Computed diffusion contribution in the complex blood oxygenation-level dependent fMRI signal

2012 ◽  
Vol 40A (3) ◽  
pp. 128-145 ◽  
Author(s):  
Zikuan Chen ◽  
Vince D. Calhoun
Keyword(s):  
Blood Oxygenation Level Dependent ◽  
Blood Oxygenation ◽  
Fmri Signal ◽  
Blood Oxygenation Level ◽  
Oxygenation Level

scholarly journals Canonical EEG Microstates Transitions Reflect Switching Among BOLD Resting State Networks and Predict fMRI Signal

2021 ◽  
Author(s):  
Obada Al Zoubi ◽  
Ahmad Mayeli ◽  
Masaya Misaki ◽  
Aki Tsuchiyagaito ◽  
Vadim Zotev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Blood Oxygenation Level Dependent ◽  
Blood Oxygenation ◽  
Learning Approaches ◽  
Resting State Networks ◽  
Functional Interpretation ◽  
Fmri Signal ◽  
Blood Oxygenation Level ◽  
Oxygenation Level

Abstract Objective. Electroencephalography microstates (EEG-ms), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. EEG-ms topographies are quasi-stable lasting between 60-120 milliseconds. Some evidence suggests that EEG-ms are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of EEG-ms and their association with functional MRI (fMRI) remains unclear. Approach. In a large cohort of healthy subjects (n = 52), we conducted several statistical and machine learning approaches analyses on the association among EEG-ms spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and machine learning approaches. Main results. Our results using a generalized linear model unraveled that EEG-ms transitions were largely and negatively associated with blood-oxygenation-level dependent (BOLD) signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between EEG-ms transitioning and fMRI signal while revealing that EEG-ms dynamics can predict BOLD signals and vice versa. Significance. Results suggest that EEG-ms transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities can measure common aspects of undergoing brain neuronal activities. Moreover, our results may help to better understand the electrophysiological interpretation of EEG-ms and solve several contradicting findings in the literature.

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