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Related Experiment Video

Updated: May 21, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

From features to functional maps: An attention based framework for explainable memory-related brain network analysis.

Muhammad Shahzaib1, Salma Zainab Farooq1, Sadia Shakil2

  • 1Department of Electrical Engineering, Institute of Space Technology, Islamabad, Pakistan.

Physiology & Behavior
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

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This study reveals that memory recall involves the entire brain, not just traditional regions. A Graph Attention Network (GAT) model accurately identified key brain networks for memory performance.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Artificial Intelligence in Medicine

Background:

  • Memory recall engages distributed brain networks beyond traditional regions of interest.
  • Existing research often overlooks the role of white matter and cerebellum in memory recall.
  • Many studies lack interpretability of the identified brain networks.

Purpose of the Study:

  • To propose and validate the involvement of the entire brain in memory recall networks.
  • To develop a Graph Attention Network (GAT) model for identifying memory-related brain regions and their connections.
  • To enhance the interpretability of brain network models for memory recall.

Main Methods:

  • Utilized functional connectivity (FC) from fMRI data of 180 subjects.
  • Developed a Graph Attention Network (GAT) model to identify regions of interest (ROIs) and their inter-region dependencies.
Keywords:
CerebellumExplainable AIFunctional connectivityGraph attention networksMemory encodingMemory recallWhite matter

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Last Updated: May 21, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Published on: October 13, 2023

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  • Employed GATxp, an attention-based explainer, to generate interpretable sub-network ROI maps.
  • Validated findings using Network-Based Statistics, Coordinate-based Meta-analytic decoding (NeuroQuery), and literature review.

    • Main Results:

    • The GAT model achieved 84% accuracy in classifying high versus low memory recall using four-fold cross-validation.
    • Identified memory-related networks encompassing default mode, frontoparietal control, visual, cerebellar, and white matter regions.
    • 84 out of 90 identified ROIs showed significant associations with memory recall.

    Conclusions:

    • Memory recall is supported by distributed, whole-brain networks, including noncanonical regions.
    • Graph Attention Networks offer accurate classification and neuro-scientific interpretability for memory encoding studies.
    • This approach advances understanding of ecologically valid memory encoding processes.