Du et al. (2025) N=11 MRI Dataset: Within-Individual Functional Connectivity Matrices

11 participants underwent 8-11 MRI sessions each at the Harvard Center for Brain Science. The dataset contains within-individual functional connectivity correlation matrices computed from dense fMRI acquisitions, provided for both resting-state fixation and task-regressed data. It was created by Jingnan Du and colleagues, with data last updated in June 2026.

Use Cases

• Estimate stable individual brain network organization based on dense within-individual fMRI data.
• Explore task responses across sensory, motor, and cognitive domains using task-regressed functional connectivity matrices.
• Compare functional connectivity patterns between resting-state fixation and task conditions within the same individuals.

Strengths

• Data from 11 participants with 8-11 MRI sessions each, providing dense within-individual sampling.
• Matrices are provided for both resting-state fixation and task-regressed data, enabling condition comparisons.
• Data acquired with a 3-T Prisma-fit MRI scanner and a 32-channel head coil, suggesting high-quality neuroimaging data.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data files are split into 2.5GB sub-files, requiring recombination after download.

Provenance

Source

Harvard Center for Brain Science

Collection Method

Extensive BOLD-contrast fMRI data were acquired using both resting-state fixation and task protocols over multiple sessions.

Freshness

Last updated 2026-06-10 19:54:52; freshness should be verified.