Projects per year
Hemodynamic modeling is used to explore the origin, predict, and analyze the power spectrum of the resting-state blood-oxygen-level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI), which has been reported to have a power-law form, i.e., P(f)f-s, where P(f) is the power, f is the frequency, and s>0 is the power-law exponent. However, current fMRI experimental paradigms have limited acquisition durations, affecting the spectral resolution of fMRI data at the low-frequency regime. Here, the claimed power-law spectrum is investigated by using a recent hemodynamic model to analytically derive the BOLD power spectrum, with parameters that are related to neurophysiology. The theoretical results show that, for all realistic parameter combinations, the BOLD power spectrum is flat at f0.01Hz, has a weak resonance originating from intrinsic oscillations of vasodilatory response, and becomes a power law for high frequencies, all of which is in agreement with an empirical data set that describes the spectrum of one subject and brain region. However, the results are contrary to studies reporting a pure power-law spectrum at f0.2Hz. The discrepancy is attributed largely to data averaging employed by current approaches that averages together important properties of the BOLD power spectrum, such as its resonance, that biases the spectrum to only show a power law. Data averaging also reduces the high-frequency power-law exponent relative to individual cases. Overall, this work demonstrates how the model can reproduce BOLD dynamics and further analyze its low-frequency behavior. Moreover, it also uses the model to explain the impact of procedures, such as data averaging, on the reported features of the BOLD power spectrum.
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Egan, G., Rosa, M., Lowery, A., Stuart, G., Arabzadeh, E., Skafidas, E., Ibbotson, M., Petrou, S., Paxinos, G., Mattingley, J., Garrido, M., Sah, P., Robinson, P. A., Martin, P., Grunert, U., Tanaka, K., Mitra, P., Johnson, G., Diamond, M., Margrie, T., Leopold, D., Movshon, J., Markram, H., Victor, J., Hill, S. & Jirsa, V.
Australian National University , ETH Zurich, Australian Research Council (ARC), Karolinska Institute, QIMR Berghofer Medical Research Institute, Ecole Polytechnique Federale de Lausanne (Swiss Federal Institute of Technology in Lausanne) , Monash University, University of Melbourne, University of New South Wales, University of Queensland , University of Sydney, Monash University – Internal University Contribution, National Institutes of Health (United States), Cornell University, New York University, MRC National Institute for Medical Research, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Duke University, Cold Spring Harbor Laboratory, RIKEN
25/06/14 → 31/12/21