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Bridging Multi-Imaging Modalities Using Deep Learning for Comprehensive Insights on Resting State Brain Activities

첫 페이지 보기
  • 발행기관
    선문효정학술연구회 바로가기
  • 간행물
    The Journal of Sciences and Innovation for Sustainable Peace(구 The journal of Hyojeong Academia) 바로가기
  • 통권
    Vol. 3 No. 1 (2025.03)바로가기
  • 페이지
    pp.53-61
  • 저자
    Grace Dominique Bruno, Gyuseok Lee, Jörg Stadler, André Brechmann, Wonsang You
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A481826

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원문정보

초록

영어
In the resting state, EEG and fMRI have functional correlations in the low-frequency band, and integrating the two modalities can provide a more comprehensive understanding of brain activity. However, multimodal imaging faces challenges such as high cost and complexity of data fusion. In this study, we developed a Transformer-CNN to generate fMRI data from EEG signals and introduced spatial normalization to compensate for differences in brain structures between sub-jects. Our results showed that the brain structures were normalized to the same extent, so that the model could focus only on predicting the signal values of fMRI, and compared with actual fMRI scans, we obtained PSNR of 25.92 and SSIM of 0.56, which were quantitatively and qualitatively evaluated. Although there are some qualitative limitations for medical device utilization, our ap-proach opens new avenues in neuroscience, especially in environments where simultaneous EEG-fMRI acquisition is not possible. This study highlights the potential of deep learning in advancing multimodal imaging and provides enhanced insights into brain function.

목차

Abstract
1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Data Preprocessing
2.3. Transformer-CNN model
3. Results
4. Discussion
References

키워드

Multimodal neuroimaging deep learning EEG fMRI

저자

  • Grace Dominique Bruno [ AIIP Lab, Department of Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ]
  • Gyuseok Lee [ AIIP Lab, Department of Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ]
  • Jörg Stadler [ Combinatorial Neuroimaging Core Facility, Leibniz Institute for Neurobiology, 3918 Magdeburg, Germany ]
  • André Brechmann [ Combinatorial Neuroimaging Core Facility, Leibniz Institute for Neurobiology, 3918 Magdeburg, Germany ]
  • Wonsang You [ AIIP Lab, Department of Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    선문효정학술연구회 [Sun Moon Hyojeong Academy Society]
  • 설립연도
    2023
  • 분야
    복합학>학제간연구
  • 소개
    Journal of Hyojeong Academia aims to serve as a global platform where researchers and scholars of various disciplines can contribute ideas for our sustainable global community of Co‐existence, Co‐prosperity, and Co‐righteousness. The journal is a multidisciplinary, open‐access, internationally peer‐reviewed academic journal, and it invites all areas of research conducted in the spirit of post materialism including studies centering on God, studies unifying religions and sciences, and studies on all aspects of Co‐existence, Co‐prosperity, and Co‐righteousness.

간행물

  • 간행물명
    The Journal of Sciences and Innovation for Sustainable Peace(구 The journal of Hyojeong Academia)
  • 간기
    반년간
  • pISSN
    2982-9305
  • 수록기간
    2023~2025
  • 십진분류
    KDC 238 DDC 289

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