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 Modeling of the Hemodynamic Responses in Block Design fMRI Studies

2013 ◽  
Vol 34 (2) ◽  
pp. 316-324 ◽  
Author(s):  
Zuyao Y Shan ◽  
Margaret J Wright ◽  
Paul M Thompson ◽  
Katie L McMahon ◽  
Gabriella G A M Blokland ◽  
...  
Keyword(s):  
Response Function ◽  
Goodness Of Fit ◽  
Block Design ◽  
Onset Time ◽  
Hemodynamic Response ◽  
Fmri Data ◽  
Parameter Estimates ◽  
Block Designs ◽  
Hemodynamic Response Function ◽  
Time To Peak

The hemodynamic response function (HRF) describes the local response of brain vasculature to functional activation. Accurate HRF modeling enables the investigation of cerebral blood flow regulation and improves our ability to interpret fMRI results. Block designs have been used extensively as fMRI paradigms because detection power is maximized; however, block designs are not optimal for HRF parameter estimation. Here we assessed the utility of block design fMRI data for HRF modeling. The trueness (relative deviation), precision (relative uncertainty), and identifiability (goodness-of-fit) of different HRF models were examined and test–retest reproducibility of HRF parameter estimates was assessed using computer simulations and fMRI data from 82 healthy young adult twins acquired on two occasions 3 to 4 months apart. The effects of systematically varying attributes of the block design paradigm were also examined. In our comparison of five HRF models, the model comprising the sum of two gamma functions with six free parameters had greatest parameter accuracy and identifiability. Hemodynamic response function height and time to peak were highly reproducible between studies and width was moderately reproducible but the reproducibility of onset time was low. This study established the feasibility and test–retest reliability of estimating HRF parameters using data from block design fMRI studies.

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 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.

Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function

NeuroImage ◽  
2014 ◽  
Vol 94 ◽  
pp. 203-215 ◽  
Author(s):  
K.M. Aquino ◽  
P.A. Robinson ◽  
M.M. Schira ◽  
M. Breakspear
Keyword(s):  
Response Function ◽  
Hemodynamic Response ◽  
Neural Dynamics ◽  
Fmri Data ◽  
Hemodynamic Response Function

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

WAVELET-BASED ESTIMATION OF HEMODYNAMIC RESPONSE FUNCTION FROM fMRI DATA

2006 ◽  
Vol 16 (02) ◽  
pp. 125-138 ◽  
Author(s):  
R. SRIKANTH ◽  
A. G. RAMAKRISHNAN
Keyword(s):  
Response Function ◽  
Wavelet Transforms ◽  
Sampling Rate ◽  
Hemodynamic Response ◽  
Fmri Data ◽  
Wavelet Domain ◽  
Hemodynamic Response Function ◽  
Vanishing Moments ◽  
Nonparametric Models ◽  
Performance Results

We present a new algorithm to estimate hemodynamic response function (HRF) and drift components of fMRI data in wavelet domain. The HRF is modeled by both parametric and nonparametric models. The functional Magnetic resonance Image (fMRI) noise is modeled as a fractional brownian motion (fBm). The HRF parameters are estimated in wavelet domain by exploiting the property that wavelet transforms with a sufficient number of vanishing moments decorrelates a fBm process. Using this property, the noise covariance matrix in wavelet domain can be assumed to be diagonal whose entries are estimated using the sample variance estimator at each scale. We study the influence of the sampling rate of fMRI time series and shape assumption of HRF on the estimation performance. Results are presented by adding synthetic HRFs on simulated and null fMRI data. We also compare these methods with an existing method,1 where correlated fMRI noise is modeled by a second order polynomial functions.

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