Characterizing Autonomic Contributions to fMRI-BOLD and fMRI-EEG Dynamics in a Cognitive Task
Poster No:
1052
Submission Type:
Abstract Submission
Authors:
Kübra Eren1, Lina Alqam1, Belal Tavashi2, Kadir Berat Yıldırım3, Elif Can3, Cem Karakuzu4, Alp Dincer5, Pınar Özbay2
Institutions:
1Boğaziçi University, İstanbul, İstanbul, 2Boğaziçi University, Istanbul, Istanbul, 3Boğaziçi University, İstanbul, Turkey, 4Bogazici University, Istanbul, Turkey, 5Acibadem University, Istanbul, Istanbul
First Author:
Co-Author(s):
Introduction:
The autonomic nervous system (ANS) and brain interact at systemic and neural levels to adapt to internal and external demands. Isolating the systemic and neural effects of the ANS on BOLD signals is essential for understanding fMRI-EEG dynamics. Research on autonomic contributions to fMRI BOLD and fMRI-EEG dynamics (e.g., sequence of coupled events in BOLD signal and EEG power bands) during resting state and sleep has revealed insights into the brain's adaptations to fluctuations in arousal and physiological measures like respiratory volume, heart rate, and vascular tone (e.g., Özbay et al., 2019,Gu et al.,202). However, autonomic contributions during tasks with cognitive demands remain underexplored. This study aims to isolate and characterize ANS contributions to fMRI BOLD and cortical arousal-related fMRI-EEG (global alpha power) dynamics using autonomic markers during a mental arithmetic task.
Methods:
Building on our OHBM 2024 work, physiological data (PPG, 400Hz; respiration, 400Hz), 3T fMRI (TR: 3s, TE: 36ms, spatial resolution: 2.5mm), and EEG (band-pass filtered 0.5–35 Hz) were collected during a block-design mental arithmetic task (See Figure 1) from 8 healthy participants (5 for EEG). PPG amplitude (0.5–2 Hz bandpass) was calculated per TR (Özbay et al., 2018), and RVT was computed using RetroTS.m (Birn et al., 2006) and resampled to match fMRI data. EEG preprocessing included gradient and cardioballistic artifact removal, ICA, and referencing to Fz. Power Spectral Density (PSD) of continuous EEG data is computed using the Welch method by applying Fourier transforms to 3-second Hamming-windowed segments, squaring and isolating alpha (8–12 Hz) band. Preprocessing of fMRI included motion correction, RETROICOR (4 respiration and 4 cardiac regressors per slice), slice-timing correction, MNI registration, 3mm smoothing, and signal scaling, with motion outliers removed (threshold: 0.2) in AFNI (Cox,1996).
Nuisance regression models were generated in 3dDeconvolve to assess each regressor's contribution to the fMRI signal. All models included 6 motion regressors as baseline and the task design convolved with the canonical HRF and its derivatives as regressors of interest. Additional autonomic measures were added as baseline regressors: (1) none (R), (2) RVT (R+RVT, with 12s, 15s shifts), (3) PPG amplitude (R+PPG-AMP, with 0s, 3s shifts), and (4) both (R+RVT+PPG-AMP). Model fits were compared to the BOLD signal via event-locked time series, and group-level statistics were generated (3dttest++). For further analysis, autonomic regressors were regressed out from the BOLD signal (3dTproject). Residual signals underwent cross-correlation analysis with global EEG alpha power (gAP), and 3D maps were created to examine spatiotemporal characteristics of BOLD-gAP relationships.
Nuisance regression models were generated in 3dDeconvolve to assess each regressor's contribution to the fMRI signal. All models included 6 motion regressors as baseline and the task design convolved with the canonical HRF and its derivatives as regressors of interest. Additional autonomic measures were added as baseline regressors: (1) none (R), (2) RVT (R+RVT, with 12s, 15s shifts), (3) PPG amplitude (R+PPG-AMP, with 0s, 3s shifts), and (4) both (R+RVT+PPG-AMP). Model fits were compared to the BOLD signal via event-locked time series, and group-level statistics were generated (3dttest++). For further analysis, autonomic regressors were regressed out from the BOLD signal (3dTproject). Residual signals underwent cross-correlation analysis with global EEG alpha power (gAP), and 3D maps were created to examine spatiotemporal characteristics of BOLD-gAP relationships.
Results:
Group statistics revealed that task-correlated activity patterns shifted when autonomic signals were included as baseline regressors. For example, visual, thalamus, and insular activation decreased, while IPS remained relatively unchanged (Figure 1). Models incorporating autonomic signals, particularly vascular tone, showed slightly improved fits to the BOLD signals in task-related regions (e.g., Visual region, IPS). Cross-correlation patterns between gAP and BOLD (gray matter average and voxel-wise) varied across subjects, with the correction for vascular tone revealing a shift in trend (Figure 2).
Conclusions:
This study highlights the distinct and overlapping influences of sympathetic vasoconstriction and respiratory volume on fMRI BOLD signals and cortical arousal-related fMRI-EEG dynamics during cognitive tasks. Incorporating autonomic regressors, particularly vascular tone, enhanced the modeling of task-related fMRI signals. The observed effects of autonomic correction on global alpha-BOLD correlations suggest autonomic contributions to cortical arousal-related fMRI-EEG dynamics during cognitive tasks.
Acknowledgements: This study is funded by TUBITAK 2232 grant (121C120).
Acknowledgements: This study is funded by TUBITAK 2232 grant (121C120).
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Exploratory Modeling and Artifact Removal
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics 2
Neurophysiology of Imaging Signals
Keywords:
Cognition
Electroencephaolography (EEG)
FUNCTIONAL MRI
Other - Autonomic Nervous System
1|2Indicates the priority used for review
·Figure 1.
·Figure 2.
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Provide references using APA citation style.
Cox, R. W. (1996). AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Computers and Biomedical Research, 29, 162-173.
Gu, Y., Han, F., Sainburg, L. E., Schade, M. M., Buxton, O. M., Duyn, J. H., & Liu, X. (2022). An orderly sequence of autonomic and neural events at transient arousal changes. NeuroImage, 264, 119720.
Özbay, P. S., Chang, C., Picchioni, D., Mandelkow, H., Moehlman, T. M., Chappel-Farley, M. G., ... & Duyn, J. H. (2018). Contribution of systemic vascular effects to fMRI activity in white matter. Neuroimage, 176, 541-549.
Özbay, P. S., Chang, C., Picchioni, D., Mandelkow, H., Chappel-Farley, M. G., van Gelderen, P., ... & Duyn, J. (2019). Sympathetic activity contributes to the fMRI signal. Communications biology, 2(1), 421.