Human Brain Network Integration During Memory Encoding

A collection of functional magnetic resonance imaging (fMRI) data from participants performing a memory encoding task, with performance assessed via a subsequent surprise memory test. It was used to investigate time-varying functional connectivity patterns across the human brain in 30-40 second periods. The author is Ruedeerat Keerativittayayut, and the data was last updated in June 2020.

Use Cases

• Analyze time-varying functional connectivity patterns to identify network states associated with high versus low memory encoding performance.
• Apply multivariate analysis using graph metrics of integration to classify brain network states based on memory task outcomes.
• Investigate the integration of specific subnetworks, such as the subcortical, default-mode, salience, and visual networks, as predictors of successful memory encoding.

Strengths

• Data is derived from a controlled fMRI experiment with a subsequent memory performance test.
• Analysis focuses on time-varying functional connectivity in specific 30-40 second periods.
• The dataset is published under a permissive CC0 1.0 public domain license.

Limitations

• The sample size (number of participants or rows) is unknown, limiting assessment of statistical power.
• Specific data columns, file formats, and dataset size are not provided, complicating usability evaluation.
• Data is from 2020 and may not reflect the latest methodologies or findings in the field.

Provenance

Source

Dryad digital repository.

Collection Method

Functional magnetic resonance imaging (fMRI) data collected during a memory encoding task, with graph analysis of functional connectivity patterns.

Freshness

Last updated on 2020-06-24.