SavedAdd your journal

Journals / Abant Tıp Dergisi / 2026 / Cilt: 15 Sayı: 1

Using Tractography Software with Diffusion MRI Analysis to Improve Neuroanatomy Education, A Neuroanatomical Education Study

Tayfun Aygün(GİRESUN ÜNİVERSİTESİ, TIP FAKÜLTESİ)0000-0001-5058-3513
Necip Furkan Kazanci(GİRESUN ÜNİVERSİTESİ, TIP FAKÜLTESİ)0009-0004-2736-8582
Arif Keskin(GİRESUN ÜNİVERSİTESİ, TIP FAKÜLTESİ)0000-0002-1634-1091
Özgür Palancı(GUMUSHANE UNIVERSITY, GÜMÜŞHANE HEALTH ACADEMY, DEPARTMENT OF NURSING)0000-0003-0396-7455
Ahmet Salbacak(GİRESUN ÜNİVERSİTESİ, TIP FAKÜLTESİ)0000-0003-1790-5204
Pages
95–102
Publication Date
2026-04-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The integration of such software into medical education may facilitate the learning process, particularly in subjects involving complex anatomical structures.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Objective: This study aimed to evaluate the effect of segmentation and tractography training using DSI Studio software, in addition to traditional neuroanatomy education, on the academic achievement of medical students.
📊
Key Findings & Data:Students who participated in tractography training constituted the study group (n=32), while those who did not formed the control group (n=40).
💡
Conclusion & Impact:The integration of such software into medical education may facilitate the learning process, particularly in subjects involving complex anatomical structures.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Diffusion Tensor Imaging · Neuroanatomy · Medical Education

Abstract

Objective: This study aimed to evaluate the effect of segmentation and tractography training using DSI Studio software, in addition to traditional neuroanatomy education, on the academic achievement of medical students. Materials and Methods: The study was conducted with volunteer second-year medical students. Students who participated in tractography training constituted the study group (n=32), while those who did not formed the control group (n=40). A 23-item multiple-choice knowledge test was administered to both groups before and after the training. The educational content included neuronal structures, the basic principles of DTI/DSI, Brodmann areas, visualization of white matter organization, and three-dimensional tractography applications. Data were analyzed using SPSS 27.0, and statistical significance was set at p<0.05. Results: Post-test scores were significantly higher than pre-test scores in both groups (p<0.001). However, the study group achieved significantly higher post-test scores compared with the control group (p=0.044). The post–pre test difference (learning gain) was also greater in the study group (p=0.013). The effect size for learning gain between groups was moderate. Conclusion: Segmentation and tractography applications performed with DSI Studio enhance academic performance in neuroanatomy education by enabling three-dimensional examination and evaluation of neuroanatomical structures. The integration of such software into medical education may facilitate the learning process, particularly in subjects involving complex anatomical structures.

🔖

Cite This Article

Aygün, T., Kazanci, N. F., Keskin, A., Palancı, Ö., Salbacak, A. (2026). Using Tractography Software with Diffusion MRI Analysis to Improve Neuroanatomy Education, A Neuroanatomical Education Study. Abant Tıp Dergisi, Cilt: 15 Sayı: 1, 95–102. https://doi.org/10.47493/abantmedj.1883188

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (29)

  1. Harden RM, Stevenson M, Downie WW, Wilson GM. Assessment of clinical competence using objective structured examination. Br Med J. 1975;1(5955):447–51.
  2. Otuyemi OD, Ijarotimi OA, Komolafe AO, Aregbesola SB, Adetutu OM, Akinwale OB, et al. Adopting virtual reality in medical education: Insights from clinical students and lecturers in a Nigerian University. Niger J Clin Pract. 2025;28(5):582–9.
  3. Guze PA. Using technology to meet the challenges of medical education. Trans Am Clin Climatol Assoc. 2015;126:260–70.
  4. Nagi F, Salih R, Alzubaidi M, Shah H, Alam T, Shah Z, et al. Applications of artificial intelligence (AI) in medical education: A scoping review. Stud Health Technol Inform. 2023;305:648–51.
  5. Pokrywka P, Kamińska H, Jarosz-Chobot P. Importance of simulation-based medical education and its application in diabetology. Pediatr Endocrinol Diabetes Metab. 2024;30(1):45–7.
  6. Wang D, Rutie Y, Qingli L, Kemin L, Dan X, Xinyi J. Application of virtual reality technology in gynecology and obstetrics teaching. West China Med J. 2017;32(6):914–7.
  7. Pandey P, Zimitat C. Medical students’ learning of anatomy: Memorisation, understanding and visualisation. Med Educ. 2007;41(1):7–14.
  8. Hlavac RJ, Klaus R, Betts K, Smith SM, Stabio ME. Novel dissection of the central nervous system to bridge gross anatomy and neuroscience for an integrated medical curriculum. Anat Sci Educ. 2018;11(2):185–95.
  9. Zhang R, Gong Z, Jiang W, Su Z. Utilizing neuroimaging visualization technology to enhance standardized neurosurgical training for traditional chinese medicine residents: A neuroanatomical education study. Brain Hemorrhages. 2025;6(2):81–5.
  10. Maier-Hein KH, Neher PF, Houde JC, Côté MA, Garyfallidis E, Zhong J, et al. The challenge of mapping the human connectome based on diffusion tractography. Nat Commun. 2017;8(1):1349.
  11. Hula WD, Panesar S, Gravier ML, Yeh FC, Dresang HC, Dickey MW, et al. Structural white matter connectometry of word production in aphasia: an observational study. Brain. 2020;143(8):2532–44.
  12. Yeh FC, Verstynen TD, Wang Y, Fernández-Miranda JC, Tseng WYI. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS One. 2013;8(11):e80713.
  13. Hindy NC, Bishara AJ, Pani JR. Described neural connections enhance classroom learning of neuroanatomy. Anat Sci Educ. 2025;18(7):642–56.
  14. Sun F, Huang Y, Wang J, Hong W, Zhao Z. Research progress in diffusion spectrum imaging. Brain Sci. 2023;13(10):1–15.
  15. Zeng J, Shi L, Liu Y, Yang J, Zhang L, Song H, et al. Enhancing neuroanatomy teaching with DSI Studio’s fiber reconstruction technology. Adv Med Educ Pract. 2025;16:875–89.
  16. Sweeney K, Hayes JA, Chiavaroli N. Does spatial ability help the learning of anatomy in a biomedical science course? Anat Sci Educ. 2014;7(4):289–94.
  17. Vorstenbosch MATM, Klaassen TPFM, Donders ARTR, Kooloos JGM, Bolhuis SM, Laan RFJM. Learning anatomy enhances spatial ability. Anat Sci Educ. 2013;6(4):257–62.
  18. Pettersson AF, Karlgren K, Al-Saadi J, Hjelmqvist H, Meister B, Zeberg H, et al. How students discern anatomical structures using digital three-dimensional visualizations in anatomy education. Anat Sci Educ. 2023;16(3):452–64.
  19. Langlois J, Bellemare C, Toulouse J, Wells GA. Spatial abilities and anatomy knowledge assessment: A systematic review. Anat Sci Educ. 2017;10(3):235–41.
  20. Dziedzic TA, Balasa A, Jeżewski MP, Michałowski Ł, Marchel A. White matter dissection with the Klingler technique: a literature review. Brain Struct Funct. 2021;226(1):13–47.
  21. Çavdar S, Esen Aydın A, Algin O, Aydoğmuş E. Fiber dissection and 3-tesla diffusion tensor tractography of the superior cerebellar peduncle in the human brain: emphasize on the cerebello-hypthalamic fibers. Brain Struct Funct. 2020;225(1):121–8.
  22. Chytas D, Salmas M, Noussios G, Paraskevas G, Protogerou V, Demesticha T, et al. Do virtual dissection tables add benefit to cadaver-based anatomy education? An evaluation. Morphologie. 2023;107(356):1–5.
  23. Singal A, Bansal A, Chaudhary P. Cadaverless anatomy: Darkness in the times of pandemic Covid-19. Morphologie. 2020;104(346):147–50.
  24. Zavala-Cerna MG, Caballero AE, Verbeeck-Mendez S, Parker MJ. Self-rated benefits and knowledge gain from e-learning: the longitudinal use of an online learning experience at an international medical school. BMC Med Educ. 2025;25(1):222.
  25. Tzuriel D, Egozi G. Gender differences in spatial ability of young children: the effects of training and processing strategies. Child Dev. 2010;81(5):1417–30.
  26. Sneider JT, Hamilton DA, Cohen-Gilbert JE, Crowley DJ, Rosso IM, Silveri MM. Sex differences in spatial navigation and perception in human adolescents and emerging adults. Behav Processes. 2015;111:42–50.
  27. Lauer JE, Yhang E, Lourenco SF. The development of gender differences in spatial reasoning: A meta-analytic review. Psychol Bull. 2019;145(6):537–65.
  28. Kooloos JGM, Bergman EM, Scheffers MAGP, Schepens-Franke AN, Vorstenbosch MATM. The Effect of passive and active education methods applied in repetition activities on the retention of anatomical knowledge. Anat Sci Educ. 2020;13(4):458–66.
  29. Chariker JH, Naaz F, Pani JR. Computer-based learning of neuroanatomy: A longitudinal study of learning, transfer, and retention. J Educ Psychol. 2011;103(1):19–31.