EEG-fMRI and Physiological Coupling During Short Eyes-Open Resting-State Scans
Poster No:
2120
Submission Type:
Abstract Submission
Authors:
Kadir Berat Yıldırım1, Lina Alqam2, Kübra Eren2, Elif Can1, Cem Karakuzu3, Belal Tavashi4, Alp Dincer5, Pınar Özbay4
Institutions:
1Boğaziçi University, İstanbul, Turkey, 2Boğaziçi University, İstanbul, İstanbul, 3Bogazici University, Istanbul, Turkey, 4Boğaziçi University, Istanbul, Istanbul, 5Acibadem University, Istanbul, Istanbul
First Author:
Co-Author(s):
Introduction:
Drowsiness affects brain activity and physiology, complicating the interpretation of resting-state fMRI scans. Using simultaneous EEG-fMRI, we aim to investigate how drowsiness, as characterized by EEG alpha power dynamics, influence brain connectivity and physiological responses, including heart rate, pulse amplitude (PPG-AMP), and respiratory variability (RV), in eyes-open short resting-state scans.
Methods:
EEG-fMRI data during 6 min resting state scans were collected on a 3T MR scanner from N=11 subjects in total of 25 runs using GE-EPI (flip angle = 90°, TR = 3s, TE = 36 ms, in-plane resolution = 2.5 mm, 135 TRs; EEG cap with 32-channel, Brain Products). fMRI data preprocessing followed the AFNI recommended 'afni_proc' pipeline, including motion regression. Physiological signals were obtained using a fingertip pulse oximeter for PPG and respiratory bellows for respiration. Gradient artifacts in EEG data were removed using Brain Products Analyzer's template-based average artifact subtraction (AAS) method, downsampling data to 250 Hz. R-peaks in ECG were semi-automatically identified, followed by cardioballistic artifact removal, and EEG data were band-pass filtered between 0.2 and 35 Hz, with Fz as the reference. EEG data from channels F3, F4, O1, and O2 were processed to calculate spectral power within two intervals: alpha (8–12 Hz) and a broader 2–12 Hz band. To classify drowsiness, we compared the relative alpha power within 8–12 Hz to the total power in 2–12 Hz, defining subjects as awake (Group A) if alpha power exceeded 50% of the broader band, and drowsy (Group B) otherwise. We conducted time-lagged correlation analyses between HR (Chang et al. 2009), PPG-AMP (Özbay et al. 2018), and RVT (Birn et al. 2009) and various brain regions, and EEG alpha power. Connectivity matrices were computed for correlations between brain regions.
Results:
In the drowsy group, stronger HR correlations were observed in the visual cortex (+1 lag, p=0.029), anterior cingulate cortex (0 lag, p=0.038), and angular gyrus (+1 lag, p=0.011). Additionally, PPG-AMP showed lowered negative correlations with the visual cortex. Awake subjects exhibited stronger RV correlations in posterior cingulate cortex (-1 lag, p=0.003), and angular gyrus (-1 lag, p=0.002), with significant RV correlations clustered in DMN regions (Fig. 1).
During drowsiness, we observed decreased RVT and fMRI correlations, though a negative correlation remains in sensory regions. In PPG-AMP & fMRI correlation maps, we observed a negative correlation in grey matter (e.g. visual), which is less pronounced in the drowsy group, likely reflecting heightened sympathetic activity - increased sympathetic activation towards drowsiness and light sleep induce vasoconstriction, and creates PPG-AMP & fMRI-GM positive correlations. Additionally, stronger correlations between HRV and fMRI signals are observed in the drowsy group, particularly in the visual cortex. At later lags, systemic white matter-gray matter (WM-GM) (Özbay et al., 2018) patterns emerge, highlighting the influence of autonomic nervous system during or in transition to drowsiness, which may be better picked by HRV. Connectivity matrices revealed reduced fMRI connectivity across brain regions in the drowsy group, indicating decreased inter-regional synchronization (Fig. 2)
During drowsiness, we observed decreased RVT and fMRI correlations, though a negative correlation remains in sensory regions. In PPG-AMP & fMRI correlation maps, we observed a negative correlation in grey matter (e.g. visual), which is less pronounced in the drowsy group, likely reflecting heightened sympathetic activity - increased sympathetic activation towards drowsiness and light sleep induce vasoconstriction, and creates PPG-AMP & fMRI-GM positive correlations. Additionally, stronger correlations between HRV and fMRI signals are observed in the drowsy group, particularly in the visual cortex. At later lags, systemic white matter-gray matter (WM-GM) (Özbay et al., 2018) patterns emerge, highlighting the influence of autonomic nervous system during or in transition to drowsiness, which may be better picked by HRV. Connectivity matrices revealed reduced fMRI connectivity across brain regions in the drowsy group, indicating decreased inter-regional synchronization (Fig. 2)
·Figure 1.
·Figure 2.
Conclusions:
The findings demonstrate how drowsiness alters the coupling between physiological signals and brain activity, underscoring the complexity of interpreting resting-state fMRI data in such states. Furthermore, the transitional dynamics of drowsiness likely contribute to the observed shifts in physiological and neural correlations. These results emphasize the importance of accounting for autonomic dynamics and their temporal patterns to improve the accuracy of resting-state fMRI interpretations.
Acknowledgments: This study is funded by TUBITAK 2232 grant (121C120).
Acknowledgments: This study is funded by TUBITAK 2232 grant (121C120).
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
EEG
Multi-Modal Imaging
Perception, Attention and Motor Behavior:
Sleep and Wakefulness
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals 1
Keywords:
Cerebro Spinal Fluid (CSF)
Data analysis
Electroencephaolography (EEG)
ELECTROPHYSIOLOGY
FUNCTIONAL MRI
Other - physiology
1|2Indicates the priority used for review
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Provide references using APA citation style.
Birn et al., Neuroimage 2008
Özbay et al., Neuroimage 2018
Özbay et al., Neuroimage 2019