A comparative study of one-level and two-level semiparametric estimation of hemodynamic response function for fMRI data

2007 ◽  
Vol 26 (21) ◽  
pp. 3845-3861 ◽  
Author(s):  
C. M. Zhang ◽  
Y. Jiang ◽  
T. Yu
Keyword(s):  
Comparative Study ◽  
Response Function ◽  
Semiparametric Estimation ◽  
Hemodynamic Response ◽  
Fmri Data ◽  
Hemodynamic Response Function

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 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.

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.

Using voxel-specific hemodynamic response function in EEG-fMRI data analysis

NeuroImage ◽  
2006 ◽  
Vol 32 (1) ◽  
pp. 238-247 ◽  
Author(s):  
Yingli Lu ◽  
Andrew P. Bagshaw ◽  
Christophe Grova ◽  
Eliane Kobayashi ◽  
François Dubeau ◽  
...  
Keyword(s):  
Data Analysis ◽  
Response Function ◽  
Hemodynamic Response ◽  
Fmri Data ◽  
Hemodynamic Response Function ◽  
Fmri Data Analysis

scholarly journals Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis

2017 ◽  
Author(s):  
Josef Wilzén ◽  
Anders Eklund ◽  
Mattias Villani
Keyword(s):  
Gaussian Process ◽  
Response Function ◽  
Brain Activity ◽  
Hemodynamic Response ◽  
Joint Estimation ◽  
Fmri Data ◽  
Hemodynamic Response Function ◽  
Lti System ◽  
Proposed Model ◽  
Physiological Information

AbstractInference from fMRI data faces the challenge that the hemodynamic system, that relates the underlying neural activity to the observed BOLD fMRI signal, is not known. We propose a new Bayesian model for task fMRI data with the following features: (i) joint estimation of brain activity and the underlying hemodynamics, (ii) the hemodynamics is modeled nonparametrically with a Gaussian process (GP) prior guided by physiological information and (iii) the predicted BOLD is not necessarily generated by a linear time-invariant (LTI) system. We place a GP prior directly on the predicted BOLD time series, rather than on the hemodynamic response function as in previous literature. This allows us to incorporate physiological information via the GP prior mean in a flexible way. The prior mean function may be generated from a standard LTI system, based on a canonical hemodynamic response function, or a more elaborate physiological model such as the Balloon model. This gives us the nonparametric flexibility of the GP, but allows the posterior to fall back on the physiologically based prior when the data are weak. Results on simulated data show that even with an erroneous prior for the GP, the proposed model is still able to discriminate between active and non-active voxels in a satisfactory way. The proposed model is also applied to real fMRI data, where our Gaussian process model in several cases finds brain activity where previously proposed LTI models, parametric and nonparametric, does not.

Using voxel-specific hemodynamic response function in EEG-fMRI data analysis: An estimation and detection model

NeuroImage ◽  
2007 ◽  
Vol 34 (1) ◽  
pp. 195-203 ◽  
Author(s):  
Yingli Lu ◽  
Christophe Grova ◽  
Eliane Kobayashi ◽  
François Dubeau ◽  
Jean Gotman
Keyword(s):  
Data Analysis ◽  
Response Function ◽  
Hemodynamic Response ◽  
Fmri Data ◽  
Hemodynamic Response Function ◽  
Detection Model ◽  
Fmri Data Analysis

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 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.

Sign in / Sign up

Export Citation Format

Share Document