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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: Jun 5, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

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Published on: May 12, 2019

Unsupervised method for representation transfer from one brain to another.

Daiki Nakamura¹, Shizuo Kaji², Ryota Kanai³

¹Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan.

Frontiers in Neuroinformatics

|December 13, 2024

Summary

This summary is machine-generated.

This study introduces a novel unsupervised method for brain representation transfer, enabling the translation of neural data between individuals without requiring corresponding labels. This technique facilitates the reapplication of existing brain decoding models to new datasets and individuals.

Keywords:

artificial neural networks brain-computer interface brain-machine interface brain-to-brain communication fMRI image reconstruction representation transfer

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Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

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Last Updated: Jun 5, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

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Published on: May 12, 2019

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Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Area of Science:

Neuroscience
Computational Neuroscience
Machine Learning

Background:

Neural information representation varies significantly across individuals.
Decoding brain activity often requires personalized models, limiting data reusability.
Brain-to-brain communication and cross-subject model application necessitate effective data translation.

Purpose of the Study:

To develop an unsupervised method for brain representation transfer between individuals.
To enable the transformation of neural data representations without corresponding label information.
To demonstrate the applicability of the method for re-purposing existing brain decoding models.

Main Methods:

Developed an algorithm for brain representation transfer using non-linear dimensional reduction via encoders.
Utilized rotational and reflectional transformations on low-dimensional hyperspheres to capture common similarity structures.
Validated the method using artificial neural network data and functional magnetic resonance imaging (fMRI) data from human participants.

Main Results:

Successfully performed unsupervised brain representation transfer, achieving transformations comparable to supervised methods in some cases.
Demonstrated the ability to reconstruct images from individual brain data without personalized decoders.
Validated the method's effectiveness on both simulated neural activity and real fMRI data.

Conclusions:

The proposed unsupervised transfer method is effective for re-applying participant-specific models to decode neural information from other individuals.
This methodology serves as a proof of concept for exchanging latent neural information properties across individuals.
Findings support the potential for broader application in personalized neuroscience and brain-computer interfaces.