A Transdiagnostic fMRI Dataset With 300+ Deeply Phenotyped Subjects Across Resting and Task States.
Anja Samardzija1, Xilin Shen2, Abigail S Greene3,4
1Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Medrxiv : the Preprint Server for Health Sciences
|August 12, 2025
Summary
The YaleNeuroConnect dataset offers functional MRI data from diverse individuals under various tasks. This resource aids in understanding brain connectivity and its relation to cognitive and clinical measures.
Area of Science:
- Neuroscience
- Neuroimaging
- Cognitive Science
Background:
- Functional magnetic resonance imaging (fMRI) is crucial for studying brain activity.
- Resting-state fMRI is common, but task-based fMRI may yield stronger predictive models.
- Understanding brain-behavior relationships requires diverse datasets with rich phenotyping.
Purpose of the Study:
- To introduce the YaleNeuroConnect dataset, a comprehensive human fMRI resource.
- To facilitate research on brain parcellation, cognitive-clinical relationships, and brain-based tests.
- To support studies utilizing the Research Domain Criteria framework with transdiagnostic data.
Main Methods:
- Collected functional MRI data (resting-state and 6 task conditions) from 302 diverse subjects.
- Acquired 48 minutes of fMRI data and high-resolution 3D brain volumes per subject.
- Gathered extensive neuropsychological testing and symptom inventories for deep phenotyping.
Main Results:
- The dataset includes functional connectomes alongside fMRI data.
- Task-based fMRI data were collected across various cognitive domains.
- The sample is transdiagnostic, enabling a wide range of symptom score analyses.
Conclusions:
- YaleNeuroConnect provides a valuable resource for studying brain function and its relation to behavior.
- The dataset supports the development of advanced brain-based diagnostic and assessment tools.
- This resource facilitates research into the neural underpinnings of psychiatric disorders within a transdiagnostic framework.
