Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation

Area of Science:

• Neuroscience
• Cognitive Psychology
• Data Science

Background:

• Self-regulation is crucial for adaptive behavior.
• Understanding the neural basis of self-regulation requires comprehensive data.
• Existing datasets often lack multi-modal neuroimaging and diverse cognitive assessments.

Purpose of the Study:

• To share a rich, multi-modal dataset for self-regulation research.
• To facilitate the study of cognitive and neural mechanisms underlying self-regulation.
• To provide a standardized dataset (BIDS) for the neuroscience community.

Main Methods:

• Acquired task-based fMRI, resting-state fMRI, and structural MRI from 103 healthy adults.
• Administered ten distinct cognitive tasks within two 90-minute scanning sessions.
• Collected survey data alongside neuroimaging and cognitive task performance.

Main Results:

• A comprehensive dataset comprising cognitive, survey, and neuroimaging data was generated.
• Data is openly available via the OpenNeuro platform.
• Dataset adheres to the Brain Imaging Data Structure (BIDS) standard for interoperability.

Conclusions:

• This openly shared dataset provides a valuable resource for investigating self-regulation.
• The multi-modal approach allows for integrated analysis of cognitive, neural, and self-report measures.
• Standardized data format (BIDS) promotes data sharing and reproducibility in self-regulation research.