PHYCAA: data-driven measurement and removal of physiological noise in BOLD fMRI

The effects of physiological noise may significantly limit the reproducibility and accuracy of BOLD fMRI. However, physiological noise evidences a complex, undersampled temporal structure and is often non-orthogonal relative to the neuronally-linked BOLD response, which presents a significant challenge for identifying and removing such artifact. This paper presents a multivariate, data-driven method for the characterization and removal of physiological noise in fMRI data, termed PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis). The method identifies high frequency, autocorrelated physiological noise sources with reproducible spatial structure, using an adaptation of Canonical Correlation Analysis performed in a split-half resampling framework. The technique is able to identify physiological effects with vascular-linked spatial structure, and an intrinsic dimensionality that is task- and subject-dependent. We also demonstrate that increasing dimensionality of such physiological noise is correlated with increasing variability in externally-measured respiratory and cardiac processes. Using PHYCAA as a denoising technique significantly improves simulated signal detection with physiological noise, and real data-driven model prediction and reproducibility, for both block and event-related task designs. This is demonstrated compared to no physiological noise correction, and to the widely used RETROICOR (Glover et al., 2000) physiological denoising algorithm, which uses externally measured cardiac and respiration signals.     

Physiological noise effects on the flip angle selection in BOLD fMRI

This work addresses the choice of imaging flip angle in blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI). When noise of physiological origin becomes the dominant noise source in fMRI timeseries, it causes a nonlinear dependence of the temporal signal-to-noise ratio (TSNR) versus signal-to-noise ratio (SNR) that can be exploited to perform BOLD fMRI at angles well below the Ernst angle without any detrimental effect on our ability to detect sites of neuronal activation. We show, both experimentally and theoretically, that for situations where available SNR is high and physiological noise dominates over system/thermal noise, although TSNR still reaches it maximum for the Ernst angle, reduction of imaging flip angle well below this angle results in negligible loss in TSNR. Moreover, we provide a way to compute a suggested imaging flip angle, which constitutes a conservative estimate of the minimum flip angle that can be used under given experimental SNR and physiological noise levels. For our experimental conditions, this suggested angle equals 7.63° for the grey matter compartment, while the Ernst angle=77°. Finally, using data from eight subjects with a combined visual-motor task we show that imaging at angles as low as 9° introduces no significant differences in observed hemodynamic response time-course, contrast-to-noise ratio, voxel-wise effect size or statistical maps of activation as compared to imaging at 75° (an angle close to the Ernst angle). These results suggest that using low flip angles in BOLD fMRI experimentation to obtain benefits such as (1) reduction of RF power, (2) limitation of apparent T(1)-related inflow effects, (3) reduction of through-plane motion artifacts, (4) lower levels of physiological noise, and (5) improved tissue contrast is feasible when physiological noise dominates and SNR is high.     

Integration of motion correction and physiological noise regression in fMRI

Physiological fluctuations resulting from the heart beat and respiration are a dominant source of noise in fMRI, particularly at high field strengths. Commonly used physiological noise correction techniques, such as RETROspective Image CORrection (RETROICOR), rely critically on the timing of the image acquisition relative to the heart beat, but do not account for the effects of subject motion. Such motion affects the fluctuation amplitude, yet volume registration can distort the timing information. In this study, we aimed to systematically determine the optimal order of volume registration, slice-time correction and RETROICOR in their traditional forms. In addition, we evaluate the sensitivity of RETROICOR to timing errors introduced by the slice acquisition, and we develop a new method of accounting for timing errors introduced by volume registration into physiological correction (motion-modified RETROICOR). Both simulation and resting data indicate that the temporal standard deviation is reduced most by performing volume registration before RETROICOR and slice-time correction after RETROCIOR. While simulations indicate that physiological noise correction with regressors constructed on a slice-by-slice basis more accurately modeled physiological noise compared to using the same regressors for the entire volume, the difference between these regression techniques in subject data was minimal. The motion-modified RETROICOR showed marked improvement in simulations with varying amounts of subject motion, reducing the temporal standard deviation by up to 36% over the traditional RETROICOR. Though to a lesser degree than in simulation, the motion-modified RETROICOR performed better in nearly every voxel in the brain in both high- and low-resolution subject data.     

Effect of spatial smoothing on physiological noise in high-resolution fMRI

Physiological noise dominates the SNR of the fMRI time-course at commonly used spatial resolutions at field strengths of 3 T and above. Operating in this physiological noise dominated regime limits some benefits of high field acquisition since increases in image SNR produce only modest increases in time-course SNR. Although previous studies have shown that the physiological noise dominance can be mitigated by using higher spatial resolutions, not all functional studies require voxel sizes smaller than the thickness of the human cortex. In this study, we examine the effect of acquiring high spatial resolution, thermal noise dominated time-courses and spatially smoothing the images to lower resolutions, which would otherwise be physiological noise dominated. At high field strengths, where physiological noise is most problematic, this strategy lowered the overall time-course variance compared to direct acquisition at commonly used spatial resolution. At 7 T for example, 5 x 5 x 3 mm3 resolution images derived from smoothing 1.5 x 1.5 x 3 mm3 data improved time-course SNR by a factor of 1.89 compared to a time-series acquired at 5 x 5 x 3 mm3. Presumably, this effect was derived from the reduced physiological-to-thermal noise ratio in the high spatial resolution data followed by a smoothing operation that improves SNR without adding physiological noise. Our findings demonstrate that in contrast to conventional SNR penalties associated with spatially smoothing Fourier data, the time-course SNR of smoothed high-resolution data can be improved compared to direct acquisition at the desired resolution.     

Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain.     

Signal and noise characteristics of Hahn SE and GE BOLD fMRI at 7 T in humans

At very high magnetic fields, GE BOLD fMRI is expected to contain nonspecific contributions and behave differently than HSE fMRI data. Similarly, the two approaches can conceivably suffer from different contributions to temporal instabilities in a times series that ultimately determine the contrast-to-noise ratio (CNR). We investigate the signal and signal fluctuation characteristics in GE and HSE fMRI data with the imaging parameters separately optimized for each contrast at 7 T. In HSE fMRI, activation-induced fractional signal change (DeltaS/S) decreased rapidly, and the ratio of standard deviations of image-to-image fluctuations due to physiological processes (sigmaPhys) to thermal noise (sigmaTherm) remained constant with increasing voxel volume. In contrast, DeltaS/S as well as volume of activated voxels was virtually independent of voxel size for GE BOLD, and sigma(Phys)/sigmaTherm increased with increasing voxel size. The ratio of BOLD signal changes (GE/HSE) was much closer to 1 in tissue areas compared to vessel areas. These observations led to the conclusions that the spatial extent of the activation-induced DeltaS/S was much broader in the GE data, and that the physiological processes that give rise to the temporal fluctuations lost coherence over millimeter distances in HSE compared to GE fMRI data. While further studies are needed to characterize it fully, sigmaPhys in HSE data was clearly different than in GE data. It was concluded that HSE imaging yields a significantly reduced amount of nonspecific signals compared to GE imaging, and, would be the method of choice (over GE) for high-resolution applications in humans.     

The impact of physiological noise correction on fMRI at 7 T

Sub-striatal regions of interest (ROIs) are widely used in PET studies to investigate the role of dopamine in the modulation of neural networks implicated in emotion, cognition and motor function. One common approach is that of Mawlawi et al. (2001) and Martinez et al. (2003), where each striatum is divided into five sub-regions. This study focuses on the use of two spatial normalization-based alternatives to manual sub-striatal ROI delineation per subject: manual ROI delineation on a template brain and the production of probabilistic ROIs from a set of subject-specific manually delineated ROIs. Two spatial normalization algorithms were compared: SPM5 unified segmentation and ART. The ability of these methods to quantify sub-striatal regional non-displaceable binding potential (BP(ND)) and BP(ND) % change (following methylphenidate) was tested on 32 subjects (16 controls and 16 ADHD patients) scanned with the dopamine D(2)/D(3) ligand [(18)F]fallypride. Probabilistic ROIs produced by ART provided the best results, with similarity index values against subject-specific manual ROIs of 0.75-0.89 (mean 0.84) compared to 0.70-0.85 (mean 0.79) for template ROIs. Correlations (r) for BP(ND) and BP(ND) % change between subject-specific manual ROIs and these probabilistic ROIs of 0.90-0.98 (mean 0.95) and 0.98-1.00 (mean 0.99) respectively were superior overall to those obtained with template ROIs, although only marginally so for BP(ND) % change. The significance of relationships between BP(ND) measures and both behavioural tasks and methylphenidate plasma levels was preserved with ART combined with both probabilistic and template ROIs. SPM5 virtually matched the performance of ART for BP(ND) % change estimation but was inferior for BP(ND) estimation in caudate sub-regions. ART spatial normalization combined with probabilistic ROIs and to a lesser extent template ROIs provides an efficient and accurate alternative to time-consuming manual sub-striatal ROI delineation per subject, especially when the parameter of interest is BP(ND) % change.     

A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.     

Deconvolution of impulse response in event-related BOLD fMRI

The temporal characteristics of the BOLD response in sensorimotor and auditory cortices were measured in subjects performing finger tapping while listening to metronome pacing tones. A repeated trial paradigm was used with stimulus durations of 167 ms to 16 s and intertrial times of 30 s. Both cortical systems were found to be nonlinear in that the response to a long stimulus could not be predicted by convolving the 1-s response with a rectangular function. In the short-time regime, the amplitude of the response varied only slowly with stimulus duration. It was found that this character was predicted with a modification to Buxton's balloon model. Wiener deconvolution was used to deblur the response to concatenated short episodes of finger tapping at different temporal separations and at rates from 1 to 4 Hz. While the measured response curves were distorted by overlap between the individual episodes, the deconvolved response at each rate was found to agree well with separate scans at each of the individual rates. Thus, although the impulse response cannot predict the response to fully overlapping stimuli, linear deconvolution is effective when the stimuli are separated by at least 4 s. The deconvolution filter must be measured for each subject using a short-stimulus paradigm. It is concluded that deconvolution may be effective in diminishing the hemodynamically imposed temporal blurring and may have potential applications in quantitating responses in eventrelated fMRI.     

Functional connectivity in the rat at 11.7T: Impact of physiological noise in resting state fMRI

Resting state functional MRI (rs-fMRI) of the brain has the potential to elicit networks of functional connectivity and to reveal changes thereof in animal models of neurological disorders. In the present study, we investigate the contribution of physiological noise and its impact on assessment of functional connectivity in rs-fMRI of medetomidine sedated, spontaneously breathing rats at ultrahigh field of 11.7 Tesla. We employed gradient echo planar imaging (EPI) with repetition times of 3s and used simultaneous recordings of physiological parameters. A model of linear regression was applied to quantify the amount of BOLD fMRI signal fluctuations attributable to physiological sources. Our results indicate that physiological noise - mainly originating from the respiratory cycle -dominates the rs-fMRI time course in the form of spatially complex correlation patterns. As a consequence, these physiological fluctuations introduce severe artifacts into seed-based correlation maps and lead to misinterpretation of corresponding connectivity measures. We demonstrate that a scheme of motion correction and linear regression can significantly reduce physiological noise in the rs-fMRI time course, remove artifacts, and hence improve the reproducibility of functional connectivity assessment. In conclusion, physiological noise can severely compromise functional connectivity MRI (fcMRI) of the rodent at high fields and must be carefully considered in design and interpretation of future studies. Motion correction should be considered the primary strategy for reduction of apparent motion related to respiratory fluctuations. Combined with subsequent regression of physiological confounders, this strategy has proven successful in reducing physiological noise and related artifacts affecting functional connectivity analysis. The proposed new and rigorous protocol now opens the potential of fcMRI to elicit the role of brain connectivity in pathological processes without concerns of confounding contributions from physiological noise.     

Phase stability in fMRI time series: effect of noise regression, off-resonance correction and spatial filtering techniques

Although the majority of fMRI studies exploit magnitude changes only, there is an increasing interest regarding the potential additive information conveyed by the phase signal. This integrated part of the complex number furnished by the MR scanners can also be used for exploring direct detection of neuronal activity and for thermography. Few studies have explicitly addressed the issue of the available signal stability in the context of phase time-series, and therefore we explored the spatial pattern of frequency specific phase fluctuations, and evaluated the effect of physiological noise components (heart beat and respiration) on the phase signal. Three categories of retrospective noise reduction techniques were explored and the temporal signal stability was evaluated in terms of a physiologic noise model, for seven fMRI measurement protocols in eight healthy subjects at 3 T, for segmented CSF, gray and white matter voxels. We confirmed that for most processing methods, an efficient use of the phase information is hampered by the fact that noise from physiological and instrumental sources contributes significantly more to the phase than to the magnitude instability. Noise regression based on the phase evolution of the central k-space point, RETROICOR, or an orthonormalized combination of these were able to reduce their impact, but without bringing phase stability down to levels expected from the magnitude signal. Similar results were obtained after targeted removal of scan-to-scan variations in the bulk magnetic field by the dynamic off-resonance in k-space (DORK) method and by the temporal off-resonance alignment of single-echo time series technique (TOAST). We found that spatial high-pass filtering was necessary, and in vivo a Gaussian filter width of 20 mm was sufficient to suppress physiological noise and bring the phase fluctuations to magnitude levels. Stronger filters brought the fluctuations down to levels dictated by thermal noise contributions, and for 62.5 mm³ voxels the phase stability was as low as 5 mrad (0.27°). In conditions of low SNR_o and high temporal sampling rate (short TR); we achieved an upper bound for the phase instabilities at 0.0017 ppm, which is close to the dHb contribution to the GM/WM phase contrast.     

Mapping the MRI voxel volume in which thermal noise matches physiological noise--implications for fMRI

This work addresses the choice of the imaging voxel volume in blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI). Noise of physiological origin that is present in the voxel time course is a prohibitive factor in the detection of small activation-induced BOLD signal changes. If the physiological noise contribution dominates over the temporal fluctuation contribution in the imaging voxel, further increases in the voxel signal-to-noise ratio (SNR) will have diminished corresponding increases in temporal signal-to-noise (TSNR), resulting in reduced corresponding increases in the ability to detect activation induced signal changes. On the other hand, if the thermal and system noise dominate (suggesting a relatively low SNR) further decreases in SNR can prohibit detection of activation-induced signal changes. Here we have proposed and called the "suggested" voxel volume for fMRI the volume where thermal plus system-related and physiological noise variances are equal. Based on this condition we have created maps of fMRI suggested voxel volume from our experimental data at 3T, since this value will spatially vary depending on the contribution of physiologic noise in each voxel. Based on our fast EPI segmentation technique we have found that for gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) brain compartments the mean suggested cubical voxel volume is: (1.8 mm)3, (2.1 mm)3 and (1.4 mm)3, respectively. Serendipitously, (1.8 mm)3 cubical voxel volume for GM approximately matches the cortical thickness, thus optimizing BOLD contrast by minimizing partial volume averaging. The introduced suggested fMRI voxel volume can be a useful parameter for choice of imaging volume for functional studies.     

Temporal dynamics of the BOLD fMRI impulse response

Using computer simulations and high-resolution fMRI experiments in humans (n=6) and rats (n=8), we investigated to what extent BOLD fMRI temporal resolution is limited by dispersion in the venous vasculature. For this purpose, time-to-peak (TTP) and full-width at half-maximum (FWHM) of the BOLD impulse response (IR) function were determined. In fMRI experiments, a binary m-sequence probe method was used to obtain high-sensitivity model-free single-pixel estimates of IR. Simulations of postcapillary flow suggested that flow-related dispersion leads to a TTP and FWHM increase, which can amount to several seconds in larger pial veins. fMRI experiments showed substantial spatial variation in IR timing within human visual cortex, together with a correlation between TTP and FWHM. Averaged across the activated regions and across subjects, TTP and FWHM were 4.51+/-0.52 and 4.04+/-0.42 s, respectively. In regions of interest (ROI) weighted toward the larger venous structures, TTP and FWHM increased to 5.07+/-0.64 and 4.32+/-0.48 s, respectively. In rat somatosensory cortex, TTP and FWHM were substantially shorter than in humans (2.73+/-0.60 and 2.28+/-0.63 s, respectively). These results are consistent with a substantial macrovascular dispersive contribution to BOLD IR in humans, and furthermore suggest that neurovascular coupling is a relatively rapid process, with a resolution below 2.3 s FWHM.     

血氧水平依赖磁共振功能成像评价国人女性正常乳腺

目的 观察不同年龄、不同腺体类型、不同部位正常乳腺组织的R2^*值及其与乳腺组织血供的相关性。方法 BI-RADS 1级患者18例,脂肪型和少量腺体型6例,多量腺体型8例,致密腺体型4例。根据女性乳腺组织的血管构筑特点,将乳腺组织分成乳头乳晕后区(相当于中央区)、前部和后部。应用3.0T MR系统进行成像,以Functool软件处理原始数据,比较不同腺体类型、乳腺不同部位、绝经组与月经期组患者乳腺的R2^*值以及R2^*值与乳腺组织信号增强率之间的关系。结果 左侧乳腺各部位的R2^*值为:中央部(64.77±17.29)Hz,前部(51.95±21.24)Hz,后部(47.41±16.18)Hz;右侧乳腺分别为:中央部(66.17±19.80)Hz,前部(52.48±12.21)Hz,后部(46.13±12.56)Hz,同侧乳腺不同部位的R2^*值有明显差别。脂肪型和少量腺体型中,腺体前部的R2^*值高于中央部和后部,而多量腺体型和致密型乳腺中央部的R2^*值高于其他两个部位,3型之间腺体各部位R2^*值差异无统计学意义。绝经组乳腺中央部的R2^*值小于月经期组,而前部和后部均大于月经期组。乳腺中央部信号增强率最小,腺体后部最大,随着R2^*值减小,信号增强率增加。结论 国人女性正常乳腺组织血氧含量变化存在一定规律。BOLD-fMRI可以监测组织氧合状态和血管功能,可为乳腺疾病的诊治提供一定的参考。     

Complex spatio-temporal dynamics of fMRI BOLD: A study of motor learning

Many studies have investigated the temporal properties of BOLD signal responses to task performance in regions of interest, often noting significant departures from the conventionally modelled response shape, and significant variation between regions. However, these investigations are rarely extended across the whole brain nor incorporated into the routine analysis of fMRI studies. As a result, little is known about the range of response shapes generated in the brain by common paradigms. The present study finds such temporal dynamics can be complex. We made a detailed investigation of BOLD signal responses across the whole brain during a two minute motor-sequence task, and tracked changes due to learning. The multi-component OSORU (Onset, Sustained, Offset, Ramp, Undershoot) linear model, developed by Harms and Melcher (J.Neurophysiology, 2003), was extended to characterise responses. In many regions, signal transients persisted for over thirty seconds, with large signal spikes at onset often followed by a dip in signal below the final sustained level of activation. Training altered certain features of the response shape, suggesting that different features of the response may reflect different aspects of neuro-vascular dynamics. Unmodelled, this may give rise to inconsistent results across paradigms of varying task durations. Few of the observed effects have been thoroughly addressed in physiological models of the BOLD response. The complex, extended dynamics generated by this simple, often employed task, suggests characterisation and modelling of temporal aspects of BOLD responses needs to be carried out routinely, informing experimental design and analysis, and physiological modelling.     

Characterization of event-related designs using BOLD and IRON fMRI

Despite many desirable characteristics, event-related (ER) stimulus designs for BOLD and IRON suffer from low detection power relative to block designs because the hemodynamic impulse response function (IRF) acts as a low-pass filter on neural activation to attenuate the size of differential responses to alternate stimuli. While the use of exogenous contrast agent (IRON technique) provides an alternative fMRI method in animal models to improve sensitivity and spatial localization, the inherently slower hemodynamic IRF causes IRON detection efficiency to decrease faster than BOLD efficiency as the interstimulus interval (ISI) is shortened. Using simulations based upon assumptions of stimulus-response linearity and experimental data obtained in awake, non-human primates, this study compared detection efficiencies for fixed, random and semi-random ISI distributions for BOLD and IRON techniques. A larger relative gain in detection efficiency at short ISI was obtained by randomized designs using IRON contrast relative to BOLD contrast due to the slower IRF of the IRON method. To quantify tradeoffs between detection efficiency and the predictability of stimulus presentation, the Shannon entropy was introduced as an objective measure of predictability. Small amounts of entropy can be traded for large gains in efficiency, particularly for the IRON method.     

Dynamic models of BOLD contrast

This personal recollection looks at the evolution of ideas about the dynamics of the blood oxygenation level dependent (BOLD) signal, with an emphasis on the balloon model. From the first detection of the BOLD response it has been clear that the signal exhibits interesting dynamics, such as a pronounced and long-lasting post-stimulus undershoot. The BOLD response, reflecting a change in local deoxyhemoglobin, is a combination of a hemodynamic response, related to changes in blood flow and venous blood volume, and a metabolic response related to oxygen metabolism. Modeling is potentially a way to understand the complex path from changes in neural activity to the BOLD signal. In the early days of fMRI it was hoped that the hemodynamic/metabolic response could be modeled in a unitary way, with blood flow, oxygen metabolism, and venous blood volume-the physiological factors that affect local deoxyhemoglobin-all tightly linked. The balloon model was an attempt to do this, based on the physiological ideas of limited oxygen delivery at baseline and a slow recovery of venous blood volume after the stimulus (the balloon effect), and this simple model of the physiology worked well to simulate the BOLD response. However, subsequent experiments suggest a more complicated picture of the underlying physiology, with blood flow and oxygen metabolism driven in parallel, possibly by different aspects of neural activity. In addition, it is still not clear whether the post-stimulus undershoot is a hemodynamic or a metabolic phenomenon, although the original venous balloon effect is unlikely to be the full explanation, and a flow undershoot is likely to be important. Although our understanding of the physics of the BOLD response is now reasonably solid, our understanding of the underlying physiological relationships is still relatively poor, and this is the primary hurdle for future models of BOLD dynamics.     

Compensation of susceptibility-induced BOLD sensitivity losses in echo-planar fMRI imaging.

Gradient-echo echo-planar imaging is a standard technique in functional magnetic resonance imaging (fMRI) experiments based on the blood oxygenation level-dependent (BOLD) effect. A major problem is the occurrence of susceptibility gradients near air/tissue interfaces. As a consequence, the detection of neuronal activation may be greatly compromised in certain brain areas, especially in the temporal lobes and in the orbitofrontal cortex. Common approaches to overcome this problem, such as z-shimming or the use of tailored radio frequency pulses, usually compensate only for susceptibility gradients in the slice selection direction. In the present study, the influence of susceptibility gradients in the phase encoding direction is investigated both theoretically and experimentally. It is shown that these gradients influence the effective echo time TE and may reduce considerably the local BOLD sensitivity, even in the case of acceptable image intensities. A compensation method is proposed and tested in an fMRI experiment based on a hypercapnic challenge. The results suggest that the compensation method allows for the detection of activation in brain areas which are usually unavailable for BOLD studies.     

Vascular dynamics and BOLD fMRI: CBF level effects and analysis considerations

Changes in the cerebral blood flow (CBF) baseline produce significant changes to the hemodynamic response. This work shows that increases in the baseline blood flow level produce blood oxygenation-level dependent (BOLD) and blood flow responses that are slower and lower in amplitude, while decreases in the baseline blood flow level produce faster and higher amplitude hemodynamic responses. This effect was characterized using a vascular model of the hemodynamic response that separated arterial blood flow response from the venous blood volume response and linked the input stimulus to the vascular response. The model predicted the baseline blood flow level effects to be dominated by changes in the arterial vasculature. Specifically, it predicted changes in the arterial blood flow time constant and venous blood volume time constant parameters of +294% and -24%, respectively, for a 27% increase in the baseline blood flow. The vascular model performance was compared to an empirical model of the hemodynamic response. The vascular and empirical hemodynamic models captured most of the baseline blood flow level effects observed and can be used to correct for these effects in fMRI data. While the empirical hemodynamic model is easy to implement, it did not incorporate any explicit physiological information.     

Comparison of BOLD fMRI and MEG characteristics to vibrotactile stimulation

The characteristics of blood oxygenation level-dependent (BOLD) fMRI and magnetoencephalographic (MEG) responses to vibrotactile stimuli in humans were studied and compared. The stimuli, presented with interstimulus intervals (ISIs) ranging from 1 to 5 s, yielded highly reproducible MEG responses, with current dipoles in the primary somatosensory (SI) cortex in all subjects. BOLD fMRI responses to similar stimuli showed substantial intrasubject variation in the activation sites around the SI cortex. BOLD responses were detected in all subjects in the secondary somatosensory (SII) cortices as well, with comparable BOLD response amplitudes to those in the SI cortex. Current dipoles, used to model the MEG signals, were stronger at longer ISIs than shorter ISIs. The BOLD response amplitudes did not show a similar dependence on ISI, but the activated brain area was larger when longer ISIs or longer stimuli were applied. Our results support the view that combined use of brain mapping methods provides complementary information and should be considered in functional brain examinations.