The impact of temporal regularization on estimates of the BOLD hemodynamic response function: A comparative analysis

Non-parametric hemodynamic response function (HRF) estimation in noisy functional Magnetic Resonance Imaging (fMRI) plays an important role when investigating the temporal dynamic of a brain region response during activations. Assuming the drift Lipschitz continuous; a new algorithm for non-parametric HRF estimation is derived in this paper. The proposed algorithm estimates the HRF by applying a first order differencing to the fMRI time series samples. It is shown that the proposed HRF estimator is √(N) consistent. Its performance is assessed using both simulated and a real fMRI data sets obtained from an event-related fMRI experiment. The application results reveal that the proposed HRF estimation method is efficient both computationally and in term of accuracy.

Non-parametric hemodynamic response function (HRF) estimation in noisy functional near-infrared spectroscopy (fNIRS) plays an important role when investigating the temporal dynamics of a brain region response during activations. Assuming the drift Lipschitz continuous; a new algorithm for non-parametric HRF estimation from the oxygenated (HbO) and deoxygenated (HbR) fNIRS time-series is derived in this paper. The proposed algorithm estimates the HRF by applying a first order differencing to the fNIRS time series samples. It is shown that the proposed HRF estimator is √N consistent. Its performance is assessed using both simulated and a real fNIRS data set obtained from a motor activity experiment. The application results reveal that the proposed HRF estimation method is efficient both computationally and in terms of accuracy.

In functional magnetic resonance imaging (fMRI), the hemodynamic response function (HRF) represents the impulse response of the neurovascular system. Its identification is essential for a deeper understanding of the dynamics of cerebral activity. In previous papers, we developed a voxelwise approach, i.e. based on a single time-course. In this paper, we propose an extension to cope with region-based HRF estimation. We introduce a spatial homogeneous model that assumes the same HRF shape for a majority of voxels within a given region-of-interest (ROI). A least trimmed squares estimator is employed to select those voxels. A Bayesian HRF estimation is then performed with the corresponding time courses. Our approach is tested on real fMRI data to illustrate the gain in robustness achieved with the region-based estimate.

<!-- *** Custom HTML *** --> Hemodynamic response function (HRF) has played an important role in many recent functional magnetic resonance imaging (fMRI) based brain studies where the main focus is to investigate the relationship between stimuli and the neural activity. Standard statistical analysis of fMRI data usually calls for a “canonical” model of HRF, but it is uncertain how well this fits the actual HRF. The objective of this paper is to exploit the experimental designs by modeling the stimulus sequences using stochastic point processes. The identification of the stimulus-response relationship will be conducted in the frequency domain, which will be facilitated by fast Fourier transforms (FFT). The usefulness of this approach will be illustrated using both simulated and real human brain data. Under regularity conditions, it is shown that the estimated HRF possesses an asymptotic normal distribution.

In an event-related functional MRI data analysis, an accurate and robust extraction of the hemodynamic response function (HRF) and its associated statistics (e.g., magnitude, width, and time to peak) is critical to infer quantitative information about the relative timing of the neuronal events in different brain regions. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) to accurately estimate HRFs pertaining to each stimulus sequence across all voxels. MASM explicitly accounts for both spatial and temporal smoothness information, while incorporating such information to adaptively estimate HRFs in the frequency domain. One simulation study and a real data set are used to demonstrate the methodology and examine its finite sample performance in HRF estimation, which confirms that MASM significantly outperforms the existing methods including the smooth finite impulse response model, the inverse logit model and the canonical HRF.

The hemodynamic response function (HRF), reflecting cerebral blood flow in response to neural activity, plays a crucial role in the analysis of the brain data obtained by functional magnetic resonance imaging (fMRI). In this s tudy, a comparison of two s tatis tical models was performed to evaluate HRF for block design. Materials and Methods: fMRI data from 3 patients with brain tumor were taken using a 3 Tesla scanner. Analysis of fMRI data was performed by the SPM12 toolbox in MATLAB software. The AIC, SBC and MSE indices were used to select the mos t convenient HRF mode. Results: Based on the simulation data, HRF es timated by canonical HRF model plus time derivations (TD) model was more consis tent with simulated HRF. These models were evaluated on real data. The MSE, AIC and SBC indices were obtained for TD-logis tic model (IL) models (for TD and logis tic IL models; 0.052, 1235.1, 1223.9 and 0.068, -1091.5, 1049.2, respectively). Based on the average values of T, W, H and model selection indicators, IL model for es timating HRF in healthy regions of the brain and brain tumor is a more appropriate approach. Conclusion:

The hemodynamic response function (HRF) links neuronal activity to functional magnetic resonance imaging (fMRI) signals. While most fMRI studies use a canonical HRF, increasing evidence from studies of healthy subjects suggests that the HRF depends on anatomical location and disease states. Here, we investigate how HRF variability relates to anatomical location and pathology in the epileptic brain, using a large simultaneous electroencephalogram and fMRI dataset. Applying HRF deconvolution and temporal decomposition, we built the first whole-brain HRF library specific to epilepsy, identifying four distinct shape groups. We mapped HRF features across parcellations of two atlases using novel Bayesian hierarchical models. In non-epileptogenic regions, HRF shape and spatial distributions align with findings from healthy subjects. Within pathological regions, they vary significantly according to pathology. Our results indicate that HRF variability is associated with pathology, in addition to its dependence on anatomical location, motivating region- and pathology-based HRF modulation in epilepsy studies.

BOLD fMRI has become a prevalent method to study cerebral sensory processing in rodent disease models, including pain and mechanical hypersensitivity. fMRI data analysis is frequently combined with a general-linear-model (GLM) -based analysis, which uses the convolution of a hemodynamic response function (HRF) with the stimulus paradigm. However, several studies indicated that the HRF differs across species, sexes, brain structures, and experimental factors, including stimulation modalities or anesthesia, and hence might strongly affect the outcome of BOLD analyzes. While considerable work has been done in humans and rats to understand the HRF, much less is known in mice. As a prerequisite to investigate mechano-sensory processing and BOLD fMRI data in male and female mice, we (1) designed a rotating stimulator that allows application of two different mechanical modalities, including innocuous von Frey and noxious pinprick stimuli and (2) determined and statistically compared HRFs across 30 brain structures and experimental conditions, including sex and, stimulus modalities. We found that mechanical stimulation lead to brain-wide BOLD signal changes thereby allowing extraction of HRFs from multiple brain structures. However, we did not find differences in HRFs across all brain structures and experimental conditions. Hence, we computed a whole-brain mouse HRF, which is based on 88 functional scans from 30 mice. A comparison of this mouse-specific HRF with our previously reported rat-derived HRF showed significantly slower kinetics in mice. Finally, we detected pronounced differences in cerebral BOLD activation between male and female mice with mechanical stimulation, thereby exposing divergent processing of noxious and innocuous stimuli in both sexes.

In BOLD fMRI data analysis, robust and accurate estimation of the Hemodynamic Response Function (HRF) is still under investigation. Parametric methods assume the shape of the HRF to be known and constant throughout the brain, whereas non-parametric methods mostly rely on artificially increasing the signal-to-noise ratio. We extend and develop a previously proposed method that makes use of basic yet relevant temporal information about the underlying physiological process of the brain BOLD response in order to infer the HRF in a Bayesian framework. A general hypothesis test is also proposed, allowing to take advantage of the knowledge gained regarding the HRF to perform activation detection. The performances of the method are then evaluated by simulation. Great improvement is shown compared to the Maximum-Likelihood estimate in terms of estimation error, variance, and bias. Robustness of the estimators with regard to the actual noise structure or level, as well as the stimulus sequence, is also proven. Lastly, fMRI data with an event-related paradigm are analyzed. As suspected, the regions selected from highly discriminating activation maps resulting from the method exhibit a certain inter-regional homogeneity in term of HRF shape, as well as noticeable inter-regional differences.

The emerging functional MRI (magnetic resonance imaging), fMRI, imaging modality was developed to obtain non-invasive information regarding the neural processes behind pre-determined task. The data is gathered in such a way that the extraction certainty of the desired information is maximized. Still this is a difficult task due to low Signal-to-Noise Ratio (SNR), corrupting noise and artifacts from several sources. The most prevalent method, here called SPM-GLM uses a conventional statistical inference methodology based on the t-statistics, where it assumes a rather rigid shape on the BOLD hemodynamic response function (HRF), constant for the whole region of interest (ROI). A new algorithm, designed in a Bayesian framework, is presented in this paper, called SPM-MAP. The algorithm jointly detects the brain activated regions and the underlying HRF in an adaptative and local basis. This approach presents two main advantages: (1) the activity detection benefits from the method's high flexibility toward the HRF shape; (2) it provides local estimations for the HRF. The SPM-MAP algorithm is validated by using Monte Carlo tests with synthetic data and comparisons with the SPM-GLM are also performed. Tests using real data are also performed and results are compared with the ones provided by the SPM-GLM method tuned by the medical doctor.

.SignificanceDeaf and hard-of-hearing infants’ hemodynamic response function (HRF) has not yet been characterized. However, without an appropriate estimate of their HRF, neuroimaging modalities relying on hemodynamic responses, e.g., functional magnetic resonance imaging (fMRI) or functional near-infrared spectroscopy (fNIRS), cannot be used reliably in this population, e.g., prior to and following cochlear implantation. We contribute to better theoretical models of and more suitable therapeutic interventions for the neural changes induced by auditory and language deprivation in deaf infants.AimWe aim to characterize the parameters of the HRF of deaf and hard-of-hearing infants in response to Italian.ApproachWe measured 2- to 20-month-old Italian-exposed infants’ HRF to Italian using fNIRS in the bilateral temporal, i.e., auditory, cortices. We characterized the HRF for all infants and for three clinically relevant subsets: (i) monolingual Italian infants, (ii) genetically deaf infants, and (iii) infants aged 5 to 12 months. We computed the following parameters: peak amplitude, time-to-peak, full width at half maximum, and where present, the amplitude and latency of the initial dip and/or the final undershoot, using a model-based parameter fitting approach. We statistically compared these HRF parameters to those of typically hearing infants.ResultsDeaf and typically hearing infants showed largely similar HRFs, with both groups reaching comparable main peak amplitudes. Minor differences have been found in the latencies of some response components.ConclusionsOur results provide the first detailed characterization of the hemodynamic response to native-language speech in deaf and hard-of-hearing infants, improving clinical and therapeutic approaches through more accurate analysis of fMRI and fNIRS data.

The hemodynamic response function (HRF) describes the dynamic process of the blood oxygen level dependent (BOLD) signal evoked by a very short stimulus of unit intensity. Understanding HRF is a key issue for exploring the underlying dynamics of nervous system activation. Some researches suggested that the shape of HRF varied with different brain regions and different subjects, even at different time point in the same brain region of a subject. In this work, epilepsy discharges were adopted as markers of short stimuli, and then we use EEG-fMRI method to investigate HRF in temporal lobe epilepsy with interictal regional slow wave. Five patients with idiopathic temporal lobe epilepsy were enrolled in this study, the peak delay and amplitude of the estimated HRF was compared with Gamma, Glover and the SPM canonical HRF, and the results show that the delay of the estimated HRF is different from either one of the three theoretical model, thus further study on both theoretical model and real data estimation is needed.

Identification of the hemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is essential for a better understanding of cerebral activity. In a given region-of-interest (ROI) of the brain, several works have demonstrated that neighbor voxels have close shape responses, but with potentially different magnitude. This property of similarity also holds between HRFs corresponding to different stimuli in the paradigm. In this paper, we account for these features in a given ROI and estimate a single HRF shape but several magnitude coefficients per voxel and condition, which may be thought of representing the neural response level. Identified as a semiblind deconvolution inverse problem, we use a Gibbs sampling approach to estimate the unknown quantities. Our method, validated both on synthetic and real fMRI data provides spatial activation maps in a given ROI with no assumptions on the exact shape of the HRF.

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

The hemodynamic response to somatosensory stimulation in mice depends on the anesthetic used: Implications on analysis of mouse fMRI data