کشف روندهای فعلی و شناسایی مسیرهای پژوهشی آینده در علوم اعصاب

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار گروه علم اطلاعات و دانش شناسی، دانشگاه رازی، کرمانشاه، ایران.

2 دانشیار گروه علم اطلاعات و دانش شناسی، دانشگاه پیام نور، تهران، ایران.

3 دانشیار گروه روان‌شناسی، دانشگاه رازی، کرمانشاه، ایران.

چکیده

هدف: هدف این پژوهش، شناسایی ساختار مفهومی پژوهش‌های مرتبط با علوم اعصاب در پایگاه کلاریویت آنلیتیکس و کشف زیرحوزه‌ها و ابعاد مهم مفهوم است.
روش‌شناسی: این پژوهش با استفاده از روش‌های کتاب‌سنجی و تحلیل هم‌رخدادی واژگان انجام‌شده است. تعداد 19701 مدرک در بازة زمانی 1985 تا 2024 از پایگاه کلاریویت آنلیتیکس استخراج گردید تا روند پژوهش‌های مرتبط با علوم اعصاب بررسی شود. از تحلیل خوشه‌ای و نمودار راهبردی برای ترسیم ساختار مفهومی پژوهش‌های حوزة علوم اعصاب استفاده‌شده است. داده‌ها با بهره‌گیری از نرم‌افزارهای تخصصی بیب اکسل، ووس ویور و یوسی‌آی‌نت پردازش شدند.
یافته‌ها: تحلیل داده‌های پژوهش نشان داد که مفاهیم مرتبط با علوم اعصاب، بیماری آلزایمر و آسیب‌شناسی عصبی از بیشترین فراوانی برخوردارند. یافته‌های تحلیل خوشه‌ای نشان داد که مفاهیم این حوزه در شش خوشه، ناقل عصبی (عصب رسانا)، اختلالات عصب‌شناختی، عصب‌شناسی شناختی، اتصالات عصبی، بازاریابی عصبی، و درد و اختلالات عصبی رشدی. یافته‌ها همچنین نشان داد که خوشه اتصالات عصبی، یکی از حوزه‌های نوظهور در پژوهش‌های علوم اعصاب به شمار می‌رود.
نتیجه‌گیری: این مطالعه نشان ‌داد که علوم اعصاب، به‌عنوان حوزه‌ای پویا و بین‌رشته‌ای، ظرفیت بالایی برای توسعه پژوهش‌های آینده در زمینه‌های مختلف دارد. همچنین نتایج نشان داد که خوشه‌های اختلالات عصب‌شناختی (2) و عصب‌شناسی شناختی (3) از موضوعات محوری در حوزة علوم اعصاب محسوب می‌شوند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Discovering Current Trends and Identi-fying Future Research Directions in Neuroscience

نویسندگان [English]

  • Saleh Rahimi 1
  • Faramarz Soheili 2
  • Kamran Yazdanbakhsh 3
1 Associate Professor, Department of Knowledge and Information Science, Razi University, Kermanshah, Iran
2 Associate professor, Department of Knowledge and Information Science, Payame Noor University, Tehran, Iran.
3 Associate Professor, Department of Psychology, Razi University, Kermanshah, Iran
چکیده [English]

Purpose: Neuroscience, a field characterized by its vast scope and thematic diversity, requires systematic efforts to map its conceptual structure and analyze evolving research trends. A lack of clarity in these areas risks misallocation of resources and the perpetuation of redundant studies. This study addresses this gap by employing bibliometric methods to identify the conceptual architecture of neuroscience, pinpoint emerging subdomains, and evaluate critical dimensions within the Clarivate Analytics database. The primary objectives are to delineate the intellectual landscape of neuroscience research and highlight its interdisciplinary potential to guide future scientific inquiry.
Methodology: This applied research integrates co-word and network analysis to examine four decades of neuroscience literature (1985–2024). Data were extracted from Clarivate Analytics on February 24, 2025, using a targeted search strategy with MeSH descriptors in the title fields, including terms such as:
TI=(Neurosciences) OR TI= (Cognitive Neuroscience) OR TI=(Neuroanatomy) OR TI=(Neurobiology) OR TI=(Neurochemistry) OR TI=(Neuroendocrinology) OR TI=(Neuropathology) OR TI=(Neuropharmacology) OR TI=(Neurophysiology)
The search yielded 19,701 documents containing 69,740 author keywords, which were standardized to 27,529 unique terms after deduplication and normalization. Analytical tools such as VOSviewer, UCINET, and BibExcel were employed for data processing. A symmetric matrix was generated in BibExcel, transformed into a correlation matrix, and filtered using a threshold of 38 to produce a 188 × 188 matrix (with diagonal values set to zero). Cluster analysis using the K-means method in VOSviewer visualized the co-occurrence network, while UCINET was used to calculate network metrics.
Findings: The findings revealed a general growth trend in neuroscience publications, although a decline was observed in recent years. Keyword analysis identified neuroscience, Alzheimer's disease, and neuropathology as the most frequent terms, with neuroscience serving as the field's conceptual anchor. Cluster analysis uncovered six thematic groups: Neurotransmitters (1), Cognitive Disorders (2), Cognitive Neuroscience (3), Neural Connections (4), Neuromarketing (5), and Pain and Neurodevelopmental Disorders (6). Among these, Cognitive Disorders (2) demonstrated the highest centrality, reflecting their broad interdisciplinary influence and connectivity within the research network, while Pain and Neurodevelopmental Disorders (6) exhibited the highest density, indicating strong intra-cluster cohesion. Strategic diagram analysis positioned Cognitive Disorders (2) and Cognitive Neuroscience (3) as dominant, cohesive themes occupying the network's core due to their extensive linkages and centrality. In contrast, Neuromarketing (5) and Pain and Neurodevelopmental Disorders (6) occupied peripheral positions, representing specialized but less influential niches. Neural Connections (4) emerged as a rapidly evolving subfield, whereas Neurotransmitters (1) resided in the fourth quadrant, signaling untapped potential for future development despite its current underrepresentation.
Conclusion: The study concludes that the dynamism of neuroscience stems from its interdisciplinary nature, with a significant capacity to drive innovation across medicine, education, marketing, and spatial design. The centrality of cognitive disorders and cognitive neuroscience underscores their foundational role, combining high cohesion, cross-disciplinary influence, and conceptual interconnectedness. These clusters dominate the research network and exhibit robust internal synergy, enabling them to address complex questions such as the mechanisms of neurodegenerative diseases and models of cognitive processing. Meanwhile, emerging areas like neural connectivity and neuromarketing underscore the field's expansion into novel domains, facilitated by advanced machine learning and neuroimaging methodologies. The increasing adoption of hybrid analytical tools—particularly within these clusters—reflects a broader shift toward integrating computational and experimental paradigms in neuroscience research. The results highlight how neuroscience connects traditional disciplines with innovative applications. For example, its intersection with medicine advances diagnostic tools for conditions such as Alzheimer's disease, while collaborations with marketing explore consumer behavior through neuromarketing frameworks. Similarly, linkages with spatial design and education demonstrate neuroscience’s potential to optimize learning environments and urban planning through neuroscientific insights. However, the study identifies gaps in underdeveloped areas, such as neurotransmitter research, which remains underrepresented despite its fundamental importance and warrants targeted investment to unlock its transformative potential. Strategic diagram analysis reinforces the importance of modern, data-driven approaches in parsing neuroscience's complexity. The convergence of diverse methodologies—from bibliometric mapping to network analysis—enables researchers to navigate the extensive literature and identify highly influential trajectories. This integrative approach reduces redundancy and fosters innovation by highlighting underserved niches, such as neurodevelopmental disorders, and emerging frontiers, such as neural connectivity. This study maps the conceptual evolution of neuroscience, highlighting its dual focus on established domains and emerging innovations. By delineating clusters of influence, cohesion, and growth, it provides a roadmap for prioritizing research efforts, fostering interdisciplinary collaboration, and leveraging advanced tools to address pressing scientific and societal challenges. The findings affirm neuroscience's role as a catalyst for breakthroughs across sectors—from healthcare to technology—while emphasizing the need for sustained investment in both its core pillars and emerging frontiers.

کلیدواژه‌ها [English]

  • Neuroscience
  • Cognitive disorders
  • Co-words analysis
  • Conceptual struc-ture
حاجیان، ح.، و زرجینی، ا. (1402). تحلیلی بر کاربردهای داده‌کاوی در صنعت بیمه بر اساس شبکه هم‌رخدادی واژگان‌ها و شناسایی معتبرترین مجلات با شاخص استناد به پژوهش‌های علمی با استفاده از رویکرد علم‌سنجی. پژوهشنامه بیمه، 13(1)، ۷۱-۸۶. https://doi.org/10.22056/ijir.2024.01.06
حاضری، ا.، توکلی زاده راوری، م.، احمدی، ن.، و سهیلی، ف. (1395). تبیین چگونگی پیوند فناوری و علم: مطالعه موردی حوزه نانو الکترونیک. پژوهش‌نامه کتابداری و اطلاع‌رسانی، 6(2)، ۲۶۱-۲۸۰.
حسینی نسب، ص.، مهدیزاده سراج، ف.، و خان‌محمدی، م. (1402). تحلیل تولیدات علمی دانشگاه‌های ایران در حوزه‌ی معماری عصب‌محور: مرور دامنه. پژوهش‌نامه علم‌سنجی، 9(1)، ۲۳۱-۲۵۸.
رستمی، م.، سهیلی، ف.، و خاصه، ع. (1399). ساختار دانش در پروانه‌های ثبت اختراع حوزه کشف دانش: مصورسازی با استفاده از تحلیل هم‌رخدادی واژگان. پژوهش‌نامه علمسنجی، 6(12)، ۴۱-۶۰.
رمضانی، ه.، علیپورحافظی، م. و مؤمنی، ع. (1393). نقشه‌های علمی: فنون و روش‌ها. ترویج علم، ۶(۱)، ۵۳-۸۴. https://dor.isc.ac/dor/20.1001.1.22519033.1393.5.1.4.1
شرق، ع محمدحسن زاده، ح جوهری، ک ولی نژادی، ع مولایی، ع امان‌اللهی، ع و عشایری، ح. (1390). بررسی حضور علوم اعصاب ایران در پایگاه ISI بر اساس شاخص‌های علم‌سنجی. مدیریت سلامت، 14(44)، ۶۱-۷۰. https://jha.iums.ac.ir/article-1-834-fa.html
مکی‌زاده، ف.، توکلی‌زاده راوری، م.، منصوری، ن.، و سهیلی، ف. (1397). بررسی شباهت بین متون علمی و فنی در حوزه ایمپلنت‌های دندانی. مدیریت اطلاعات سلامت، 15(5)، ۲۱۴-۲۱۹.
An, X. Y., & Wu, Q. Q. (2011). Co-word analysis of the trends in stem cells field based on subject heading weighting. Scientometrics, 88(1), 133-144.
https://doi.org/10.1007/s11192-011-0374-1
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Assefa, S. G., & Rorissa, A. (2013). A bibliometric mapping of the structure of stem education using co-word analysis. Journal of the American Society for Information Science and Technology, 64(12), 2513-2536. https://doi.org/10.1002/asi.22917
Baker, B., Lansdell, B., & Kording, K. P. (2022). Three aspects of representation in neuroscience. Trends in Cognitive Sciences, 26(11), 942-958.
Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management, 37(6), 817–842. https://doi.org/10.1016/S0306-4573(00)00051-0
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Espiritu-Martinez, A. P., Espinoza-Veliz, M. Z., Espinoza-Egoavil, M. J., Gomez-Perez, K. K., Espinoza-Véliz, K. L., Villa-Ricapa, L. F., & Núñez-Palacios, E. L. (2024). Bibliometric analysis of publications on neuroscience and noncommunicable diseases in the Scopus database. EAI Endorsed Transactions on Pervasive Health and Technology, 10, 1-8.
Gordon, J. A., Dzirasa, K., & Petzschner, F. H. (2024). The neuroscience of mental illness: Building toward the future. Cell, 187(21), 5858-5870.
Hajiyan, H., & Zarjini, A. (2023). A comprehensive analysis of keywords co-occurrence network and the most cited journals on data mining techniques in insurance industry using scientometrics approach. Iranian Journal of Insurance Research, 13(1), 71-86.
Hazawawi, N. A. M., Othman, M. F. I., Emran, M. H., Zakaria, M. H., Pee, N. C., & Othman, M. A. (2015, August). A structured literature review on applied neuroscience in Information Systems (neuroIS) [Conference presentation]. 2015 International Symposium on Technology Management and Emerging Technologies (ISTMET) (pp. 20-24). IEEE.
Hazeri, A., Tavakolizadeh-Ravari, M., Ahmadi, N. & Soheili, F. (2016). A study of the linkages between technology and science: a case study of nano- electronics. Library and Information Science Research, 6(2), 261-280. https://doi.org/10.22067/riis.v6i2.57221 [In Persian].
He, Q. (1999). Knowledge discovery through co-word analysis. Library Trends, 48(1), 59-133 https://www.sciepub.com/reference/174713
Hosseini Nasab, S., Mehdizadeh Saraj, F., & Khanmohammadi, M. A. (2023). Analysis of Iranian scientific productions in neuro-architecture: a scoping review. Scientometrics Research Journal, 9(1), 231-258. https://doi.org/10.22070/rsci.2021.13910.1479 [In Persian].
Ke, W., Yunjiang, X., Xiao, L., Weichan, L. (2013). Analysis on current research of supernetwork through knowledge mapping method. In M. Wang, (ed.), Knowledge Science, Engineering and Management. KSEM 2013. Lecture Notes in Computer Science  (Vol. 8041, pp. 538-550). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39787-5_45
Khasseh, A., Soheili, F., Sharif moghaddam, H., & Mousavi chelak, A. (2017). Intellectual structure of knowledge of imetrics: A co-word analysis. Information Processing & Management, 53(3):705-720. https://doi.org/10.1016/j.ipm.2017.02.001
Kumar, S., & Mohd Jan, J. (2012). Discovering Knowledge landscapes: An epistemic analysis of business and management field in Malaysia. Procedia-Social and Behavioral Sciences, 65, 1027-1032. https://doi.org/10.1016/j.sbspro.2012.11.237
Lin, C. L., Chen, Z., Jiang, X., Chen, G. L., & Jin, P. (2022). Roles and research trends of neuroscience on major information systems journal: a bibliometric and content analysis. Frontiers in Neuroscience, 16(872532). https://doi.org/10.3389/fnins.2022.872532
Liu, Y., Zhao, R., Xiong, X., & Ren, X. (2023). A bibliometric analysis of consumer neuroscience towards sustainable consumption. Behavioral Sciences, 13(4), 298.
Makkizadeh, F., Tavakolizadeh-Ravari, M., Mansoori, N. & Soheili, F. (2018). The similarity between science and technology literature in dental implants field. Health Information Management15(5), 214-219. https://doi.org/10.22122/him.v15i5.3534 [In Persian].
Mane, K. K., & Börner, K. (2004). Mapping topics and topic bursts in PNAS. Proceedings of the National Academy of Sciences, 101(suppl 1), 5287-5290.
Melcer, E., Nguyen, T. H. D., Chen, Z., Canossa, A., El-Nasr, M. S., & Isbister, K. (2015). Games research today: Analyzing the academic landscape 2000-2014 . Network, 17, 20. https://adk.elsevierpure.com/en/publications/games-research-today-analyzing-the-academic-landscape-2000-2014
Musgrove, P. B., Binns, R., Page-Kennedy, T., & Thelwall, M. (2003). A method for identifying clusters in sets of interlinking Web spaces. Scientometrics, 58(3), 657-672.
Öberg, C. (2023). Neuroscience in business-to-business marketing research: A literature review, co-citation analysis and research agenda. Industrial Marketing Management, 113, 168-179. https://doi.org/10.1016/j.indmarman.2023.06.004
Pessin, V. Z., Yamane, L. H., & Siman, R. R. (2022). Smart bibliometrics: an integrated method of science mapping and bibliometric analysis. Scientometrics, 127(6), 3695-3718.
Ramezani, H., Alipour-Hafezi, M. & Momeni, E. (2014). Scientific maps: Methods and techniques. Popularization of Science, 6(1), 53-84.  
https://dor.isc.ac/dor/20.1001.1.22519033.1393.5.1.4.1[In Persian].
Rostami, M., Soheili, F., & Khasseh, A. (2020). Knowledge structure in knowledge discovery patents: visualization based on co-word analysis. Scientometrics Research Journal6(2), 41-60. https://doi.org/10.22070/rsci.2019.3841.1240 [In Persian].
Şekerci, Y. (2024). Neuroscience and spatial design bibliometric analysis in Web of Science database. Journal of Computational Design, 5(2), 279-300.
Shargh, A., Mohammadhassanzadeh, H., Johari, K., Valinejadi, A., Molaei, A., Amanollahi, A., & Ashayeri, H. (2011). The study of the presence of Iranian neuroscience in ISI database based on scientometric factors. Journal of Health Administration, 14(44), 61-70.
Tabibnia, G. (2024). Neuroscience education as a tool for improving stress management and resilience. Current Opinion in Behavioral Sciences, 59(101401).
Terrón, P. D. (2024). Neuroscience in the educational field. Analysis of scientific production and co-words of the term neuroeducation. Journal of Neuroeducation, 4(2), 46-65.
Wang, X., & Inaba, M. (2009). Analyzing structures and evolution of digital humanities based on correspondence analysis and co-word analysis. Art Research, 9, 123-134.
Wu, Y., & Krueger, F. (2024). Charting the neuroscience of interpersonal trust: A bibliographic literature review. Neuroscience & Biobehavioral Reviews, 167(105930).
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629