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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Using unsupervised capsule neural network reveal spatial representations in the human brain.

Gongshu Wang¹, Ning Jiang¹, Tiantian Liu¹

¹School of Medical Technology, Beijing Institute of Technology, Beijing, China.

Human Brain Mapping

|March 28, 2024

Summary

This study used an unsupervised capsule neural network (U-CapsNet) to model human spatial processing. The U-CapsNet successfully captured brain activity patterns and human behavioral factors, offering insights into spatial representation.

Keywords:

brain encoding brain model fMRI spatial working memory

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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

Area of Science:

Neuroscience
Computational Neuroscience
Artificial Intelligence

Background:

Human spatial perception relies on complex brain mechanisms that are not fully understood.
Unsupervised capsule neural networks (U-CapsNets) offer a computational approach sensitive to spatial relationships, mirroring potential brain processing principles.

Purpose of the Study:

To investigate if U-CapsNets can model human spatial information processing.
To compare the representational capabilities of U-CapsNets with human brain activity during spatial tasks.

Main Methods:

Functional magnetic resonance imaging (fMRI) was used to record brain activity during a spatial working memory task.
A U-CapsNet was trained to perform the same spatial working memory task.
Representational similarity analysis was employed to compare U-CapsNet's latent space with brain activity patterns.

Main Results:

Human-defined spatial features emerged naturally within the U-CapsNet's latent space.
U-CapsNet representations mirrored the response structures of specific brain regions.
The model captured key factors influencing human behavior in the spatial task.

Conclusions:

U-CapsNets provide a viable computational framework for understanding human spatial feature encoding.
This approach offers insights into the brain's representational format and objectives for spatial information.