum occurrence threshold of 2 and 100 links

Figure 17 showcases the co-occurrence network of author keywords in Neuroinformatics research from Europe, with a minimum occurrence threshold of 2 and 100 links. This visualization highlights the most frequently used keywords and how they interrelate, revealing key themes and trends in European Neuroinformatics research.

At the core of the network, magnetic resonance imaging (MRI) and neuroimaging emerge as dominant research focuses, closely linked with other important terms like fMRI, machine learning, and visualization. This indicates a strong emphasis on imaging technologies in the European Neuroinformatics community, with computational methods like machine learning being increasingly applied to analyse and visualize complex neuroimaging data.

Deep learning appears as another significant theme, connected with terms like convolutional neural networks, data mining, and image segmentation, reflecting the growing role of advanced machine learning techniques in the field. These technologies are particularly relevant for analysing large-scale datasets, making them integral to research into brain imaging and neurological diseases.

The network also highlights the importance of Alzheimer's disease and schizophrenia in European research, suggesting a focus on understanding these disorders through neuroimaging and computational analysis. Functional connectivity and reproducibility are key keywords connected to these areas, indicating efforts to understand brain connectivity and ensure the reliability of research findings.

In addition, data sharing and database are central to the network, underscoring the European focus on collaborative research and open access to neuroimaging and Neuroinformatics data. The integration of databases and shared resources is critical for advancing the field and ensuring wide-reaching impacts on neuroscience research.

Keywords such as neuron morphology, neuron reconstruction, and neural networks reflect ongoing research into the structural aspects of neurons and the use of computational tools to m