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Sensitivity of the resting-state haemodynamic response function estimation to autonomic nervous system fluctuations

Guorong Wu (UGent) and Daniele Marinazzo (UGent)
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The integrative neuroscience of behavioral control (Neuroscience)
Abstract
The haemodynamic response function (HRF) is a key component of the blood oxygen level-dependent (BOLD) signal, providing the mapping between neural activity and the signal measured with functional magnetic resonance imaging (fMRI). Most of the time the HRF is associated with task-based fMRI protocols, in which its onset is explicitly included in the design matrix. On the other hand, the HRF also mediates the relationship between spontaneous neural activity and the BOLD signal in resting-state protocols, in which no explicit stimulus is taken into account. It has been shown that resting-state brain dynamics can be characterized by looking at sparse BOLD 'events', which can be retrieved by point process analysis. These events can be then used to retrieve the HRF at rest. Crucially, cardiac activity can also induce changes in the BOLD signal, thus affecting both the number of these events and the estimation of the haemodynamic response. In this study, we compare the resting-state haemodynamic response retrieved by means of a point process analysis, taking the cardiac fluctuations into account. We find that the resting-state HRF estimation is significantly modulated in the brainstem and surrounding cortical areas. From the analysis of two high-quality datasets with different temporal and spatial resolution, and through the investigation of intersubject correlation, we suggest that spontaneous point process response durations are associated with the mean interbeat interval and low-frequency power of heart rate variability in the brainstem.
Keywords
PHYSIOLOGICAL NOISE, BOLD SIGNAL, SPONTANEOUS BRAIN ACTIVITY, HEART-RATE-VARIABILITY, FMRI DATA, TEMPORAL DYNAMICS, CONNECTIVITY MRI, MOTION, NETWORKS, METAANALYSIS, resting state, functional magnetic resonance imaging, haemodynamic response, point process, cardiac fluctuations

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Citation

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MLA
Wu, Guorong, and Daniele Marinazzo. “Sensitivity of the Resting-state Haemodynamic Response Function Estimation to Autonomic Nervous System Fluctuations.” PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 374.2067 (2016): n. pag. Print.
APA
Wu, G., & Marinazzo, D. (2016). Sensitivity of the resting-state haemodynamic response function estimation to autonomic nervous system fluctuations. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 374(2067).
Chicago author-date
Wu, Guorong, and Daniele Marinazzo. 2016. “Sensitivity of the Resting-state Haemodynamic Response Function Estimation to Autonomic Nervous System Fluctuations.” Philosophical Transactions of the Royal Society A-mathematical Physical and Engineering Sciences 374 (2067).
Chicago author-date (all authors)
Wu, Guorong, and Daniele Marinazzo. 2016. “Sensitivity of the Resting-state Haemodynamic Response Function Estimation to Autonomic Nervous System Fluctuations.” Philosophical Transactions of the Royal Society A-mathematical Physical and Engineering Sciences 374 (2067).
Vancouver
1.
Wu G, Marinazzo D. Sensitivity of the resting-state haemodynamic response function estimation to autonomic nervous system fluctuations. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES. 2016;374(2067).
IEEE
[1]
G. Wu and D. Marinazzo, “Sensitivity of the resting-state haemodynamic response function estimation to autonomic nervous system fluctuations,” PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, vol. 374, no. 2067, 2016.
@article{7174286,
  abstract     = {The haemodynamic response function (HRF) is a key component of the blood oxygen level-dependent (BOLD) signal, providing the mapping between neural activity and the signal measured with functional magnetic resonance imaging (fMRI). Most of the time the HRF is associated with task-based fMRI protocols, in which its onset is explicitly included in the design matrix. On the other hand, the HRF also mediates the relationship between spontaneous neural activity and the BOLD signal in resting-state protocols, in which no explicit stimulus is taken into account. It has been shown that resting-state brain dynamics can be characterized by looking at sparse BOLD 'events', which can be retrieved by point process analysis. These events can be then used to retrieve the HRF at rest. Crucially, cardiac activity can also induce changes in the BOLD signal, thus affecting both the number of these events and the estimation of the haemodynamic response. In this study, we compare the resting-state haemodynamic response retrieved by means of a point process analysis, taking the cardiac fluctuations into account. We find that the resting-state HRF estimation is significantly modulated in the brainstem and surrounding cortical areas. From the analysis of two high-quality datasets with different temporal and spatial resolution, and through the investigation of intersubject correlation, we suggest that spontaneous point process response durations are associated with the mean interbeat interval and low-frequency power of heart rate variability in the brainstem.},
  articleno    = {20150190},
  author       = {Wu, Guorong and Marinazzo, Daniele},
  issn         = {1364-503X},
  journal      = {PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES},
  keywords     = {PHYSIOLOGICAL NOISE,BOLD SIGNAL,SPONTANEOUS BRAIN ACTIVITY,HEART-RATE-VARIABILITY,FMRI DATA,TEMPORAL DYNAMICS,CONNECTIVITY MRI,MOTION,NETWORKS,METAANALYSIS,resting state,functional magnetic resonance imaging,haemodynamic response,point process,cardiac fluctuations},
  language     = {eng},
  number       = {2067},
  title        = {Sensitivity of the resting-state haemodynamic response function estimation to autonomic nervous system fluctuations},
  url          = {http://dx.doi.org/10.1098/rsta.2015.0190},
  volume       = {374},
  year         = {2016},
}

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