EEG-Based Effective Connectivity Analysis of Brain Networks During Episodic Encoding

Hrishikesh Pable's research presents a computational framework for estimating effective connectivity from source-localized EEG data. The study analyzes bidirectional interactions among the Salience, Default Mode, and Task-Positive networks during rest and the encoding phase of a free-recall task. It investigates the mediating influence of individual differences in mental visual imagery, as measured by the Vividness of Visual Imagery Questionnaire.

Use Cases

• Analyze state-dependent connectivity patterns between the Salience Network (SN), Default Mode Network (DMN), and Task-Positive Network (TPN) during rest and encoding phases.
• Model the mediating influence of Vividness of Visual Imagery Questionnaire (VVIQ) scores on connectivity strength from SN to TPN for different stimulus types.
• Study the reversal in DMN-to-SN influence across cognitive states using a biologically-informed recurrent neural network framework.

Strengths

• Framework integrates standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA) with a recurrent neural network for interpretable connectivity estimates.
• Analysis is based on a supraspan immediate free-recall paradigm with two distinct stimulus types: geometric shapes and words.
• The model incorporates neurobiological constraints including Dale's Law and separate excitatory/inhibitory populations.

Limitations

• The dataset is very small, consisting of a single 9.0 KB DOCX file, limiting direct data analysis and reuse.
• No raw EEG time-series or tabular connectivity matrices are provided, only the computational findings in a document.
• Sample size and demographic details of the participant cohort are not specified in the provided metadata.

Provenance

Source

Hrishikesh Pable via figshare.

Collection Method

Source-localized EEG data analyzed with a computational framework integrating sLORETA and a recurrent neural network.

Freshness

Last updated on 2026-03-23.