scholarly journals Cortical laminar resting‐state signal fluctuations scale with the hypercapnic blood oxygenation level‐dependent response

2020 ◽  
Vol 41 (8) ◽  
pp. 2014-2027 ◽  
Author(s):  
Maria Guidi ◽  
Laurentius Huber ◽  
Leonie Lampe ◽  
Alberto Merola ◽  
Kristin Ihle ◽  
...  
Keyword(s):  
Resting State ◽  
Blood Oxygenation Level Dependent ◽  
Blood Oxygenation ◽  
Blood Oxygenation Level ◽  
Oxygenation Level ◽  
Cortical Laminar

scholarly journals Measuring vascular reactivity with resting-state blood oxygenation level-dependent (BOLD) signal fluctuations: A potential alternative to the breath-holding challenge?

2016 ◽  
Vol 37 (7) ◽  
pp. 2526-2538 ◽  
Author(s):  
Hesamoddin Jahanian ◽  
Thomas Christen ◽  
Michael E Moseley ◽  
Nicholas M Pajewski ◽  
Clinton B Wright ◽  
...  
Keyword(s):  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Vascular Reactivity ◽  
Blood Oxygenation Level Dependent ◽  
Blood Oxygenation ◽  
Breath Holding ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Blood Oxygenation Level ◽  
Oxygenation Level

Measurement of the ability of blood vessels to dilate and constrict, known as vascular reactivity, is often performed with breath-holding tasks that transiently raise arterial blood carbon dioxide (PaCO2) levels. However, following the proper commands for a breath-holding experiment may be difficult or impossible for many patients. In this study, we evaluated two approaches for obtaining vascular reactivity information using blood oxygenation level-dependent signal fluctuations obtained from resting-state functional magnetic resonance imaging data: physiological fluctuation regression and coefficient of variation of the resting-state functional magnetic resonance imaging signal. We studied a cohort of 28 older adults (69 ± 7 years) and found that six of them (21%) could not perform the breath-holding protocol, based on an objective comparison with an idealized respiratory waveform. In the subjects that could comply, we found a strong linear correlation between data extracted from spontaneous resting-state functional magnetic resonance imaging signal fluctuations and the blood oxygenation level-dependent percentage signal change during breath-holding challenge ( R2 = 0.57 and 0.61 for resting-state physiological fluctuation regression and resting-state coefficient of variation methods, respectively). This technique may eliminate the need for subject cooperation, thus allowing the evaluation of vascular reactivity in a wider range of clinical and research conditions in which it may otherwise be impractical.

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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