Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis

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.

Download Full-text

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

NeuroImage ◽

10.1016/j.neuroimage.2014.03.001 ◽

2014 ◽

Vol 94 ◽

pp. 203-215 ◽

Cited By ~ 28

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

Download Full-text

Modeling of the Hemodynamic Responses in Block Design fMRI Studies

Journal of Cerebral Blood Flow & Metabolism ◽

10.1038/jcbfm.2013.200 ◽

2013 ◽

Vol 34 (2) ◽

pp. 316-324 ◽

Cited By ~ 35

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.

Download Full-text

The Trouble Interpreting fMRI Studies in Populations with Cerebrovascular Risk: The Use of a Subject-Specific Hemodynamic Response Function in a Study of Age, Vascular Risk, and Memory

10.1101/512343 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ian M. McDonough ◽

Andrew Bender ◽

Lawrence Patihis ◽

Elizabeth A. Stinson ◽

Sarah K. Letang ◽

...

Keyword(s):

Response Function ◽

Brain Activity ◽

Hemodynamic Response ◽

Memory Encoding ◽

Vascular Risk ◽

Hemodynamic Response Function ◽

Maximum Peak ◽

Brain Vasculature ◽

Subject Specific ◽

Related Design

AbstractFunctional magnetic resonance imaging (fMRI) is commonly used to investigate the neural bases of behavior ranging from basic cognitive mechanisms to aging to psychological disorders. However, the BOLD signal captured by fMRI is an indirect measure of neural function and is affected by many factors that are non-neural in origin. These non-neural factors, however, do affect brain vasculature such as the shape and timing of the hemodynamic response function (HRF) during task-evoked fMRI that, in turn, can cause inappropriate and/or misleading interpretations of fMRI differences between groups. In this study, we tested the proposition that vascular health risks, which often go unmeasured in neuroimaging studies, and aging interact to modify the shape and/or timing of the HRF (height, time-to-peak, width), which then affect the differences in patterns of brain activity in a task-evoked memory encoding paradigm. Adult participants (aged 20–74) answered questions about their health history and underwent two fMRI tasks: viewing of a flashing checkerboard using a slow event-related design and a paired associates memory encoding task during a fast event-related design. We found that aging and vascular risk had the largest impacts on the maximum peak value of the HRF. Using a subject-specific HRF resulted in an overall dampening of the estimated brain activity in both task-positive and task-negative regions due to a reduction in the inter-individual variance of that activity. Across three vascular risk factors, using a subject-specific HRF resulted in more consistent brain regions that reached significance and larger effect sizes compared with the canonical HRF. A slight advantage in the reliability of brain-behavior correlations also was found. The findings from this study have far reaching consequences for the interpretation of task-evoked fMRI activity, especially in populations known to experience alterations to brain vasculature including adults of all ages that have higher vascular risk, the majority of older adults, and people with neurocognitive disorders in which vasculature differences may play a role including dementia.HighlightsOlder age was associated with smaller maximum peak of the hemodynamic response.Younger and middle-aged adults with more vascular risk had higher HRF peaks.Using a subject-specific HRF resulted in a “dampening” of brain activity.A subject-specific HRF resulted in more consistent aging and vascular risk effects.

Download Full-text

Joint Estimation of Hemodynamic Response Function and Voxel Activation in Functional MRI Data

Lecture Notes in Computer Science - Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 ◽

10.1007/978-3-319-24553-9_18 ◽

2015 ◽

pp. 142-149 ◽

Cited By ~ 1

Author(s):

Priya Aggarwal ◽

Anubha Gupta ◽

Ajay Garg

Keyword(s):

Functional Mri ◽

Response Function ◽

Hemodynamic Response ◽

Joint Estimation ◽

Hemodynamic Response Function

Download Full-text

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

10.7287/peerj.preprints.1317v1 ◽

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.

Download Full-text

WAVELET-BASED ESTIMATION OF HEMODYNAMIC RESPONSE FUNCTION FROM fMRI DATA

International Journal of Neural Systems ◽

10.1142/s012906570600055x ◽

2006 ◽

Vol 16 (02) ◽

pp. 125-138 ◽

Cited By ~ 5

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.

Download Full-text