Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test–retest reliability and effect of fMRI sampling rate

Golestani, Ali-Mohammad; Chang, Catie; Kwinta, Jonathan B.; Khatamian, Yasha B.; Chen, Jean

doi:10.1016/j.neuroimage.2014.10.031

Cited by 118 publications

(160 citation statements)

References 54 publications

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“…The cardiac and respiratory signals were convolved with the population-average response functions described by Chang et al (44) and Brin et al (27), respectively. Recent studies (45,46) have suggested, however, that subject-specific transfer functions might remove additional signal noise. Future studies will investigate whether and how much this can benefit various sequences/ TR.…”

Section: Discussionmentioning

confidence: 99%

Influence of physiological noise on accelerated 2D and 3D resting state functional MRI data at 7 T

Reynaud

Jorge

Gruetter

et al. 2017

Magnetic Resonance in Med

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Purpose: Physiological noise often dominates the bloodoxygen level-dependent (BOLD) signal fluctuations in high-field functional MRI (fMRI) data. Therefore, to optimize fMRI protocols, it becomes crucial to investigate how physiological signal fluctuations impact various acquisition and reconstruction schemes at different acquisition speeds. In particular, further differences can arise between 2D and 3D fMRI acquisitions due to different encoding strategies, thereby impacting fMRI sensitivity in potentially significant ways. Methods: The amount of physiological noise to be removed from the BOLD fMRI signal acquired at 7 T was quantified for different sampling rates (repetition time from 3300 to 350 ms, acceleration 1 to 8) and techniques dedicated to fast fMRI (simultaneous multislice echo planar imaging [EPI] and 3D EPI). Resting state fMRI (rsfMRI) performances were evaluated using temporal signal-tonoise ratio (tSNR) and network characterization based on seed correlation and independent component analysis. Results: Overall, acceleration enhanced tSNR and rsfMRI metrics. 3D EPI benefited the most from physiological noise removal at long repetition times. Differences between 2D and 3D encoding strategies disappeared at high acceleration factors (6-to 8-fold). Conclusion: After physiological noise correction, 2D-and 3D-accelerated sequences provide similar performances at high fields, both in terms of tSNR and resting state network identification and characterization. Magn Reson Med 78:888-896,

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Section: Discussionmentioning

confidence: 99%

Influence of physiological noise on accelerated 2D and 3D resting state functional MRI data at 7 T

Reynaud

Jorge

Gruetter

et al. 2017

Magnetic Resonance in Med

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show abstract

“…Thus, the global signal is not uniform across the brain, or even across grey matter, and these observations constrain the possible sources of the global fluctuations:

Physiological effects . Substantial fMRI timeseries variance is attributable to physiological variables, including rate and depth of breathing, heart rate, and end tidal pCO2 110–112 ). Indeed, any physiological process that affects global perfusion pressure of the brain (i.e.

…”

Section: Figurementioning

confidence: 99%

“…Substantial fMRI timeseries variance is attributable to physiological variables, including rate and depth of breathing, heart rate, and end tidal pCO2 110–112 ). Indeed, any physiological process that affects global perfusion pressure of the brain (i.e.…”

Section: Figurementioning

confidence: 99%

The Human Connectome Project's neuroimaging approach

et al. 2016

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Non-invasive human neuroimaging has yielded many exciting discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis, and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The “HCP-style” paradigm has seven core tenets: (1) collect multimodal imaging data from many subjects; (2) acquire data at high spatial and temporal resolution; (3) preprocess data to minimize distortions, blurring, and temporal artifacts; (4) represent data using the natural geometry of cortical and subcortical structures; (5) accurately align corresponding brain areas across subjects and studies; (6) analyze data using neurobiologically accurate brain parcellations; and (7) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP datasets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.

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“…Alternatively, whole-brain venous oxygenation levels can be measured with a special pulse sequence and used to normalize the BOLD response [68]. A more easily applicable option is to use the amplitude of low-frequency fluctuations in resting-state fMRI data (RS-ALFF) [69,70] as a normalization factor; indeed RS-ALFF reflects naturally occurring variations in cardiac rhythm and respiratory rate and depth [71] and approximates the BOLD response to a hypercapnic challenge (Figure 3(a)). In fact, one does not even need to acquire a separate resting-state scan here.…”

Section: Validity: Are Individual Differences Attributable To Brain Fmentioning

confidence: 99%

Building a Science of Individual Differences from fMRI

Dubois

Adolphs

2016

Trends in Cognitive Sciences

592

482

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To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity and reliability that arise when the focus shifts to individual subjects and that are widely applicable across imaging modalities. We also emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability, sources of within-subject variability, and statistical power.

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Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test–retest reliability and effect of fMRI sampling rate

Cited by 118 publications

References 54 publications

Influence of physiological noise on accelerated 2D and 3D resting state functional MRI data at 7 T

Influence of physiological noise on accelerated 2D and 3D resting state functional MRI data at 7 T

The Human Connectome Project's neuroimaging approach

Building a Science of Individual Differences from fMRI

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