Signature of EEG-based Beta Bursts on Hemodynamic Responses during Rest and Motor Tasks
Poster No:
1696
Submission Type:
Abstract Submission
Authors:
Siyu Long1, Georgios Mitsis2, Marie-Hélène Boudrias3
Institutions:
1Integrated Program in Neuroscience, McGill University, Montreal, Quebec, 2Department of Bioengineering, McGill University, Montreal, Quebec, 3School of Physical and Occupational Therapy, McGill University, Montreal, Quebec
First Author:
Co-Author(s):
Marie-Hélène Boudrias
School of Physical and Occupational Therapy, McGill University
Montreal, Quebec
School of Physical and Occupational Therapy, McGill University
Montreal, Quebec
Introduction:
Motor control relies on synchronized beta oscillations (13-30 Hz) between populations of neurons in the sensorimotor system, which can be captured by Electroencephalography (EEG) (Feingold et al., 2015). Rather than occurring continuously, these oscillations manifest as transient bursts of high-amplitude activity that are crucial in regulating movement velocity and motor performance (Little et al., 2019; Torrecillos et al., 2018). Modulation of neural activity during movement is also known to induce concurrent changes in Blood Oxygenation Level Dependent (BOLD) signals, as measured by functional magnetic resonance imaging (fMRI) (Jasdzewski et al., 2003). Simultaneous EEG-fMRI recordings have revealed temporal correspondence between brain oscillations and hemodynamic responses (Hunyadi et al., 2019). However, the relationship between transient beta bursts and BOLD signals remains poorly understood. Here, we aim to characterize this relationship by estimating the hemodynamic response function (HRF) to EEG-based beta bursts and evaluating its predictive performance in terms of BOLD signal fluctuations.
Methods:
Eleven participants (aged 20-29 years) underwent simultaneous EEG-fMRI recordings during a 10-minute unilateral hand grip task and a 10-minute resting-state session. A high-resolution T1-weighted structural MRI scan was also acquired for spatial co-registration of EEG and fMRI data. Both EEG and fMRI data were preprocessed using standard pipelines. EEG source reconstruction was performed using the Brainstorm software following recommended procedures (Tadel et al., 2011), and source data were parcellated using the Desikan-Killiany-Tourville (DKT) atlas (Klein & Tourville, 2012).
To extract beta bursts, the aperiodic component of the power spectral density was subtracted from time-frequency matrix, with negative values set to zero (Szul et al., 2023). The beta burst detection threshold was set to twice the standard deviation of each trial's adjusted time-frequency matrix. The resulting matrices were binarized and combined, retaining only continuous beta bursts lasting over 100 milliseconds. Finally, the Spherical Laguerre basis function set was used to estimate the HRF between beta bursts and the BOLD signal in each brain region of the DKT atlas (Leistedt & McEwen, 2012; Prokopiou et al., 2022).
To extract beta bursts, the aperiodic component of the power spectral density was subtracted from time-frequency matrix, with negative values set to zero (Szul et al., 2023). The beta burst detection threshold was set to twice the standard deviation of each trial's adjusted time-frequency matrix. The resulting matrices were binarized and combined, retaining only continuous beta bursts lasting over 100 milliseconds. Finally, the Spherical Laguerre basis function set was used to estimate the HRF between beta bursts and the BOLD signal in each brain region of the DKT atlas (Leistedt & McEwen, 2012; Prokopiou et al., 2022).
Results:
First, we assessed the performance of spherical Laguerre functions in terms of BOLD signal prediction. Predictions using the original beta bursts showed significantly higher correlation with the observed BOLD signals than those using surrogate (shuffled) beta burst time-series (p < 0.0001), indicating robust predictive power (Fig. 1A). Fig. 1B illustrates a representative HRF curve estimated from beta bursts during a unimanual motor task, while Fig. 1C shows a segment for which strong correlation (r = 0.500, p < 0.0001) was achieved between the predicted and observed BOLD signals. These results confirmed the model's effectiveness in capturing BOLD dynamics with beta bursts as input.
Fig. 2 shows that in most cortical regions, negative HRF responses were observed consistently across both resting and motor task states. However, in the contralateral motor regions (left precentral and postcentral regions), beta bursts elicited distinct responses, showing positive HRF peaks and areas during the motor task but negative responses at rest, highlighting the unique role of the motor areas in task-specific hemodynamic dynamics. Meanwhile, HRF power remained consistent across resting and task states, exhibiting minimal regional variation across the cortical surface.
Fig. 2 shows that in most cortical regions, negative HRF responses were observed consistently across both resting and motor task states. However, in the contralateral motor regions (left precentral and postcentral regions), beta bursts elicited distinct responses, showing positive HRF peaks and areas during the motor task but negative responses at rest, highlighting the unique role of the motor areas in task-specific hemodynamic dynamics. Meanwhile, HRF power remained consistent across resting and task states, exhibiting minimal regional variation across the cortical surface.
Conclusions:
Our findings demonstrate that the proposed basis expansion approach effectively modelled the dynamic relationship between beta bursts and the BOLD signal. We also highlighted the distinct signature of beta bursts on the BOLD response during task versus rest. Overall, this study advances our understanding of the relationship between fMRI signals and transient neurophysiological events.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Task-Independent and Resting-State Analysis 2
Motor Behavior:
Motor Planning and Execution 1
Novel Imaging Acquisition Methods:
BOLD fMRI
EEG
Keywords:
Electroencephaolography (EEG)
FUNCTIONAL MRI
Modeling
Motor
1|2Indicates the priority used for review
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Provide references using APA citation style.
Hunyadi, B., Woolrich, M. W., Quinn, A. J., Vidaurre, D., & De Vos, M. (2019). A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates. NeuroImage, 185, 72-82.
Jasdzewski, G., Strangman, G., Wagner, J., Kwong, K. K., Poldrack, R. A., & Boas, D. A. (2003). Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy. NeuroImage, 20(1), 479-488.
Klein, A., & Tourville, J. (2012). 101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol. Frontiers in Neuroscience, 6.
Leistedt, B., & McEwen, J. D. (2012). Exact Wavelets on the Ball. IEEE Transactions on Signal Processing, 60(12), 6257-6269.
Little, S., Bonaiuto, J., Barnes, G., & Bestmann, S. (2019). Human motor cortical beta bursts relate to movement planning and response errors. PLOS Biology, 17(10), e3000479.
Prokopiou, P. C., Xifra-Porxas, A., Kassinopoulos, M., Boudrias, M.-H., & Mitsis, G. D. (2022). Modeling the Hemodynamic Response Function Using EEG-fMRI Data During Eyes-Open Resting-State Conditions and Motor Task Execution. Brain Topography, 35(3), 302-321.
Szul, M. J., Papadopoulos, S., Alavizadeh, S., Daligaut, S., Schwartz, D., Mattout, J., & Bonaiuto, J. J. (2023). Diverse beta burst waveform motifs characterize movement-related cortical dynamics. Progress in Neurobiology, 228, 102490.
Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D., & Leahy, R. M. (2011). Brainstorm: a user-friendly application for MEG/EEG analysis. Intell. Neuroscience, 2011, Article 8.
Torrecillos, F., Tinkhauser, G., Fischer, P., Green, A. L., Aziz, T. Z., Foltynie, T., Limousin, P., Zrinzo, L., Ashkan, K., Brown, P., & Tan, H. (2018). Modulation of Beta Bursts in the Subthalamic Nucleus Predicts Motor Performance. The Journal of Neuroscience, 38(41), 8905-8917.