Zobrazeno 1 - 10
of 15
pro vyhledávání: '"V. D. Calhoun"'
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cebd40d1d40d014f43fe84e5a8d50e68
https://doi.org/10.1109/icassp49357.2023.10095816
https://doi.org/10.1109/icassp49357.2023.10095816
Autor:
P. Boonyakitanont, B. Gabrielson, I. Belyaeva, P. Olikkal, J. Songsiri, Y. P. Wang, T. W. Wilson, V. D. Calhoun, J. M. Stephen, T. Adali
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2022
This paper proposes an independent component analysis (ICA)-based framework for exploring associations between neural signals measured with magnetoencephalography (MEG) and non-neuroimaging data of healthy subjects. Our proposed framework contains me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a370e631fa1893d0dfd7d895370118c
https://pubmed.ncbi.nlm.nih.gov/36086046
https://pubmed.ncbi.nlm.nih.gov/36086046
Autor:
S. M. Motlaghian, A. Belger, J. R. Bustillo, A. Faghiri, J. M. Ford, A. Iraji, K. Lim, D. H. Mathalon, R. Miller, B. A. Mueller, D. O’Leary, G. Pearlson, S. G. Potkin, A. Preda, T.G. van Erp, V. D. Calhoun
Most dynamic functional connectivity in fMRI data is focused on linear correlations, and to our knowledge, no study has studied whole brain explicitly nonlinear dynamic relationships within the data. While some approaches have attempted to study over
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3667e8f6718f0e4ab806ee16550cbc08
https://doi.org/10.1101/2022.06.22.497262
https://doi.org/10.1101/2022.06.22.497262
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::964522971f48ee30c954fc762aab2e75
https://doi.org/10.1109/icassp43922.2022.9747224
https://doi.org/10.1109/icassp43922.2022.9747224
Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data has proven useful in providing a fully multivariate summary that can be used for multiple purposes. ICA can identify patterns that can discriminat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb4b9f7c2e638d121b934470238593df
Autor:
I. F. Akyildiz, M. Pierobon, S. Balasubramaniam, J. Zhang, T. Chen, S. Zhong, J. Wang, W. Zhang, X. Zuo, R. G. Maunder, L. Hanzo, J. Chen, J. Liu, V. D. Calhoun, A. B. Magoun
Publikováno v:
Proceedings of the IEEE. 107:866-867
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0497d91c1b068a8f2b2cae086e889064
https://doi.org/10.1109/jproc.2019.2913137
https://doi.org/10.1109/jproc.2019.2913137
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Publikováno v:
Magnetic Resonance in Medicine. 48:180-192
In BOLD fMRI a series of MR images is acquired and examined for task-related amplitude changes. These functional changes are small, so it is important to maximize detection efficiency. Virtually all fMRI processing strategies utilize magnitude inform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c9f36e5d985a958eef8fba62f73b53c
https://doi.org/10.1002/mrm.10202
https://doi.org/10.1002/mrm.10202
Publikováno v:
NeuroImage. 20(3)
Independent component analysis (ICA), a data-driven approach utilizing high-order statistical moments to find maximally independent sources, has found fruitful application in functional magnetic resonance imaging (fMRI). A limitation of the standard
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::60c838ddc4e0c1909f0012b755d74bed
https://pubmed.ncbi.nlm.nih.gov/14642476
https://pubmed.ncbi.nlm.nih.gov/14642476