Neurovascular Coupling During Task Engagement and Resting State in Concurrent EEG and MRI
Poster No:
1639
Submission Type:
Abstract Submission
Authors:
Yi-Chia Kung1, Chia-Wei Li2, Ai-Ling Hsu3, Chi-Yun Liu4, Changwei Wu4,5, Wei-Chou Chang1, Ching-Po Lin6
Institutions:
1Department of Radiology, Tri-service General Hospital, National Defense Medical Center, Taipei, Taiwan, 2Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan, 3Chang Gung University, Taoyuan, Taiwan, 4Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan, 5Research Center of Sleep Medicine, Taipei Medical University Hospital, Taipei, Taiwan, 6Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
First Author:
Yi-Chia Kung
Department of Radiology, Tri-service General Hospital, National Defense Medical Center
Taipei, Taiwan
Department of Radiology, Tri-service General Hospital, National Defense Medical Center
Taipei, Taiwan
Co-Author(s):
Chi-Yun Liu
Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University
Taipei, Taiwan
Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University
Taipei, Taiwan
Changwei Wu
Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University|Research Center of Sleep Medicine, Taipei Medical University Hospital
Taipei, Taiwan|Taipei, Taiwan
Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University|Research Center of Sleep Medicine, Taipei Medical University Hospital
Taipei, Taiwan|Taipei, Taiwan
Wei-Chou Chang
Department of Radiology, Tri-service General Hospital, National Defense Medical Center
Taipei, Taiwan
Department of Radiology, Tri-service General Hospital, National Defense Medical Center
Taipei, Taiwan
Introduction:
Neurovascular coupling constitutes a pivotal neurophysiological mechanism within functional neuroimaging, which traditionally posited to exhibit robustness across physiological states, encompassing both task engagement and resting states. However, recent evidence suggests that neurovascular may exhibit state-dependent variability in humans [1]. By employing concurrent EEG and fMRI or neurovascular coupling, we investigated the cross-frequency spectral correspondence in two distinct states: Eyes-Open-Eyes-Close (EOEC) task and resting.
Methods:
The simultaneous EEG-fMRI data were collected from 14 young participants (7 females; mean age=25.6±3.9) using 3T SIEMENS Trio MRI with a 32-channel MRI-compatible system (Brain Products, Gilching, Germany). The functional data (TR/TE/FA=2500ms/30ms/80°, FOV=220, matrix size=64x64, 35 slices with 3.4 mm thickness) were collected under EOEC task and a resting state. The EOEC protocol comprised three blocks, each featuring a 30-second epoch of both eye-open and eye-closed states.
We used Hilbert-Huang Transformations (HHT) with amplitude-amplitude coupling (AAC) and phase-amplitude coupling (PAC) analysis to explore neurovascular coupling. The HHT [2] entailed: (1) decomposition of the dataset into intrinsic mode functions (IMFs), (2) calculating the instantaneous frequency in IMFs, and (3) presenting spectral information through Hilbert spectral analysis (HSA).
PAC was calculated via cross-frequency correlations between the IMFs of fMRI signals and the HSAs of EEG signals, while AAC was determined through correlations across HSA maps. Group-level PAC/AAC across IMFs or frequency bands was evaluated using one-sample t-tests.
We used Hilbert-Huang Transformations (HHT) with amplitude-amplitude coupling (AAC) and phase-amplitude coupling (PAC) analysis to explore neurovascular coupling. The HHT [2] entailed: (1) decomposition of the dataset into intrinsic mode functions (IMFs), (2) calculating the instantaneous frequency in IMFs, and (3) presenting spectral information through Hilbert spectral analysis (HSA).
PAC was calculated via cross-frequency correlations between the IMFs of fMRI signals and the HSAs of EEG signals, while AAC was determined through correlations across HSA maps. Group-level PAC/AAC across IMFs or frequency bands was evaluated using one-sample t-tests.
Results:
For EOEC task, as shown in Figure 1, the activated cluster in the occipital lobe was selected from fMRI signal for neurovascular coupling analysis with EEG-Oz signal. In the PAC between EEG and fMRI, significant negative correlation (Bonferroni-corrected p < 0.05) emerged between theta/alpha-bands of EEG and the IMFs (0.008-0.031 Hz) of fMRI signal during the EOEC task. In the AAC between EEG and fMRI, beta- and gamma-bands of EEG showed significant positive correlation with fMRI signal in 0.025-0.05 and 0.1-0.125 Hz frequency ranges, respectively, during the EOEC-task performed.
In resting-state, as shown in Figure 2, no significant PAC correlation was found between EEG signal from the Oz channel and fMRI signals. However, fMRI signal in the relative higher frequency band (0.15-0.25 Hz) displayed a significant positive AAC with the gamma band of the EEG-Oz channel (Bonferroni corrected p<0.05).
In resting-state, as shown in Figure 2, no significant PAC correlation was found between EEG signal from the Oz channel and fMRI signals. However, fMRI signal in the relative higher frequency band (0.15-0.25 Hz) displayed a significant positive AAC with the gamma band of the EEG-Oz channel (Bonferroni corrected p<0.05).
·Figure 1. The EEG-fMRI spectral correspondence in eye-open-eye-close (EOEC) task.
·Figure 2. The EEG-fMRI spectral coupling of the visual network in the resting state.
Conclusions:
Our study examined the neurovascular coupling through the EEG-fMRI spectral correspondence in both EOEC and resting conditions. During the EOEC task, we observed a negative PAC between alpha-band EEG oscillations and the relevant fMRI fluctuation of visual activation. In the resting state, AAC of EEG-fMRI was present between gamma-band of EEG-Oz and the high-frequency band fMRI signals within the visual network. This dichotomy may stem from the nonlinear characteristic of resting-state fluctuations, distinct from those observed in task engagement [3]. Further, PAC affords insight into the temporal dynamics of neural oscillations, while AAC provides insight into the intensity relationship between different rhythms [4].
To sum, the divergent findings between EOEC and resting states intimate that neurovascular coupling via EEG-fMRI synchronization may be contingent on cognitive or physiological states rather than representing a static relationship. These revelations hold significant import for the comprehension of brain functionality and the refinement of neuroimaging data interpretation.
To sum, the divergent findings between EOEC and resting states intimate that neurovascular coupling via EEG-fMRI synchronization may be contingent on cognitive or physiological states rather than representing a static relationship. These revelations hold significant import for the comprehension of brain functionality and the refinement of neuroimaging data interpretation.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Task-Independent and Resting-State Analysis 2
Keywords:
Electroencephaolography (EEG)
FUNCTIONAL MRI
Other - neurovascular coupling
1|2Indicates the priority used for review
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[2] Huang, N. E. (1998), 'The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis.' Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–95.
[3] Gultepe, E., and Bin H. (2013). 'A Linear/Nonlinear Characterization of Resting State Brain Networks in fMRI Time Series.' Brain Topography, vol. 26, no. 1, pp. 39–49.
[4] Bruns, A. and Reinhard E. (2004), 'Task-Related Coupling from High- to Low-Frequency Signals among Visual Cortical Areas in Human Subdural Recordings.', International Journal of Psychophysiology, vol. 51, no. 2, pp. 97–116.