Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
501-03-004 ISMRM Abstract

Bridging Temporal Scales: A Data-Driven Integration of MEG and fMRI

Primary: Brain Function and fMRI - fMRI Analysis
Secondary: Analysis Methods - Classification and Prediction
501-03-004 · fMRI: Advanced Analysis · Wednesday, 13 May, 1:40 PM–3:30 PM · Hall 1A
Keywords: Brain function BOLD-fMRI Temporal resolution Transfer learning Machine learning (ML)
Accepted
Jiri Benacek1, Krish Singh1, Derek K Jones2,3,4,5, David Marshall6, Simon Rushton7, Marco Palombo 1,3,5,6,8,9
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
2CUBRIC, School of Psychology, United Kingdom
3Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
4School of Psychology, Cardiff University, Cardiff, United Kingdom
5School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
6School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
7Cardiff University, Cardiff, United Kingdom
8CUBRIC, Cardiff University, Cardiff, United Kingdom
9School of Computer Science and Informatics, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
Presenting Author: Marco Palombo

Synopsis

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References

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2. Z.-Q. Liu, G. Shafiei, S. Baillet, and B. Misic, “Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks,” NeuroImage, vol. 278, p. 120276, 2023.
3. D. Rangaprakash, R. L. Barry, and G. Deshpande, “The confound of hemodynamic response function variability in human resting-state functional mri studies,” Frontiers in Neuroscience, vol. 17, p. 934138,2023.
4. K. D. Singh, “Which “neural activity” do you mean? fmri, meg, oscillations and neurotransmitters,” Neuroimage, vol. 62, no. 2, pp. 1121–1130, 2012.
5. J. Daunizeau, H. Laufs, and K. J. Friston, “Eeg–fmri information fusion: biophysics and data analysis,” EEG-fMRI: Physiological Basis, Technique, and Applications, pp. 511–526, 2010.
6. Benacek et al. "Enhancing brain activity mapping through BOLD-fMRI and MEG data fusion using explainable machine learning." ISMRM 2025 Abstract. 2025.
7. M. G. Wilson, B. Broccoli, N. Purvis, R. Wade, and J. Logan, Skyfall. Sony Pictures Entertainment, 2012.
8. C. F. Beckmann and S. M. Smith, “Tensorial extensions of independent component analysis for multi- subject fmri analysis,” Neuroimage, vol. 25, no. 1, pp. 294–311, 2005.
9. T. Chen and C. Guestrin, “Xgboost. a scalable tree boosting system. arxiv. 2016; 1603.02754 v3,” 2023.
10. S. M. Smith, P. T. Fox, K. L. Miller, D. C. Glahn, P. M. Fox, C. E. Mackay, N. Filippini, K. E. Watkins, R. Toro, A. R. Laird, et al., “Correspondence of the brain’s functional architecture during activation and rest,” Proceedings of the national academy of sciences, vol. 106, no. 31, pp. 13040–13045, 2009.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/501-03-004