Delogu Franco, Aspinall Chantol, Ray Kimberly, Heinsfeld Anibal Solon, Victory Conner, Pestilli Franco
Department of Humanities, Social Sciences and Communication, Lawrence Technological University, Southfield, MI, United States.
Department of Psychology, The University of Texas at Austin, Austin, TX, United States.
Front Neuroinform. 2025 Jul 16;19:1608900. doi: 10.3389/fninf.2025.1608900. eCollection 2025.
This study demonstrates the effectiveness of integrating cloud computing platforms with Course-based Undergraduate Research Experiences (CUREs) to broaden access to neuroscience education. Over four consecutive spring semesters (2021-2024), a total of 42 undergraduate students at Lawrence Technological University participated in computational neuroscience CUREs using brainlife.io, a cloud-computing platform. Students conducted anatomical and functional brain imaging analyses on openly available datasets, testing original hypotheses about brain structure variations. The program evolved from initial data processing to hypothesis-driven research exploring the influence of age, gender, and pathology on brain structures. By combining open science and big data within a user-friendly cloud environment, the CURE model provided hands-on, problem-based learning to students with limited prior knowledge. This approach addressed key limitations of traditional undergraduate research experiences, including scalability, early exposure, and inclusivity. Students consistently worked with MRI datasets, focusing on volumetric analysis of brain structures, and developed scientific communication skills by presenting findings at annual research days. The success of this program demonstrates its potential to democratize neuroscience education, enabling advanced research without extensive laboratory facilities or prior experience, and promoting original undergraduate research using real-world datasets.
本研究证明了将云计算平台与基于课程的本科研究经验（CUREs）相结合以拓宽神经科学教育途径的有效性。在连续四个春季学期（2021 - 2024年）中，劳伦斯理工大学共有42名本科生使用云计算平台brainlife.io参与了计算神经科学CUREs项目。学生们对公开可用的数据集进行了大脑解剖和功能成像分析，检验了关于大脑结构变异的原始假设。该项目从最初的数据处理发展到以假设为驱动的研究，探索年龄、性别和病理对大脑结构的影响。通过在用户友好的云环境中结合开放科学和大数据，CURE模型为先前知识有限的学生提供了实践操作、基于问题的学习。这种方法解决了传统本科研究经验的关键局限性，包括可扩展性、早期接触和包容性。学生们持续处理MRI数据集，专注于大脑结构的体积分析，并通过在年度研究日展示研究结果来培养科学交流技能。该项目的成功证明了其使神经科学教育民主化的潜力，无需广泛的实验室设施或先前经验就能开展高级研究，并促进使用真实世界数据集的本科原创研究。
