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

Most existing analytical techniques for EEG-fMRI data need specific assumptions about the hemodynamic response function (HRF). These assumptions may not be appropriate when the HRF varies from subject to subject or from region to region. In this article, we introduce a deconvolution method for EEG-fMRI activation detection, which can be implemented with voxel-specific HRFs. A comparison of performance is made between three fixed HRFs and the deconvolution method under the framework of the general linear model. The main results are as follows: (1) the volume of detected regions from the deconvolved HRFs is larger. (2) In some subjects, the deconvolution technique can find areas of activation that have not been detected with the three fixed HRFs at our threshold of significance. (3) Deconvolution obtained higher adjusted coefficients of multiple determination compared to those obtained with the three fixed HRFs. The results suggest that the fixed HRF methods may not be the most appropriate for the analysis of epileptic activity with EEG-fMRI, and the deconvolution method may be a better choice.     

Using patient-specific hemodynamic response functions in combined EEG-fMRI studies in epilepsy

Combining electroencephalogram (EEG) and functional MRI (fMRI) allows localization of brain regions activated as a result of epileptic spikes. The statistical analysis of fMRI data usually includes a standard model of the hemodynamic response function (HRF) but it is not known how well this fits the actual HRF of epileptic spikes. The objective of this exploratory study was to compare the activated areas and t-statistical scores obtained with a standard HRF to those obtained with a patient-specific HRF. Eight patients with focal epilepsy were studied. We obtained an estimate of the patient-specific HRFs for each patient at the local maximum of activation in the standard HRF analysis. The activated areas obtained with the patient-specific HRFs were larger or similar to the originally activated areas. Additional activated areas were seen in five patients, and most were compatible with the EEG and anatomical MRI localization of epileptogenic and lesional regions. Using patient-specific HRFs brings increased sensitivity to the analysis of epileptic spikes by EEG-fMRI.     

Using nonlinear models in fMRI data analysis: model selection and activation detection

There is an increasing interest in using physiologically plausible models in fMRI analysis. These models do raise new mathematical problems in terms of parameter estimation and interpretation of the measured data. In this paper, we show how to use physiological models to map and analyze brain activity from fMRI data. We describe a maximum likelihood parameter estimation algorithm and a statistical test that allow the following two actions: selecting the most statistically significant hemodynamic model for the measured data and deriving activation maps based on such model. Furthermore, as parameter estimation may leave much incertitude on the exact values of parameters, model identifiability characterization is a particular focus of our work. We applied these methods to different variations of the Balloon Model (Buxton, R.B., Wang, E.C., and Frank, L.R. 1998. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn. Reson. Med. 39: 855-864; Buxton, R.B., Uluda?, K., Dubowitz, D.J., and Liu, T.T. 2004. Modelling the hemodynamic response to brain activation. NeuroImage 23: 220-233; Friston, K. J., Mechelli, A., Turner, R., and Price, C. J. 2000. Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics. NeuroImage 12: 466-477) in a visual perception checkerboard experiment. Our model selection proved that hemodynamic models better explain the BOLD response than linear convolution, in particular because they are able to capture some features like poststimulus undershoot or nonlinear effects. On the other hand, nonlinear and linear models are comparable when signals get noisier, which explains that activation maps obtained in both frameworks are comparable. The tools we have developed prove that statistical inference methods used in the framework of the General Linear Model might be generalized to nonlinear models.     

Simultaneous intracranial EEG-fMRI in humans: Protocol considerations and data quality

We have recently performed simultaneous intracranial EEG and fMRI recordings (icEEG-fMRI) in patients with epilepsy. In this technical note, we examine limited thermometric data for potential electrode heating during our protocol and found that heating was ≤0.1°C in-vitro at least 10 fold less than in-vivo limits. We quantify EEG quality, which can be degraded by MRI scanner-induced artefacts, and fMRI image (gradient echo echo-planar imaging: GE-EPI) signal quality around the electrodes, which can be degraded by electrode interactions with B1 (radiofrequency) and B0 (static) magnetic fields. We recorded EEG outside and within the MRI scanner with and without scanning. EEG quality was largely preserved during scanning and in particular heartbeat-related artefacts were small compared to epileptic events. To assess the GE-EPI signal reduction around the electrodes, we compared image signal intensity along paths into the brain normal to its surface originating from the individual platinum-iridium electrode contacts. GE-EPI images were obtained at 1.5T with an echo time (TE) of 40ms and repetition time (TR) of 3000ms and a slice thickness of 2.5mm. We found that GE-EPI signal intensity reduction was confined to a 10mm radius and that it was reduced on average by less than 50% at 5mm from the electrode contacts. The GE-EPI image signal reduction also varied with electrode orientation relative to the MRI scanner axes; in particular, cortical grid contacts with a normal along the scanner's main magnetic field (B(0)) axis have higher artefact levels relative to those with a normal perpendicular to the z-axis. This suggests that the artefacts were predominantly susceptibility-related rather than due to B1 interactions. This information can be used to guide interpretation of results of icEEG-fMRI experiments proximal to the electrodes, and to optimise artefact reduction strategies.     

Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties

In this paper, we present a framework to estimate local ventricular myocardium contractility using clinical MRI, a heart model and data assimilation. First, we build a generic anatomical model of the ventricles including muscle fibre orientations and anatomical subdivisions. Then, this model is deformed to fit a clinical MRI, using a semi-automatic fuzzy segmentation, an affine registration method and a local deformable biomechanical model. An electromechanical model of the heart is then presented and simulated. Finally, a data assimilation procedure is described, and applied to this model. Data assimilation makes it possible to estimate local contractility from given displacements. Presented results on fitting to patient-specific anatomy and assimilation with simulated data are very promising. Current work on model calibration and estimation of patient parameters opens up possibilities to apply this framework in a clinical environment.     

Neuronal chronometry of target detection: fusion of hemodynamic and event-related potential data

Event-related potential (ERP) studies of the brain's response to infrequent, target (oddball) stimuli elicit a sequence of physiological events, the most prominent and well studied being a complex, the P300 (or P3) peaking approximately 300 ms post-stimulus for simple stimuli and slightly later for more complex stimuli. Localization of the neural generators of the human oddball response remains challenging due to the lack of a single imaging technique with good spatial and temporal resolution. Here, we use independent component analyses to fuse ERP and fMRI modalities in order to examine the dynamics of the auditory oddball response with high spatiotemporal resolution across the entire brain. Initial activations in auditory and motor planning regions are followed by auditory association cortex and motor execution regions. The P3 response is associated with brainstem, temporal lobe, and medial frontal activity and finally a late temporal lobe "evaluative" response. We show that fusing imaging modalities with different advantages can provide new information about the brain.     

Hemodynamic response function in patients with stroke-induced aphasia: implications for fMRI data analysis

Functional MRI is based on changes in cerebral microvasculature triggered by increased neuronal oxidative metabolism. This change in blood flow follows a pattern known as the hemodynamic response function (HRF), which typically peaks 4-6 s following stimulus delivery. However, in the presence of cerebrovascular disease the HRF may not follow this normal pattern, due to either the temporal signal to noise (tSNR) ratio or delays in the HRF, which may result in misinterpretation or underestimation of fMRI signal. The present study examined the HRF and SNR in five individuals with aphasia resulting from stroke and four unimpaired participants using a lexical decision task and a long trial event-related design. T1-weighted images were acquired using an MP-RAGE sequence and BOLD T2*-weighted images were acquired using Echo Planar Imaging to measure time to peak (TTP) in the HRF. Data were analyzed using Brain Voyager in four anatomic regions known to be involved in language processing: Broca's area and the posterior perisylvian network (PPN) (including Wernicke's area, the angular and supramarginal gyri) and right hemisphere homologues of these regions. The occipital area also was examined as a control region. Analyses showed that the TTP in three out of five patients in the left perisylvian area was increased significantly as compared to normal individuals and the left primary visual cortex in the same patients. In two other patients no significant delays were detected. We also found that the SNR for BOLD signal detection may by insufficient in damaged areas. These findings indicate that obtaining physiologic (TTP) and quality assurance (tSNR) information is essential for studying activation patterns in brain-damaged patients in order to avoid errors in interpretation of the data. An example of one such misinterpretation and the need for alternative data analysis strategies is discussed.     

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

In fMRI data analysis it has been shown that for a wide range of situations the hemodynamic response function (HRF) can be reasonably characterized as the impulse response function of a linear and time invariant system. An accurate and robust extraction of the HRF is essential to infer quantitative information about the relative timing of the neuronal events in different brain regions. When no assumptions are made about the HRF shape, it is most commonly estimated using time windowed averaging or a least squares estimated general linear model based on either Fourier or delta basis functions. Recently, regularization methods have been employed to increase the estimation efficiency of the HRF; typically these methods produce more accurate HRF estimates than the least squares approach [Goutte, C., Nielsen, F.A., Hansen, L.K., 2000. Modeling the Haemodynamic Response in fMRI Using Smooth FIR Filters. IEEE Trans. Med. Imag. 19(12), 1188-1201.]. Here, we use simulations to clarify the relative merit of temporal regularization based methods compared to the least squares methods with respect to the accuracy of estimating certain characteristics of the HRF such as time to peak (TTP), height (HR) and width (W) of the response. We implemented a Bayesian approach proposed by Marrelec et al. [Marrelec, G., Benali, H., Ciuciu, P., Pelegrini-Issac, M., Poline, J.-B., 2003. Robust Estimation of the Hemodynamic Response Function in Event-Related BOLD fMRI Using Basic Physiological Information. Hum. Brain Mapp. 19, 1-17., Marrelec, G., Benali, H., Ciuciu, P., Poline, J.B. Bayesian estimation of the hemodynamic of the hemodynamic response function in functional MRI. In: R. F, editor; 2001; Melville. p 229-247.] and its deterministic counterpart based on a combination of Tikhonov regularization [Tikhonov, A.N., Arsenin, V.Y., 1977. Solution of ill-posed problems. Washington DC: W.H. Winston.] and generalized cross-validation (GCV) [Wahba, G., 1990. Spline Models for Observational Data. Philadelphia: SIAM.] for selecting the regularization parameter. The performance of both methods is compared with least square estimates as a function of temporal resolution, color and strength of the noise, and the type of stimulus sequences used. In almost all situations, under the considered assumptions (e.g. linearity, time invariance and smooth HRF), the regularization-based techniques more accurately characterize the HRF compared to the least-squares method. Our results clarify the effects of temporal resolution, noise color, and experimental design on the accuracy of HRF estimation.     

A linear model for estimation of neurotransmitter response profiles from dynamic PET data

The parametric ntPET model (p-ntPET) estimates the kinetics of neurotransmitter release from dynamic PET data with receptor-ligand radiotracers. Here we introduce a linearization (lp-ntPET) that is computationally efficient and can be applied to single scan data. lp-ntPET employs a non-invasive reference region input function and extends the LSRRM of Alpert et al. (2003) using basis functions to characterize the time course of neurotransmitter activation. In simulation studies, the temporal precision of neurotransmitter profiles estimated by lp-ntPET was similar to that of p-ntPET (standard deviation ~ 3 min for responses early in the scan) while computation time was reduced by several orders of magnitude. Violations of model assumptions such as activation-induced changes in regional blood flow or specific binding in the reference tissue have negligible effects on lp-ntPET performance. Application of the lp-ntPET method is demonstrated on [¹¹C]raclopride data acquired in rats receiving methamphetamine, which yielded estimated response functions that were in good agreement with simultaneous microdialysis measurements of extracellular dopamine concentration. These results demonstrate that lp-ntPET is a computationally efficient, linear variant of ntPET that can be applied to PET data from single or multiple scan designs to estimate the time course of neurotransmitter activation.     

Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients

IntroductionSeizures occur rarely during EEG-fMRI acquisitions of epilepsy patients, but can potentially offer a better estimation of the epileptogenic zone than interictal activity. Independent component analysis (ICA) is a data-driven method that imposes minimal constraints on the hemodynamic response function (HRF). In particular, the investigation of HRFs with clear peaks, but varying latency, may be used to differentiate the ictal focus from propagated activity.MethodsICA was applied on ictal EEG-fMRI data from 15 patients. Components related to seizures were identified by fitting an HRF to the component time courses at the time of the ictal EEG events. HRFs with a clear peak were used to derive maps of significant BOLD responses and their associated peak delay. The results were then compared with those obtained from a general linear model (GLM) method. Concordance with the presumed epileptogenic focus was also assessed.ResultsThe ICA maps were significantly correlated with the GLM maps for each patient (Spearman's test, p < 0.05). The ictal BOLD responses identified by ICA always included the presumed epileptogenic zone, but were also more widespread, accounting for 20.3% of the brain volume on average. The method provided a classification of the components as a function of peak delay. BOLD response clusters associated with early HRF peaks were concordant with the suspected epileptogenic focus, while subsequent HRF peaks may correspond to ictal propagation.ConclusionICA applied to EEG-fMRI can detect areas of significant BOLD response to ictal events without having to predefine an HRF. By estimating the HRF peak time in each identified region, the method could also potentially provide a dynamic analysis of ictal BOLD responses, distinguishing onset from propagated activity.     

Investigating neural-hemodynamic coupling and the hemodynamic response function in the awake rat

An understanding of the relationship between changes in neural activity and the accompanying hemodynamic response is crucial for accurate interpretation of functional brain imaging data and in particular the blood oxygen level-dependent (BOLD) fMRI signal. Much physiological research investigating this topic uses anesthetized animal preparations, and yet, the effects of anesthesia upon the neural and hemodynamic responses measured in such studies are not well understood. In this study, we electrically stimulated the whisker pad of both awake and urethane anesthetized rats at frequencies of 1-40 Hz. Evoked field potential responses were recorded using electrodes implanted into the contralateral barrel cortex. Changes in hemoglobin oxygenation and concentration were measured using optical imaging spectroscopy, and cerebral blood flow changes were measured using laser Doppler flowmetry. A linear neural-hemodynamic coupling relationship was found in the awake but not the anesthetized animal preparation. Over the range of stimulation conditions studied, hemodynamic response magnitude increased monotonically with summed neural activity in awake, but not in anesthetized, animals. Additionally, the temporal structure of the hemodynamic response function was different in awake compared to anesthetized animals. The responses in each case were well approximated by gamma variates, but these were different in terms of mean latency (approximately 2 s awake; 4 s anesthetized) and width (approximately 0.6 s awake; 2.5 s anesthetized). These findings have important implications for research into the intrinsic signals that underpin BOLD fMRI and for biophysical models of cortical hemodynamics and neural-hemodynamic coupling.     

An outpatient segmentation model: estimation of stakeholder costs

This research describes a constraint-based heuristic model of capacity segmentation for outpatient facilities used to estimate the effect of segmentation constraints on stakeholders. Growth of free-standing ambulatory surgery centres has been dramatic in recent years with institutions being urged by governments and insurers to segment inpatients (IP) and outpatients (OP) to reduce costs and improve services. Critics of segmentation argue it is a false economy to separate inpatients and outpatients since pooling of patients in large IP facilities offers economies of scale and opportunities for parallel processing, not to mention elimination of infrastructure. We implemented a constraint-based heuristic model of capacity segmentation for OP facilities and used it to estimate the effect of segmentation on stakeholders.     

A three-compartment model of the hemodynamic response and oxygen delivery to brain

We describe a mathematical model linking changes in cerebral blood flow, blood volume and the blood oxygenation state in response to stimulation. The model has three compartments to take into account the fact that the cerebral blood flow and volume as measured concurrently using laser Doppler flowmetry and optical imaging spectroscopy have contributions from the arterial, capillary as well as the venous compartments of the vasculature. It is an extension to previous one-compartment hemodynamic models which assume that the measured blood volume changes are from the venous compartment only. An important assumption of the model is that the tissue oxygen concentration is a time varying state variable of the system and is driven by the changes in metabolic demand resulting from changes in neural activity. The model takes into account the pre-capillary oxygen diffusion by flexibly allowing the saturation of the arterial compartment to be less than unity. Simulations are used to explore the sensitivity of the model and to optimise the parameters for experimental data. We conclude that the three-compartment model was better than the one-compartment model at capturing the hemodynamics of the response to changes in neural activation following stimulation.     

Pulmonary embolism: improvement in hemodynamic function with amrinone therapy

We have described a 57-year-old man who had had lower extremity vascular surgery complicated by PE. Dyspnea, hypotension, hypoxemia, and CHF developed, but thrombolytic therapy was not started because of the recent surgical procedure; instead, he had plication of the inferior vena cava. Because of persistent pulmonary artery hypertension and decreasing cardiac indices, amrinone therapy was begun, and resulted in marked hemodynamic improvement.     

不同程度急性肺动脉栓塞犬模型右心室功能变化的实验研究

目的建立犬不同栓塞程度急性肺栓塞(APE)模型,评价血流动力学与右心室功能影像学指标之间的相关性。方法实验用犬9只,经股静脉注入条状自体新鲜血栓,通过控制血栓栓塞量,建立低危肺栓塞组(G1)、轻度次大面积肺栓塞组(G2)和重度次大面积肺栓塞组(G3)动物模型:评价栓塞后120 min血流动力学变化,同时观察CT成像及心脏超声右心功能指标变化。结果栓塞后120 min,所有实验动物平均肺动脉压(MPAP)均较基线水平显著升高,并随栓塞程度增加呈上升趋势(G1:18.0±3.6比28.7±3.5,P0.05;G2:12.3±3.1比36.0±2.0,P0.05;G3:15.3±4.0比45.3±2.5,P0.05)。肺动脉/主动脉直径(PA/AA)较基线水平均显著升高,且PA/AA值对早期肺动脉压上升表现的较为敏感。心脏超声与CT成像均显示右心室/左心室横径(RV/LV)比值随栓塞程度增加呈上升趋势,但对低危肺栓塞组超声测量数据未见显著差异。虽然在3个实验组均观察到右心室面积变化率(FAC)数值降低,但仅G3组的改变具有显著性(57.1±1.9%比41.6±0.9%,P0.05)。结论本研究建立了犬不同栓塞程度APE模型,其血流动力学和右心室功能指标指标的相关性可为相关实验研究提供数据支持。     

Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.