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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 Stimulation (TMS).

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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

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Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation.

Xia Liu¹, Guowei Zheng², Iman Beheshti³

¹School of Management Science and Information Engineering, Hebei University of Economics and Businesses, Shijiazhuang 050061, China.

|January 8, 2025

Summary

This study introduces a novel low-rank tensor fusion method to integrate multimodal brain imaging data for more accurate brain age estimation. The approach enhances deep learning models, showing promising results in predicting brain age.

Keywords:

brain age deep learning low-rank tensor fusion machine learning multimodal spatial–temporal

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Area of Science:

Neuroimaging
Artificial Intelligence
Biomedical Engineering

Background:

Brain age estimation offers insights into neurodevelopment and aging.
Integrating multimodal neuroimaging data (sMRI, DTI, MEG) is challenging for accurate brain age prediction.
Deep learning frameworks require effective data fusion techniques.

Purpose of the Study:

To develop and evaluate an innovative data fusion technique for multimodal brain age estimation.
To enhance the accuracy of brain age prediction using deep learning.
To explore the utility of low-rank tensor fusion for integrating diverse neuroimaging data.

Main Methods:

Developed a novel data fusion technique using a low-rank tensor fusion algorithm.
Integrated structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and magnetoencephalography (MEG) data.
Applied the fused features within a deep learning framework for brain age estimation.

Main Results:

The developed prediction model demonstrated desirable accuracy on independent test samples.
The low-rank tensor fusion effectively integrated spatial-temporal brain features.
The approach showed robust performance in brain age estimation.

Conclusions:

Low-rank tensor fusion is a promising technique for integrating multimodal neuroimaging data.
This method can enhance deep learning frameworks for brain age estimation.
The findings support the potential of multimodal data integration for understanding brain aging.