To smooth or not to smooth? ROC analysis of perfusion fMRI data

Blood oxygenation level dependent (BOLD) contrast has been widely used for visualizing regional neural activation. Temporal filtering and parameter estimation algorithms are generally used to account for the intrinsic temporal autocorrelation present in BOLD data. Arterial spin labeling perfusion imaging is an emerging methodology for visualizing regional brain function both at rest and during activation. Perfusion contrast manifests different noise properties compared with BOLD contrast, represented by the even distribution of noise power and spatial coherence across the frequency spectrum. Consequently, different strategies are expected to be employed in the statistical analysis of functional magnetic resonance imaging (fMRI) data based on perfusion contrast. In this study, the effect of different analysis methods upon signal detection efficacy, as assessed by receiver operator characteristic (ROC) measures, was examined for perfusion fMRI data. Simulated foci of neural activity of varying amplitude and spatial extent were added to resting perfusion data, and the accuracy of each analysis was evaluated by comparing the results with the known distribution of pseudo-activation. In contrast to the BOLD fMRI, temporal smoothing or filtering reduces the power of perfusion fMRI data analyses whereas spatial smoothing is beneficial to the efficacy of analyses.     

High spatial resolution BOLD fMRI using simultaneous multislice excitation with echo-shifting gradient echo at 7 Tesla

We introduce an accelerated gradient echo (GRE) sequence combining simultaneous multislice excitation (SMS) with echo-shifting technique for high spatial resolution blood oxygen level dependent (BOLD) functional MRI (fMRI). The simulation was conducted to optimize scan parameters. To validate the feasibility of the proposed technique, the visual and motor task experiments were performed at 7.0 Tesla (T). The single-shot EPI sequence was also applied in comparison with the proposed technique. The simulation results showed that an optimized flip angle of 9° provided maximal BOLD contrast for our scanning scheme, allowing low power deposition and SMS acceleration factor of 5. Additionally, parallel acquisition imaging with acceleration factor of 2 was utilized, which allowed a total acceleration factor of 10 in volunteer study. The experiment results showed that geometric distortion-free BOLD images with voxel size of 1.0 × 1.0 × 2.5 mm³ were obtained. Significant brain activation was identified in both visual and motor task experiments, which were in accordance with previous investigations. The proposed technique has potential for high spatial resolution fMRI at ultra-high field because of its sufficient BOLD sensitivity as well as improved acquisition speed over conventional GRE-based techniques.     

Comparison of BOLD,diffusion-weighted fMRI and ADC-fMRI for stimulation of the primary visual system with a block paradigm

The blood oxygen level-dependent (BOLD) effect is extensively used for functional MRI (fMRI) but presents some limitations. Diffusion-weighted fMRI (DfMRI) has been proposed as a method more tightly linked to neuronal activity. This work proposes a protocol of DfMRI acquired for several b-values and diffusion directions that is compared to gradient-echo BOLD (GE-BOLD) and to repeated spin-echo BOLD (SE-BOLD, acquisitions performed with b = 0 s/mm²), which was also used to ensure the reproducibility of the response.A block stimulation paradigm of the primary visual system (V1) was performed in 12 healthy subjects with checkerboard alternations (2 Hz frequency). DfMRI was performed at 3 T with 5 b-values (b = 1500, 1000, 500, 250, 0 s/mm²) with TR/TE = 1004/93 ms, Δ/δ = 45.4 ms/30 ms, and 6 spatial directions for diffusion measures. GE-BOLD was performed with a similar block stimulation design timing. Apparent Diffusion Coefficient (ADC)-fMRI was computed with all b-values used. An identical Z-score level was used for all fMRI modalities for the comparison of volumes of activation. ADC-fMRI and SE-BOLD fMRI activation locations were compared in a voxel-based analysis to a cytoarchitectural probability map of V1.SE-BOLD activation volumes represented only 55% of the GE-BOLD activation volumes (P < 0.0001). DfMRI activation volumes averaged for all b-values acquired represented only 12% of GE-BOLD (P < 0.0001) and only 22% of SE-BOLD activation volumes (P < 0.005). Compared to SE-BOLD-fMRI, ADC-fMRI activations showed fewer pixels outside of V1 and a higher average probability of belonging to V1.DfMRI and ADC-fMRI acquisition at 3 T could be easily post-processed with common neuro-imaging software. DfMRI and ADC-fMRI activation volumes were significantly smaller than those obtained with SE-BOLD. ADC-fMRI activations were more precisely localized in V1 than those of SE-BOLD-fMRI. This validated the increased capability of ADC-fMRI compared to BOLD to enhance the precision of localizing an fMRI activation in the cyto-architectural zone V1, thereby justifying the use of ADC-fMRI for neuro-scientific studies.     

Synchronized detection of minute electrical currents with MRI using Lorentz effect imaging

The blood oxygenation level-dependent (BOLD) effect is the most commonly used contrast mechanism in functional magnetic resonance imaging (fMRI), due to its relatively high spatial resolution and sensitivity. However, the ability of BOLD fMRI to accurately localize neuronal activation in space and time is limited by the inherent hemodynamic modulation. There is hence a need to develop alternative MRI methods that can directly image neuroelectric activity, thereby achieving both a high temporal resolution and spatial specificity as compared to conventional BOLD fMRI. In this paper, we extend the Lorentz effect imaging technique, which can detect spatially incoherent yet temporally synchronized minute electrical activity in a strong magnetic field, and demonstrate its feasibility for imaging randomly oriented electrical currents on the order of microamperes with a temporal resolution on the order of milliseconds in gel phantoms. This constitutes a promising step towards its application to direct imaging of neuroelectric activity in vivo, which has the same order of current density and temporal synchrony.     

SNR and functional sensitivity of BOLD and perfusion-based fMRI using arterial spin labeling with spiral SENSE at 3 T

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies using parallel imaging to reduce the readout window have reported a loss in temporal signal-to-noise ratio (SNR) that is less than would be expected given a purely thermal noise model. In this study, the impact of parallel imaging on the noise components and functional sensitivity of both BOLD and perfusion-based fMRI data was investigated. Dual-echo arterial spin labeling data were acquired on five subjects using sensitivity encoding (SENSE), at reduction factors (R) of 1, 2 and 3. Direct recording of cardiac and respiratory activity during data acquisition enabled the retrospective removal of physiological noise. The temporal SNR of the perfusion time series closely followed the thermal noise prediction of a √R loss in SNR as the readout window was shortened, with temporal SNR values (relative to the R=1 data) of 0.72 and 0.56 for the R=2 and R=3 data, respectively, after accounting for physiological noise. However, the BOLD temporal SNR decreased more slowly than predicted even after accounting for physiological noise, with relative temporal SNR values of 0.80 and 0.63 for the R=2 and R=3 data, respectively. Spectral analysis revealed that the BOLD trends were dominated by low-frequency fluctuations, which were not dominant in the perfusion data due to signal processing differences. The functional sensitivity, assessed using mean F values over activated regions of interest (ROIs), followed the temporal SNR trends for the BOLD data. However, results for the perfusion data were more dependent on the threshold used for ROI selection, most likely due to the inherently low SNR of functional perfusion data.     

Evaluation of preprocessing steps to compensate for magnetic field distortions due to body movements in BOLD fMRI

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is currently the dominant technique for non-invasive investigation of brain functions. One of the challenges with BOLD fMRI, particularly at high fields, is compensation for the effects of spatiotemporally varying magnetic field inhomogeneities (ΔB₀) caused by normal subject respiration and, in some studies, movement of the subject during the scan to perform tasks related to the functional paradigm. The presence of ΔB₀ during data acquisition distorts reconstructed images and introduces extraneous fluctuations in the fMRI time series that decrease the BOLD contrast-to-noise ratio. Optimization of the fMRI data-processing pipeline to compensate for geometric distortions is of paramount importance to ensure high quality of fMRI data. To investigate ΔB₀ caused by subject movement, echo-planar imaging scans were collected with and without concurrent motion of a phantom arm. The phantom arm was constructed and moved by the experimenter to emulate forearm motions while subjects remained still and observed a visual stimulation paradigm. These data were then subjected to eight different combinations of preprocessing steps. The best preprocessing pipeline included navigator correction, a complex phase regressor and spatial smoothing. The synergy between navigator correction and phase regression reduced geometric distortions better than either step in isolation and preconditioned the data to make them more amenable to the benefits of spatial smoothing. The combination of these steps provided a 10% increase in t-statistics compared to only navigator correction and spatial smoothing and reduced the noise and false activations in regions where no legitimate effects would occur.     

A kernel machine-based fMRI physiological noise removal method

Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal. In addition, changes of heartbeat and respiration patterns also generate physiological fluctuations that have similar frequencies with BOLD activation. Most existing physiological noise-removal methods either place restrictive limitations on image acquisition or utilize filtering or regression based post-processing algorithms, which cannot distinguish the frequency-overlapping BOLD activation and the physiological noise. In this work, we address the challenge of physiological noise removal via the kernel machine technique, where a nonlinear kernel machine technique, kernel principal component analysis, is used with a specifically identified kernel function to differentiate BOLD signal from the physiological noise of the frequency. The proposed method was evaluated in human fMRI data acquired from multiple task-related and resting state fMRI experiments. A comparison study was also performed with an existing adaptive filtering method. The results indicate that the proposed method can effectively identify and reduce the physiological noise in fMRI data. The comparison study shows that the proposed method can provide comparable or better noise removal performance than the adaptive filtering approach.     

Retinotopic mapping with spin echo BOLD at 7T

For blood oxygenation level-dependent (BOLD) functional MRI experiments, contrast-to-noise ratio (CNR) increases with increasing field strength for both gradient echo (GE) and spin echo (SE) BOLD techniques. However, susceptibility artifacts and nonuniform coil sensitivity profiles complicate large field-of-view fMRI experiments (e.g., experiments covering multiple visual areas instead of focusing on a single cortical region). Here, we use SE BOLD to acquire retinotopic mapping data in early visual areas, testing the feasibility of SE BOLD experiments spanning multiple cortical areas at 7T. We also use a recently developed method for normalizing signal intensity in T₁-weighted anatomical images to enable automated segmentation of the cortical gray matter for scans acquired at 7T with either surface or volume coils. We find that the CNR of the 7T GE data (average single-voxel, single-scan stimulus coherence: 0.41) is almost twice that of the 3T GE BOLD data (average coherence: 0.25), with the CNR of the SE BOLD data (average coherence: 0.23) comparable to that of the 3T GE data. Repeated measurements in individual subjects find that maps acquired with 1.8-mm resolution at 3T and 7T with GE BOLD and at 7T with SE BOLD show no systematic differences in either the area or the boundary locations for V1, V2 and V3, demonstrating the feasibility of high-resolution SE BOLD experiments with good sensitivity throughout multiple visual areas.     

Integration of EEG source imaging and fMRI during continuous viewing of natural movies

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are noninvasive neuroimaging tools which can be used to measure brain activity with excellent temporal and spatial resolution, respectively. By combining the neural and hemodynamic recordings from these modalities, we can gain better insight into how and where the brain processes complex stimuli, which may be especially useful in patients with different neural diseases. However, due to their vastly different spatial and temporal resolutions, the integration of EEG and fMRI recordings is not always straightforward. One fundamental obstacle has been that paradigms used for EEG experiments usually rely on event-related paradigms, while fMRI is not limited in this regard. Therefore, here we ask whether one can reliably localize stimulus-driven EEG activity using the continuously varying feature intensities occurring in natural movie stimuli presented over relatively long periods of time. Specifically, we asked whether stimulus-driven aspects in the EEG signal would be co-localized with the corresponding stimulus-driven BOLD signal during free viewing of a movie. Secondly, we wanted to integrate the EEG signal directly with the BOLD signal, by estimating the underlying impulse response function (IRF) that relates the BOLD signal to the underlying current density in the primary visual area (V1). We made sequential fMRI and 64-channel EEG recordings in seven subjects who passively watched 2-min-long segments of a James Bond movie. To analyze EEG data in this natural setting, we developed a method based on independent component analysis (ICA) to reject EEG artifacts due to blinks, subject movement, etc., in a way unbiased by human judgment. We then calculated the EEG source strength of this artifact-free data at each time point of the movie within the entire brain volume using low-resolution electromagnetic tomography (LORETA). This provided for every voxel in the brain (i.e., in 3D space) an estimate of the current density at every time point. We then carried out a correlation between the time series of visual contrast changes in the movie with that of EEG voxels. We found the most significant correlations in visual area V1, just as seen in previous fMRI studies (Bartels A, Zeki, S, Logothetis NK. Natural vision reveals regional specialization to local motion and to contrast-invariant, global flow in the human brain. Cereb Cortex 2008;18(3):705–717), but on the time scale of milliseconds rather than of seconds. To obtain an estimate of how the EEG signal relates to the BOLD signal, we calculated the IRF between the BOLD signal and the estimated current density in area V1. We found that this IRF was very similar to that observed using combined intracortical recordings and fMRI experiments in nonhuman primates. Taken together, these findings open a new approach to noninvasive mapping of the brain. It allows, firstly, the localization of feature-selective brain areas during natural viewing conditions with the temporal resolution of EEG. Secondly, it provides a tool to assess EEG/BOLD transfer functions during processing of more natural stimuli. This is especially useful in combined EEG/fMRI experiments, where one can now potentially study neural-hemodynamic relationships across the whole brain volume in a noninvasive manner.     

Characterization of high-resolution Gradient Echo and Spin Echo EPI for fMRI in the human visual cortex at 7 T

The increased signal-to-noise ratio (SNR) offered by functional Magnetic Resonance Imaging (fMRI) at 7T allows the acquisition of functional data at sub-millimetric spatial resolutions. However, simply reducing partial volume effects is not sufficient to precisely localize task-induced activation due to the indirect mechanisms that relate brain function and the changes in the measured signal.In this work T2* and T2 weighted Echo Planar Imaging (EPI) schemes based on Gradient Recalled Echo (GRE) and Spin Echo (SE) were evaluated in terms of temporal SNR, percent signal change, contrast to noise ratio (CNR), activation volume, and sensitivity and specificity to gray matter. Datasets were acquired during visual stimulation at in-plane resolutions ranging between 1.5 × 1.5 mm² and 0.75 × 0.75 mm² targeting the early visual cortex.While similar activation foci were obtained in all acquisitions, at in-plane resolutions of 1.0 × 1.0 mm² and larger voxel sizes the T2 weighted contrast of SE-EPI allowed the identification of the activation site with better spatial accuracy. However, at sub-millimetric resolutions the decrease in temporal SNR significantly hampered the sensitivity and the extent of the activation site. On the other hand, high resolution T2* weighted data collected with GRE-EPI provided higher CNR and sensitivity, benefiting from the decreased physiological and partial volume effects. However, spurious activations originating from regions of blood drainage were still present in GRE data, and simple thresholding techniques were found to be inadequate for the removal of such contributions. The combination of 2-class and 3-class automated segmentations, performed directly in EPI space, allowed the selection of active voxels in gray matter. This approach could enable GRE-EPI to accurately map functional activity with satisfactory CNR and specificity to the true site of activation.     

Empirical optimization of ASL data analysis using an ASL data processing toolbox: ASLtbx

Arterial spin labeling (ASL) perfusion fMRI data differ in important respects from the more familiar blood oxygen level-dependent (BOLD) fMRI data and require specific processing strategies. In this paper, we examined several factors that may influence ASL data analysis, including data storage bit resolution, motion correction, preprocessing for cerebral blood flow (CBF) calculations and nuisance covariate modeling. Continuous ASL data were collected at 3 T from 10 subjects while they performed a simple sensorimotor task with an epoch length of 48 s. These data were then analyzed using systematic variations of the factors listed above to identify the approach that yielded optimal signal detection for task activation. Improvements in statistical power were found for use of at least 10 bits for data storage at 3 T. No significant difference was found in motor cortex regarding using simple subtraction or sinc subtraction, but the former presented minor but significantly (P<.024) larger peak t value in visual cortex. While artifactual head motion patterns were observed in synthetic data and background-suppressed ASL data when label/control images were realigned to a common target, independent realignment of label and control images did not yield significant improvements in activation in the sensorimotor data. It was also found that CBF calculations should be performed prior to spatial normalization and that modeling of global fluctuations yielded significantly increased peak t value in motor cortex. The implementation of all ASL data processing approaches is easily accomplished within an open-source toolbox, ASLtbx, and is advocated for most perfusion fMRI data sets.     

Activation volume vs BOLD signal change as measures of fMRI activation – Its impact on GABA – fMRI activation correlation

PurposeTo explore the relative robustness of functional MRI (fMRI) activation volume and blood oxygen level-dependent (BOLD) signal change as fMRI metric, and to study the effect of relative robustness on the correlation between fMRI activation and cortical gamma amino butyric acid (GABA) in healthy controls and patients with multiple sclerosis (MS).MethodsfMRI data were acquired from healthy controls and patients with MS, with the subjects peforming self paced bilateral finger tapping in block design. GABA spectroscopy was performed with voxel placed on the area of maximum activation during fMRI. Activation volume and BOLD signal changes at primary motor cortex (M1), as well as GABA concentration were calculated for each patient.ResultsActivation volume correlated with BOLD signal change in healthy controls, but no such correlation was observed in patients with MS. This difference was likely the result of higher intersubject noise variance in the patient population. GABA concentration correlated with M1 activation volume in patients but not in controls, and did not correlate with any fMRI metric in patients or controls.ConclusionOur data suggest that activation volume is a more robust measure than BOLD signal change in a group with high intersubject noise variance as in patients with MS. Additionally, this study demonstrated difference in correlation behavior between GABA concentration and the 2 fMRI metrics in patients with MS, suggesting that GABA - activation volume correlation is more appropriate measure in the patient group.     

Nonlinear responses of cerebral blood volume, blood flow and blood oxygenation signals during visual stimulation

Hemodynamic-based functional magnetic resonance imaging (fMRI) techniques provide a great utility for noninvasive functional brain mapping. However, because the hemodynamic signals reflect underlying neural activity indirectly, characterization of these signals following brain activation is essential for experimental design and data interpretation. In this report, the linear (or nonlinear) responses to neuronal activation of three hemodynamic parameters based primarily on changes of cerebral blood volume, blood flow and blood oxygenation were investigated by testing these hemodynamic responses' additivity property. Using a recently developed fMRI technique that acquires vascular space occupancy (VASO), arterial spin labeling (ASL) perfusion and blood oxygenation level-dependent (BOLD) signals simultaneously, the additivity property of the three hemodynamic responses in human visual cortex was assessed using various visual stimulus durations. Experiments on healthy volunteers showed that all three hemodynamic-weighted signals responded nonlinearly to stimulus durations less than 4 s, with the degree of nonlinearity becoming more severe as the stimulus duration decreased. Vascular space occupancy and ASL perfusion signals showed similar nonlinearity properties, whereas the BOLD signal was the most nonlinear. These data suggest that caution should be taken in the interpretation of hemodynamic-based signals in fMRI.     

Reliability of the depth-dependent high-resolution BOLD hemodynamic response in human visual cortex and vicinity

Functional magnetic resonance imaging (fMRI) often relies on a hemodynamic response function (HRF), the stereotypical blood oxygen level dependent (BOLD) response elicited by a brief (< 4 s) stimulus. Early measurements of the HRF used coarse spatial resolutions (≥ 3 mm voxels) that would generally include contributions from white matter, gray matter, and the extra-pial compartment (the space between the pial surface and skull including pial blood vessels) within each voxel. To resolve these contributions, high-resolution fMRI (0.9-mm voxels) was performed at 3 T in early visual cortex and its apposed white-matter and extra-pial compartments. The results characterized the depth dependence of the HRF and its reliability during nine fMRI sessions. Significant HRFs were observed in white-matter and extra-pial compartments as well as in gray matter. White-matter HRFs were faster and weaker than in the gray matter, while extra-pial HRFs were comparatively slower and stronger. Depth trends of the HRF peak amplitude were stable throughout a broad depth range that included all three compartments for each session. Across sessions, however, the depth trend of HRF peak amplitudes was stable only in the white matter and deep-intermediate gray matter, while there were strong session-to-session variations in the superficial gray matter and the extra-pial compartment. Thus, high-resolution fMRI can resolve significant and dynamically distinct HRFs in gray matter, white matter, and extra-pial compartments.     

Activation mapping as a percentage of local excitation: fMRI stability within scans, between scans and across field strengths

Functional magnetic resonance imaging (fMRI) does not typically yield highly reproducible maps of brain activation. Maps can vary significantly even with constant scanning parameters and consistent task performance conditions (Liu et al., Magn. Reson. Med., 2004, 52:751-760). Reproducibility is even more of a problem when comparing fMRI signal magnitude and spatial extent of activation across scans involving different task performance levels, scan durations, pulse sequences or magnetic field strengths. In this report, the consistency of fMRI was reexamined by considering the relative spatial and temporal distribution of fMRI blood oxygen level dependent (BOLD) activation signals separately from the absolute magnitude of the activation signal in each brain area. Subjects repeatedly performed the same simple motor task but under a variety of imaging conditions, using both spiral and standard echo-planar pulse sequences and at 1.5- and 4.0-T magnetic field strengths. The results demonstrate that the absolute amplitude of BOLD statistical activation signals varied significantly across time and scanning conditions, but the relative spatial pattern of BOLD activation was highly reproducible across all conditions. Analysis of realistic simulated fMRI data sets indicates that stability of relative activation patterns could provide a useful tool for assessing the accuracy of fMRI maps.     

Decreases in ADC observed in tissue areas during activation in the cat visual cortex at 9.4 T using high diffusion sensitization

Recent studies in the human visual cortex using diffusion-weighted functional magnetic resonance imaging (fMRI) have suggested that the apparent diffusion coefficient (ADC) decreases, in contrast to earlier studies that consistently reported ADC increases during neuronal activation. The changes, in either case, are hypothesized to provide the ability to improve the spatial specificity of fMRI over conventional blood-oxygenation-level-dependent (BOLD) methods. Most recently, the ADC decreases have been suggested as originating from transient cell swelling caused by either shrinkage of the extracellular space or some intracellular neuronal process that precedes the hemodynamic response. All of these studies have been conducted in humans and at lower magnetic fields, which can be limited by the signal-to-noise ratio (SNR). The low SNR can lead to significant partial-volume effects because of the lower spatial resolutions required to attain sufficient SNR in diffusion-weighted images. Human studies also have the potential confound of motion. At high magnetic fields and in animal model studies, these limitations are alleviated. At high fields, SNR increases, tissue signals are enhanced and signal changes inside the blood are significantly reduced compared to lower fields. In this work, we were able to measure a small but significant ADC decrease in tissue areas, in conjunction with brain activation in the cat visual cortex at 9.4 T when using highly diffusion-weighted images (b>1200 s/mm2) where intravascular effects are minimal. When using low b-values, delayed increases in the tissue ADC during activation were observed. No significant changes in ADC were observed in surface vessels for any diffusion weighting. Furthermore, we did not observe any temporal differences in the highly diffusion-weighted data compared to BOLD; however, although the changes may likely be vascular in nature, they are highly localized to the tissue areas.     

Development of visually evoked cortical activity in infant macaque monkeys studied longitudinally with fMRI

We studied the development of visual activation longitudinally in two infant monkeys aged 103-561 days using the BOLD fMRI technique under opiate anesthesia and compared the results with those obtained in three adult animals studied under identical conditions. Visual activation in primary visual cortex, V1, was strong and reliable in monkeys of the youngest and oldest ages, showing that functional imaging techniques give qualitatively similar results in infants and adults. Visual activation in extrastriate areas involved in processing motion (MT/V5) and form (V4) was not evident in the younger animals, but became more adult-like in the older animals. This delayed onset of measurable BOLD responses in extrastriate visual cortex may reflect delayed development of visual responses in these areas, although at this stage it is not possible to rule out either effects of anesthesia or of changes in cerebral vascular response mechanisms as the cause. The demonstration of visually evoked BOLD responses in young monkeys shows that the BOLD fMRI technique can usefully be employed to address functional questions of brain development.     

Coupling of simultaneously acquired electrophysiological and haemodynamic responses during visual stimulation

We investigate the relationship between the temporal variation in the magnitude of occipital visual evoked potentials (VEPs) and of haemodynamic measures of brain activity obtained using both blood oxygenation level dependent (BOLD) and perfusion sensitive (ASL) functional magnetic resonance imaging (fMRI). Volunteers underwent a continuous BOLD fMRI scan and/or a continuous perfusion-sensitive (gradient and spin echo readout) ASL scan, during which 30 second blocks of contrast reversing visual stimuli (at 4 Hz) were interleaved with 30 second blocks of rest (visual fixation). Electroencephalography (EEG) and fMRI were simultaneously recorded and following EEG artefact cleaning, VEPs were averaged across the whole stimulation block (120 reversals, VEP₁₂₀) and at a finer timescale (15 reversals, VEP₁₅). Both BOLD and ASL time-series were linearly modelled to establish: (1) the mean response to visual stimulation, (2) transient responses at the start and end of each stimulation block, (3) the linear decrease between blocks, (4) the nonlinear between-block variation (covariation with VEP₁₂₀), (5) the linear decrease within block and (6) the nonlinear variation within block (covariation with VEP₁₅).     

SSFP fMRI at 3 tesla: Efficiency of polar acquisition-reconstruction technique

SSFP-based fMRI techniques, known for their high specificity and low geometrical distortion, look promising for high-resolution brain mapping. Nevertheless, they suffer from lack of speed and sensitivity, leading them to be exploited mostly in high-field scanners. Radial acquisition can help with these inefficiencies through better tSNR and more effective coverage of the spatial frequencies. Here, we present a SSFP-fMRI approach and experimentally investigate it at 3 T scanners using radial readout for acquisition. In particular, the visual activity is mapped through three bSSFP techniques: 1- Cartesian, 2- Radial with re-gridding reconstruction, 3- Radial with Polar Fourier Transform (PFT) reconstruction. In the PFT technique streaking artifacts, generated at high acceleration rates by re-gridding reconstruction, are avoided and pixel size in the final framework is retrospectively selectable. General agreement, but better tSNR of Radial reading, was first confirmed for these techniques in detection of neural activities at 2 × 2 mm² in-plane resolution for all 28 subjects,. Next the outcome of the PFT algorithm with 1 × 1 mm² pixel size was compared to images reconstructed by re-gridding (from the same raw data) with the identical pixel size through interpolation. The localization of the activity showed improvement in PFT over interpolation both qualitatively (i.e., well-fitting in gray-matter) and quantitatively (i.e., higher z-scores and tSNR). The proposed technique can therefore be considered as a remedy for lack of speed and sensitivity in SSFP-based fMRI, in conventional field strengths. The proposed approach is particularly useful in task-based studies when we concentrate on a ROI considerably smaller than FOV, without sacrificing spatial resolution.     

Multi-phase passband balanced SSFP fMRI with 50 ms sampling rate at 7 Tesla enables high precision in resolving 100 ms neuronal events

Passband balanced steady state free precession (b-SSFP) fMRI employs the flat portion of the SSFP off-resonance response to obtain microscopic susceptibility changes elicited by changes in blood oxygenation following enhancement in neuronal activity. This technique can reduce geometric distortion and signal dropout while maintaining rapid acquisition and high signal-to-noise ratio (SNR) compared with traditional fMRI techniques. In the study, we developed a novel multi-phase passband b-SSFP fMRI technique that can achieve a spatial resolution of a few mm³ and a high temporal sampling rate of 50 ms per slice at 7 Tesla. This technique was further applied for an event-related (ER) fMRI paradigm. As a comparison, gradient-echo echo-planar imaging (GE-EPI) with similar spatial resolution and temporal sampling rate was carried out for the same ER-fMRI experiment. Experiments with visual cortex stimulation were carried out at 7 Tesla to demonstrate whether the multi-phase b-SSFP technique and GE-EPI are able to differentiate temporal delays in hemodynamic response function (HRF) separated by 100 ms in stimulus onset. Consistent with ERP results, the upslope of the HRF of both techniques can differentiate 100 ms delay in stimulus onset, with the former showing a lower level of intersubject variability. The present study demonstrated that the multi-phase passband b-SSFP fMRI technique can be applied for resolving neuronal events on the order of 100 ms at ultrahigh magnetic fields.