scholarly journals Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity

2018 ◽  
Vol 80 (4) ◽  
pp. 1697-1713 ◽  
Author(s):  
D. Rangaprakash ◽  
Guo-Rong Wu ◽  
Daniele Marinazzo ◽  
Xiaoping Hu ◽  
Gopikrishna Deshpande
Keyword(s):  
Functional Connectivity ◽  
Response Function ◽  
Resting State ◽  
Resting State Fmri ◽  
Hemodynamic Response ◽  
Hemodynamic Response Function

scholarly journals Retrieving the Hemodynamic Response Function in resting state fMRI: methodology and applications

2015 ◽  
Author(s):  
Guo-Rong Wu ◽  
Daniele Marinazzo
Keyword(s):  
Response Function ◽  
Resting State ◽  
Onset Time ◽  
Resting State Fmri ◽  
Hemodynamic Response ◽  
Fmri Data ◽  
Hemodynamic Response Function ◽  
Opening And Closing

Retrieving the hemodynamic response function (HRF) in fMRI data is important for several reasons. Apart from its use as a physiological biomarker, HRF can act as a confounder in connectivity studies. In task-based fMRI is relatively straightforward to retrieve the HRF since its onset time is known. This is not the case for resting state acquisitions. We present a procedure to retrieve the hemodynamic response function from resting state (RS) fMRI data. The fundamentals of the procedure are further validated by a simulation and with ASL data. We then present the modifications to the shape of the HRF at rest when opening and closing the eyes using a simultaneous EEG-fMRI dataset. Finally, the HRF variability is further validated on a test-retest dataset.

scholarly journals Retrieving the Hemodynamic Response Function in resting state fMRI: methodology and applications

2015 ◽  
Author(s):  
Guo-Rong Wu ◽  
Daniele Marinazzo
Keyword(s):  
Response Function ◽  
Resting State ◽  
Onset Time ◽  
Resting State Fmri ◽  
Hemodynamic Response ◽  
Fmri Data ◽  
Hemodynamic Response Function ◽  
Opening And Closing

Retrieving the hemodynamic response function (HRF) in fMRI data is important for several reasons. Apart from its use as a physiological biomarker, HRF can act as a confounder in connectivity studies. In task-based fMRI is relatively straightforward to retrieve the HRF since its onset time is known. This is not the case for resting state acquisitions. We present a procedure to retrieve the hemodynamic response function from resting state (RS) fMRI data. The fundamentals of the procedure are further validated by a simulation and with ASL data. We then present the modifications to the shape of the HRF at rest when opening and closing the eyes using a simultaneous EEG-fMRI dataset. Finally, the HRF variability is further validated on a test-retest dataset.

scholarly journals Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner

Data in Brief ◽  
2018 ◽  
Vol 17 ◽  
pp. 1175-1179 ◽  
Author(s):  
D. Rangaprakash ◽  
Guo-Rong Wu ◽  
Daniele Marinazzo ◽  
Xiaoping Hu ◽  
Gopikrishna Deshpande
Keyword(s):  
Functional Mri ◽  
Response Function ◽  
Resting State ◽  
Hemodynamic Response ◽  
Healthy Individuals ◽  
Hemodynamic Response Function ◽  
Function Data

Modeling the hemodynamic response function using EEG-fMRI data during eyes-open resting-state conditions and motor task execution

2020 ◽  
Author(s):  
Prokopis C. Prokopiou ◽  
Alba Xifra-Porxas ◽  
Michalis Kassinopoulos ◽  
Marie-Hélène Boudrias ◽  
Georgios D. Mitsis
Keyword(s):  
Response Function ◽  
Resting State ◽  
Motor Task ◽  
Field Potential ◽  
Hemodynamic Response ◽  
Bold Signal ◽  
Hemodynamic Response Function ◽  
Beta Band ◽  
Experimental Conditions ◽  
Frequency Bands

AbstractIn this work, we investigated the regional characteristics of the dynamic interactions between oscillatory sources of ongoing neural activity obtained using electrophysiological recordings and the corresponding changes in the BOLD signal using simultaneous EEG-fMRI measurements acquired during a motor task, as well as under resting conditions. We casted this problem within a system-theoretic framework, where we initially performed distributed EEG source space reconstruction and subsequently employed block-structured linear and non-linear models to predict the BOLD signal from the instantaneous power in narrow frequency bands of the source local field potential (LFP) spectrum (<100 Hz). Our results suggest that the dynamics of the BOLD signal can be sufficiently described as the convolution between a linear combination of the power profile within individual frequency bands with a hemodynamic response function (HRF). During the motor task, BOLD signal variance was mainly explained by the EEG oscillations in the beta band. On the other hand, during resting-state all frequency bands of EEG exhibited significant contributions to BOLD signal variance. Moreover, the contribution of each band was found to be region specific. Our results also revealed considerable variability of the HRF across different brain regions. Specifically, sensory-motor cortices exhibited positive HRF shapes, whereas parietal and occipital cortices exhibited negative HRF shapes under both experimental conditions.

scholarly journals Hemodynamic Response Function in Brain White Matter in a Resting State

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Ting Wang ◽  
D Mitchell Wilkes ◽  
Muwei Li ◽  
Xi Wu ◽  
John C Gore ◽  
...  
Keyword(s):  
White Matter ◽  
Response Function ◽  
Gray Matter ◽  
Resting State ◽  
Hemodynamic Response ◽  
Temporal Variations ◽  
Blood Oxygenation ◽  
Hemodynamic Response Function ◽  
Brain White Matter ◽  
Oxygenation Level

Abstract The hemodynamic response function (HRF) characterizes temporal variations of blood oxygenation level-dependent (BOLD) signals. Although a variety of HRF models have been proposed for gray matter responses to functional demands, few studies have investigated HRF profiles in white matter particularly under resting conditions. In the present work we quantified the nature of the HRFs that are embedded in resting state BOLD signals in white matter, and which modulate the temporal fluctuations of baseline signals. We demonstrate that resting state HRFs in white matter could be derived by referencing to intrinsic avalanches in gray matter activities, and the derived white matter HRFs had reduced peak amplitudes and delayed peak times as compared with those in gray matter. Distributions of the time delays and correlation profiles in white matter depend on gray matter activities as well as white matter tract distributions, indicating that resting state BOLD signals in white matter encode neural activities associated with those of gray matter. This is the first investigation of derivations and characterizations of resting state HRFs in white matter and their relations to gray matter activities. Findings from this work have important implications for analysis of BOLD signals in the brain.

scholarly journals Modeling the hemodynamic response function using simultaneous EEG-fMRI data and convolutional sparse coding analysis with rank-1 constraints

2020 ◽  
Author(s):  
Prokopis C. Prokopiou ◽  
Michalis Kassinopoulos ◽  
Alba Xifra-Porxas ◽  
Marie-Hélène Boudrias ◽  
Georgios D. Mitsis
Keyword(s):  
Response Function ◽  
Sparse Coding ◽  
Resting State ◽  
Large Amplitude ◽  
Hemodynamic Response ◽  
Fmri Data ◽  
Bold Signal ◽  
Discrete Events ◽  
Hemodynamic Response Function ◽  
Lower Amplitude

AbstractOver the last few years, an increasing body of evidence points to the hemodynamic response function as an important confound of resting-state functional connectivity. Several studies in the literature proposed using blind deconvolution of resting-state fMRI data to retrieve the HRF, which can be subsequently used for hemodynamic deblurring. A basic hypothesis in these studies is that relevant information of the resting-state brain dynamics is condensed in discrete events resulting in large amplitude peaks in the BOLD signal. In this work, we showed that important information of resting-state activity, in addition to the larger amplitude peaks, is also concentrated in lower amplitude peaks. Moreover, due to the strong effect of physiological noise and head motion on the BOLD signal, which in many cases may not be completely removed after preprocessing, the neurophysiological origin of the large amplitude BOLD signal peaks is questionable. Hence, focusing on the large amplitude BOLD signal peaks may yield biased HRF estimates. To define discrete events of neuronal origins, we proposed using simultaneous EEG-fMRI along with convolutional sparse coding analysis. Our results suggested that events detected in the EEG are able to describe the slow oscillations of the BOLD signal and to obtain consistent HRF shapes across subjects under both task-based and resting-state conditions.

scholarly journals Hemodynamic response function parameters obtained from resting-state functional MRI data in soldiers with trauma

Data in Brief ◽  
2017 ◽  
Vol 14 ◽  
pp. 558-562 ◽  
Author(s):  
D. Rangaprakash ◽  
Michael N. Dretsch ◽  
Wenjing Yan ◽  
Jeffrey S. Katz ◽  
Thomas S. Denney ◽  
...  
Keyword(s):  
Functional Mri ◽  
Response Function ◽  
Resting State ◽  
Hemodynamic Response ◽  
Hemodynamic Response Function
Sign in / Sign up

Export Citation Format

Share Document