Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
370-03-021 ISMRM Abstract

A Rigorous Method for Measuring Power-Law Scaling Properties in fMRI Brain Signals

Primary: Analysis Methods - Software Tools
Secondary: Brain Function and fMRI - Functional Connectivity
370-03-021 · Brain Functional Methods · Monday, 11 May, 9:15 AM–10:10 AM · Traditional Posters | Exhibition Hall
Keywords: fMRI Analysis Resting-state fMRI Power-law scaling Hurst exponent Scale-free dynamics
Accepted
Erhan Asad Javed 1,2, Alexander M Weber3
1Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Vancouver, Canada
2Weber Lab, BC Children's Hospital Research Institute, Vancouver, Canada
3Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, Canada
Presenting Author: Erhan Asad Javed

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References

1. He, B. J. (2011). Scale-free properties of the functional magnetic resonance imaging signal during rest and task. Journal of Neuroscience, 31(39), 13786-13795. https://doi.org/10.1523/JNEUROSCI.2111-11.2011 [doi]
2. Thurner, S., Windischberger, C., Moser, E., Walla, P., & Barth, M. (2003). Scaling laws and persistence in human brain activity. Physica A: Statistical Mechanics and its Applications, 326(3-4), 511-521. https://doi.org/10.1016/S0378-4371(03)00279-6 [doi]
3. Davies, R. B., & Harte, D. S. (1987). Tests for Hurst effect. Biometrika, 74(1), 95-101. https://doi.org/10.1093/biomet/74.1.95 [doi]
4. Hosking, J. R. (1984). Modeling persistence in hydrological time series using fractional differencing. Water resources research, 20(12), 1898-1908. https://doi.org/10.1029/WR020i012p01898 [doi]
5. Eke, A., Hermán, P., Bassingthwaighte, J., Raymond, G., Percival, D., Cannon, M., ... & Ikrényi, C. (2000). Physiological time series: distinguishing fractal noises from motions. Pflügers Archiv, 439(4), 403-415. https://doi.org/10.1007/s004249900135 [doi]
6. Zhang, H. Y., Feng, Z. Q., Feng, S. Y., & Zhou, Y. (2024). Typical Algorithms for Estimating Hurst Exponent of Time Sequence: A Data Analyst’s Perspective. IEEE Access. doi:10.1109/ACCESS.2024.3512542 [doi]
7. Clauset, A., Shalizi, C. R., & Newman, M. E. (2009). Power-law distributions in empirical data. SIAM review, 51(4), 661-703. https://doi.org/10.1137/070710111 [doi]
8. Marshall, N., Timme, N. M., Bennett, N., Ripp, M., Lautzenhiser, E., & Beggs, J. M. (2016). Analysis of power laws, shape collapses, and neural complexity: new techniques and MATLAB support via the NCC toolbox. Frontiers in physiology, 7, 250. https://doi.org/10.3389/fphys.2016.00250 [doi]

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http://echo.ismrm.org/p/ISMRM2026/370-03-021