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Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Task-guided generative adversarial networks for synthesizing and augmenting structural connectivity matrices for

Tatsuya Yamamoto¹, Tomoki Sugiura¹, Tomoyuki Hiroyasu²

¹Graduate School of Life and Medical Sciences, Doshisha University, Kyoto, Japan.

Network Neuroscience (Cambridge, Mass.)

|October 27, 2025

Summary

This summary is machine-generated.

Task-guided generative adversarial network (GAN) II enhances brain connectome predictions by augmenting data, improving accuracy with limited samples. This method aids human connectomics research where large datasets are impractical.

Keywords:

Connectome-based prediction Data augmentation Generative adversarial networks (GANs)Generative models Structural connectivity

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Area of Science:

Neuroscience
Machine Learning
Data Science

Background:

Machine learning advances connectome-based predictions of cognitive traits.
Large datasets are typically required, posing collection challenges.

Purpose of the Study:

Introduce Task-guided GAN II, a novel data augmentation method.
Expand sample sizes for connectome-based prediction tasks using GANs.
Improve prediction accuracy by synthesizing structural connectivity matrices.

Main Methods:

Utilized a Wasserstein GAN framework with a task-guided branch.
Synthesized structural connectivity matrices to augment data.
Evaluated on fluid intelligence prediction using NIMH and Human Connectome Project datasets.

Main Results:

Data augmentation with Task-guided GAN II improved prediction accuracy.
Significant performance gains observed with up to twofold augmentation on limited data.
Excessive augmentation did not yield further improvements, indicating complementarity to real data.

Conclusions:

Task-guided GAN II is effective for small datasets in human connectomics.
The method enhances predictive modeling where large-scale data collection is challenging.
Augmentation complements, but does not fully replace, real data collection.