Multimodal dataset linking wide-field calcium imaging to behavior changes in operant lever-pull task in mice.

Area of Science:

• Neuroscience
• Behavioral Science
• Data Science

Background:

• Understanding the brain dynamics of behavior change requires linking detailed behavioral measurements with neuronal activity.
• Existing datasets often lack the multimodal and longitudinal data needed for comprehensive analysis.

Purpose of the Study:

• To provide an extensive, multimodal dataset of motor skill learning in mice.
• To facilitate research into the neural mechanisms underlying behavior and learning.

Main Methods:

• Collected 15 sessions of data over 2 weeks from 25 mice learning a lever-pulling task.
• Used high-speed videography for body, facial, and eye movements; monitored environmental parameters.
• Recorded resting-state cortical activity and sensory-evoked responses.

Main Results:

• The dataset captures detailed behavioral changes during motor skill acquisition.
• Includes simultaneous recordings of brain activity, providing a rich resource for neural correlates of behavior.
• Data is formatted using the Neurodata Without Borders (NWB) standard and adheres to FAIR principles.

Conclusions:

• This multimodal dataset is a valuable resource for investigating neural dynamics of learning and behavior.
• Enables collaborative research into within-session learning, long-term adaptations, and neural circuit function.
• Supports exploration of sensory-driven neural dynamics alongside motor learning.