Kondo Masashi, Sehara Keisuke, Harukuni Rie, Aoki Ryo, Sugimoto Shoya, Tanaka Yasuhiro R, Matsuzaki Masanori, Nakae Ken
Department of Physiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Brain Science Institute, Tamagawa University, Tokyo, Japan.
Sci Data. 2025 Jul 29;12(1):1264. doi: 10.1038/s41597-025-05482-y.
The link between comprehensive behavioral measurements during a behavioral task and brain-wide neuronal activity is an essential strategy to better understand the brain dynamics underlying the emergence of behavior changes. To tackle this, we provide an extensive, multimodal dataset that includes 15 sessions spanning 2 weeks of motor skill learning, in which 25 mice were trained to pull a lever to obtain water rewards. Simultaneous high-speed videography captured body, facial, and eye movements, and environmental parameters were monitored. The dataset also features resting-state cortical activity and sensory-evoked responses, enhancing its utility for both learning-related and sensory-driven neural dynamics studies. Data are formatted in accordance with the Neurodata Without Borders (NWB) standard, ensuring compatibility with existing analysis tools and adherence to the FAIR principles (Findable, Accessible, Interoperable, Reusable). This resource enables in-depth investigations into the neural mechanisms underlying behavior and learning. The platform encourages collaborative research, supporting the exploration of rapid within-session learning effects, long-term behavioral adaptations, and neural circuit dynamics.
在行为任务期间进行全面行为测量与全脑神经元活动之间的联系，是更好地理解行为变化出现背后的脑动力学的一项重要策略。为了解决这个问题，我们提供了一个广泛的多模态数据集，该数据集包括15个会话，涵盖了为期2周的运动技能学习，其中25只小鼠被训练拉动杠杆以获得水奖励。同时进行的高速摄像捕捉了身体、面部和眼睛的运动，并监测了环境参数。该数据集还具有静息状态皮质活动和感觉诱发反应，增强了其在学习相关和感觉驱动神经动力学研究中的实用性。数据按照无国界神经数据（NWB）标准进行格式化，确保与现有分析工具兼容并遵循FAIR原则（可查找、可访问、可互操作、可重用）。这一资源能够深入研究行为和学习背后的神经机制。该平台鼓励合作研究，支持对会话内快速学习效应、长期行为适应和神经回路动力学的探索。
