scholarly journals Spreading Codes Enables the Blind Estimation of the Hemodynamic Response with Short-Events Sequences

2015 ◽  
Vol 25 (01) ◽  
pp. 1450035 ◽  
Author(s):  
M. A. Lopez-Gordo ◽  
D. Sánchez-Morillo ◽  
Marcel A. J. Van Gerven
Keyword(s):  
Finite Impulse Response ◽  
Hemodynamic Response ◽  
Training Data ◽  
Fmri Data ◽  
Blind Estimation ◽  
Hemodynamic Response Function ◽  
Data Set ◽  
Spreading Codes ◽  
Scanning Time ◽  
General Terms

Finite impulse response (FIR) filters are considered the least constrained option for the blind estimation of the hemodynamic response function (HRF). However, they have a tendency to yield unstable solutions in the case of short-events sequences. There are solutions based on regularization, e.g. smooth FIR (sFIR), but at the cost of a regularization penalty and prior knowledge, thus breaking the blind principle. In this study, we show that spreading codes (scFIR) outperforms FIR and sFIR in short-events sequences, thus enabling the blind and dynamic estimation of the HRF without numerical instabilities and the regularization penalty. The scFIR approach was applied in short-events sequences of simulated and experimental functional magnetic resonance imaging (fMRI) data. In general terms, scFIR performed the best with both simulated and experimental data. While FIR was unable to compute the blind estimation of two simulated target HRFs for the shortest sequences (15 and 31 events) and sFIR yielded shapes barely correlated with the targets, scFIR achieved a normalized correlation coefficient above 0.9. Furthermore, scFIR was able to estimate in a responsive way dynamic changes of the amplitude of a simulated target HRF more accurately than FIR and sFIR. With experimental fMRI data, the ability of scFIR to estimate the real HRF obtained from a training data set was superior in terms of correlation and mean-square error. The use of short-events sequences for the blind estimation of the HRF could benefit patients in terms of scanning time or intensity of magnetic field in clinical tests. Furthermore, short-events sequences could be used, for instance, on the online detection of rapid shifts of visual attention that, according to literature, entails rapid changes in the amplitude of the HRF.

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

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 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.

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