Hemodynamic Response Function Parameters Research Articles

Changes in hemodynamic response function components reveal specific changes in neurovascular coupling in type 2 diabetes.

Type 2 Diabetes Mellitus (T2DM) is a metabolic disease that leads to multiple vascular complications with concomitant changes in human neurophysiology, which may lead to long-term cognitive impairment, and dementia. Early impairments of neurovascular coupling can be studied using event-related functional magnetic resonance imaging (fMRI) designs. Here, we aimed to characterize the changes in the hemodynamic response function (HRF) in T2DM to probe components from the initial dip to late undershoot. We investigated whether the HRF morphology is altered throughout the brain in T2DM, by extracting several parameters of the fMRI response profiles in 141 participants (64 patients with T2DM and 77 healthy controls) performing a visual motion discrimination task. Overall, the patients revealed significantly different HRFs, which extended to all brain regions, suggesting that this is a general phenomenon. The HRF in T2DM was found to be more sluggish, with a higher peak latency and lower peak amplitude, relative slope to peak, and area under the curve. It also showed a pronounced initial dip, suggesting that the initial avidity for oxygen is not compensated for, and an absent or less prominent but longer undershoot. Most HRF parameters showed a higher dispersion and variability in T2DM. In sum, we provide a definite demonstration of an impaired hemodynamic response function in the early stages of T2DM, following a previous suggestion of impaired neurovascular coupling. The quantitative demonstration of a significantly altered HRF morphology in separate response phases suggests an alteration of distinct physiological mechanisms related to neurovascular coupling, which should be considered in the future to potentially halt the deterioration of the brain function in T2DM.

Hemodynamic response function (HRF) as a novel brain marker: Applications in subjective cognitive decline (SCD)

ObjectiveSubjective cognitive decline (SCD) is the first clinical manifestation of the Alzheimer's disease (AD) continuum. Hemodynamic response function (HRF) carries information related to brain pathology and function. The shape of the HRF can be described by three parameters: response height (RH), time-to-peak (TTP), and full-width at half-max (FWHM). We proposed and explored our two hypotheses. Hypothesis 1: HRF was pathologically related to SCD: compared with healthy controls (HC), patients with SCD show HRF aberrations. Hypothesis 2: HRF could be employed as a novel marker of brain imaging for the classification of SCD. MethodsWe used resting-state functional magnetic resonance imaging (fMRI) data and performed deconvolution to investigate the HRF parameters in 54 individuals with SCD and 64 HC. Statistical two-sample t tests were performed to investigate between-group differences in HRF parameters. Finally, we used logistic regression to construct a binary classification of SCD and HC. ResultsWe found altered HRF parameters in the SCD group compared to HC. In the brain regions with altered HRF, we found that RH and FWHM decreased in the SCD group compared to HC, while TTP increased in the SCD group. From the binary logistic regression, we found that the classification accuracy of SCD and HC was 94.07%. ConclusionThe study demonstrated altered HRF parameters in patients with SCD, which could be used as a novel marker of brain function for the classification of SCD.

Scalable surrogate deconvolution for identification of partially-observable systems and brain modeling

Objective. For many biophysical systems, direct measurement of all state-variables, in − vivo is not feasible. Thus, a key challenge in biological modeling and signal processing is to reconstruct the activity and structure of interesting biological systems from indirect measurements. These measurements are often generated by approximately linear time-invariant dynamical interactions with the hidden system and may therefore be described as a convolution of hidden state-variables with an unknown kernel. Approach. In the current work, we present an approach termed surrogate deconvolution, to directly identify such coupled systems (i.e. parameterize models). Surrogate deconvolution reframes certain non linear partially-observable identification problems, which are common in neuroscience/biology, as analytical objectives that are compatible with almost any user-chosen optimization procedure. Main results. We show that the proposed technique is highly scalable, low in computational complexity, and performs competitively with the current gold-standard in partially-observable system estimation: the joint Kalman Filters (Unscented and Extended). We show the benefits of surrogate deconvolution for model identification when applied to simulations of the Local Field Potential and blood oxygen level dependent (BOLD) signal. Lastly, we demonstrate the empirical stability of Hemodynamic Response Function (HRF) kernel estimates for Mesoscale Individualized NeuroDynamic (MINDy) models of individual human brains. The recovered HRF parameters demonstrate reliable individual variation as well as a stereotyped spatial distribution, on average. Significance. These results demonstrate that surrogate deconvolution promises to enhance brain-modeling approaches by simultaneously and rapidly fitting large-scale models of brain networks and the physiological processes which generate neuroscientific measurements (e.g. hemodynamics for BOLD fMRI).

Hemodynamic Response Function Modeling to Determine the Areas with High Blood Supply in Block-Design fMRI Experiments

Background: Determining the activated brain areas due to different activities is one of the most common targets in functional magnetic resonance imaging (fMRI) data analysis, which could be carried out by hemodynamic response function (HRF) evaluation. The HR functions reflect changes of cerebral blood flow (CBF) in response to neural activity. Objectives: In this study, five models of HRF estimation were evaluated based on a simulated dataset. Models with higher accuracy were used to determine HRF parameters of the block-design fMRI data. Methods: The fMRI data were acquired in a 3 Tesla scanner. For block-design fMR imaging, CO2 gas was administered using a face-mask under physiological monitoring. Three patients with brain tumors were scanned. The fMRI data analysis was performed using the SPM 12 MATLAB toolbox. Akaike’s information criterion (AIC), Schwarz’ Bayesian (SBC), and mean square error (MSE) criteria were used to select the best HRF estimation model. Results: In simulation studies, the estimated HRFs by the canonical HRF plus its temporal derivative (TD), finite impulse response (FIR), and inverse logistic (IL) models were almost equal to the standard HRF. Mean square error, AIC, and SBC indices were ignorable for TD, FIR, and IL models (MSE/AIC/SBC magnitudes for TD, FIR, and IL models were 0.052/-1235.1/-1223.9, 0.055/-1206.4/-1194.9, and 0.068/-1091.5/-1049.2, respectively), which indicates that these models could accurately estimate HRF in block design fMRI studies. Conclusions: The HRF models could non-invasively evaluate the change of MR signal intensity under cerebrovascular reactivity (CVR) conditions and they might be helpful to investigate changes in human cerebral blood flow.

BOLD hemodynamic response function changes significantly with healthy aging

Functional magnetic resonance imaging (fMRI) has been used to infer age-differences in neural activity from the hemodynamic response function (HRF) that characterizes the blood-oxygen-level-dependent (BOLD) signal over time. BOLD literature in healthy aging lacks consensus in age-related HRF changes, the nature of those changes, and their implications for measurement of age differences in brain function. Between-study discrepancies could be due to small sample sizes, analysis techniques, and/or physiologic mechanisms. We hypothesize that, with large sample sizes and minimal analysis assumptions, age-related changes in HRF parameters could reflect alterations in one or more components of the neural-vascular coupling system. To assess HRF changes in healthy aging, we analyzed the large population-derived dataset from the Cambridge Center for Aging and Neuroscience (CamCAN) study (Shafto et al., 2014). During scanning, 74 younger (18–30 years of age) and 173 older participants (54–74 years of age) viewed two checkerboards to the left and right of a central fixation point, simultaneously heard a binaural tone, and responded via right index finger button-press. To assess differences in the shape of the HRF between younger and older groups, HRFs were estimated using FMRIB's Linear Optimal Basis Sets (FLOBS) to minimize a priori shape assumptions. Group mean HRFs were different between younger and older groups in auditory, visual, and motor cortices. Specifically, we observed increased time-to-peak and decreased peak amplitude in older compared to younger adults in auditory, visual, and motor cortices. Changes in the shape and timing of the HRF in healthy aging, in the absence of performance differences, support our hypothesis of age-related changes in the neural-vascular coupling system beyond neural activity alone. More precise interpretations of HRF age-differences can be formulated once these physiologic factors are disentangled and measured separately.

Aberrant hemodynamic responses in autism: Implications for resting state fMRI functional connectivity studies.

Functional MRI (fMRI) is modeled as a convolution of the hemodynamic response function (HRF) and an unmeasured latent neural signal. However, HRF itself is variable across brain regions and subjects. This variability is induced by both neural and non-neural factors. Aberrations in underlying neurochemical mechanisms, which control HRF shape, have been reported in autism spectrum disorders (ASD). Therefore, we hypothesized that this will lead to voxel-specific, yet systematic differences in HRF shape between ASD and healthy controls. As a corollary, we also hypothesized that such alterations will lead to differences in estimated functional connectivity in fMRI space compared to latent neural space. To test these hypotheses, we performed blind deconvolution of resting-state fMRI time series acquired from large number of ASD and control subjects obtained from the Autism Brain Imaging Data Exchange (ABIDE) database (N = 1102). Many brain regions previously implicated in autism showed systematic differences in HRF shape in ASD. Specifically, we found that precuneus had aberrations in all HRF parameters. Consequently, we obtained precuneus-seed-based functional connectivity differences between ASD and controls using fMRI as well as using latent neural signals. We found that non-deconvolved fMRI data failed to detect group differences in connectivity between precuneus and certain brain regions that were instead observed in deconvolved data. Our results are relevant for the understanding of hemodynamic and neurochemical aberrations in ASD, as well as have methodological implications for resting-state functional connectivity studies in Autism, and more generally in disorders that are accompanied by neurochemical alterations that may impact HRF shape.

On the distinguishability of HRF models in fMRI.

Modeling the Hemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a non-linear time-invariant dynamic system. However, the identification of this dynamic system may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model estimation task. In this context, the estimation of the HRF may be seen as a problem of model falsification or invalidation, where we are interested in distinguishing among a set of eligible models of dynamic systems. Here, we propose a systematic tool to determine the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input-distinguishable systems is introduced and applied to a biophysically informed HRF model, by exploiting the structure of the underlying non-linear dynamic system. A strategy to model uncertainty in the input time-delay and magnitude is developed and its impact on the distinguishability of two physiologically plausible HRF models is assessed, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to another. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that maximizes the distinguishability of the HRF models under investigation. The proposed approach may be used to evaluate the performance of HRF model estimation techniques from fMRI data.

Chronic assessment of cerebral hemodynamics during rat forepaw electrical stimulation using functional ultrasound imaging

Functional ultrasound imaging is a method recently developed to assess brain activity via hemodynamics in rodents. Doppler ultrasound signals allow the measurement of cerebral blood volume (CBV) and red blood cells' (RBCs') velocity in small vessels. However, this technique originally requires performing a large craniotomy that limits its use to acute experiments only. Moreover, a detailed description of the hemodynamic changes that underlie functional ultrasound imaging has not been described but is essential for a better interpretation of neuroimaging data.To overcome the limitation of the craniotomy, we developed a dedicated thinned skull surgery for chronic imaging. This procedure did not induce brain inflammation nor neuronal death as confirmed by immunostaining. We successfully acquired both high-resolution images of the microvasculature and functional movies of the brain hemodynamics on the same animal at 0, 2, and 7days without loss of quality. Then, we investigated the spatiotemporal evolution of the CBV hemodynamic response function (HRF) in response to sensory-evoked electrical stimulus (1mA) ranging from 1 (200μs) to 25 pulses (5s). Our results indicate that CBV HRF parameters such as the peak amplitude, the time to peak, the full width at half-maximum and the spatial extent of the activated area increase with stimulus duration. Functional ultrasound imaging was sensitive enough to detect hemodynamic responses evoked by only a single pulse stimulus. We also observed that the RBC velocity during activation could be separated in two distinct speed ranges with the fastest velocities located in the upper part of the cortex and slower velocities in deeper layers. For the first time, functional ultrasound imaging demonstrates its potential to image brain activity chronically in small animals and offers new insights into the spatiotemporal evolution of cerebral hemodynamics.

Hemodynamic Response Function (HRF) in Epilepsy Patients with Hippocampal Sclerosis and Focal Cortical Dysplasia

Simultaneous recording of electroencephalography and functional magnetic resonance imaging (EEG-fMRI) has recently been applied for mapping the hemodynamic changes related to epileptic activity. The aim of this study is to compare the hemodynamic response function (HRF) to epileptic spikes in patients with focal cortical dysplasia (FCD) and those with hippocampal sclerosis (HS). In EEG-fMRI studies, the HRF represents the temporal evolution of blood oxygenation level-dependent signal changes. Several studies demonstrated that amplitude and latency of the HRF are variable in patients with epilepsy. However, the consistency of HRF parameters with underlying brain pathology is unknown. In this study, we examined 14 patients with FCD and 12 with unilateral HS selected from our EEG-fMRI database and compared the amplitude and latency of the HRF peak. We analyzed (1) HRFs in peak activation clusters, (2) HRFs in peak deactivation clusters, and (3) the maximum absolute responses within the EEG spike field, activation or deactivation. We found that the HRF peak amplitude in deactivation clusters was larger in the HS group than in the FCD when the deactivation occurred in default mode network (DMN) regions. This result suggests that spikes in patients with HS affect the DMN more strongly than those with FCD. However, if we focus on the maximum absolute t-value in the spike field, there is no significant difference between the two groups. The current study indicates that it is not necessary to use different HRF models for EEG-fMRI studies in patients with FCD and HS.

Modeling of the Hemodynamic Responses in Block Design fMRI Studies

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.

Empirical Markov Chain Monte Carlo Bayesian analysis of fMRI data

In this work an Empirical Markov Chain Monte Carlo Bayesian approach to analyse fMRI data is proposed. The Bayesian framework is appealing since complex models can be adopted in the analysis both for the image and noise model. Here, the noise autocorrelation is taken into account by adopting an AutoRegressive model of order one and a versatile non-linear model is assumed for the task-related activation. Model parameters include the noise variance and autocorrelation, activation amplitudes and the hemodynamic response function parameters. These are estimated at each voxel from samples of the Posterior Distribution. Prior information is included by means of a 4D spatio-temporal model for the interaction between neighbouring voxels in space and time. The results show that this model can provide smooth estimates from low SNR data while important spatial structures in the data can be preserved. A simulation study is presented in which the accuracy and bias of the estimates are addressed. Furthermore, some results on convergence diagnostic of the adopted algorithm are presented. To validate the proposed approach a comparison of the results with those from a standard GLM analysis, spatial filtering techniques and a Variational Bayes approach is provided. This comparison shows that our approach outperforms the classical analysis and is consistent with other Bayesian techniques. This is investigated further by means of the Bayes Factors and the analysis of the residuals. The proposed approach applied to Blocked Design and Event Related datasets produced reliable maps of activation.

WAVELET-BASED ESTIMATION OF HEMODYNAMIC RESPONSE FUNCTION FROM fMRI DATA

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.