Explore the vast range of titles available.

Most fundamental cognitive processes rely on brain networks that include both cortical and subcortical structures. Studying such networks using functional magnetic resonance imaging (fMRI) requires a data acquisition protocol that provides blood-oxygenation-level dependent (BOLD) sensitivity across the entire brain. However, when using standard single echo, echo planar imaging protocols, researchers face a tradeoff between BOLD-sensitivity in cortex and in subcortical areas. Multi echo protocols avoid this tradeoff and can be used to optimize BOLD-sensitivity across the entire brain, at the cost of an increased repetition time. Here, we empirically compare the BOLD-sensitivity of a single echo protocol to a multi echo protocol. Both protocols were designed to meet the specific requirements for studying small, iron rich subcortical structures (including a relatively high spatial resolution and short echo times), while retaining coverage and BOLD-sensitivity in cortical areas. The results indicate that both sequences lead to similar BOLD-sensitivity across the brain at 7 ​T. •Single and multi echo EPI protocols were compared for studying subcortex and cortex.•Both protocols had high spatial resolution, short echo times and whole-brain coverage.•The results show marginal differences in BOLD-sensitivity across the brain.

•Tensor ICA can decompose multi-echo EPI data in time, space, and echo time domains.•Distribution across TEs separate BOLD and non-BOLD components of tensor ICA.•Elimination of the noise-related components enhances quality and activation patterns. Multi-echo echo-planar imaging (ME-EPI) acquires images at multiple echo times (TEs), enabling the differentiation of BOLD and non-BOLD fluctuations through TE-dependent analysis of transverse relaxation time and initial intensity. Decomposing ME-EPI in tensor space is a promising approach to characterize the distribution of changes across TEs (TE patterns) directly and aid the classification of components by providing information from an additional domain. In this study, the tensorial extension of independent component analysis (tensor-ICA) is used to characterize the TE patterns of neural and non-neural components in ME-EPI data. With the constraints of independent spatial maps, an ME-EPI dataset was decomposed into spatial, temporal, and TE domains to understand the TE patterns of noise or signal-related independent components. Our analysis revealed three distinct groups of components based on their TE patterns. Motion-related and other non-BOLD origin components followed decreased TE patterns. While the long-TE-peak components showed a large overlay on grey matter and signal patterns, the components that peaked at short TEs reflected noise that may be related to the vascular system, respiration, or cardiac pulsation, amongst others. Accordingly, removing short-TE peak components as part of a denoising strategy significantly improved quality control metrics and revealed clearer, more interpretable activation patterns compared to non-denoised data. To our knowledge, this work is the first application of decomposing ME-EPI in a tensor way. Our findings demonstrate that tensor-ICA is efficient in decomposing ME-EPI and characterizing the neural and non-neural TE patterns aiding in classifying components which is important for denoising fMRI data.

Gradient-recalled echo (GRE) echo-planar imaging (EPI) is an efficient MRI pulse sequence that is commonly used for several enticing applications, including functional MRI (fMRI), susceptibility-weighted imaging (SWI), and proton resonance frequency (PRF) thermometry. These applications are typically not performed in the mid-field (<1 T) as longer T2* and lower polarization present significant challenges. However, recent developments of mid-field scanners equipped with high-performance gradient sets offer the possibility to re-evaluate the feasibility of these applications. The paper introduces a metric “T2* contrast efficiency” for this evaluation, which minimizes dead time in the EPI sequence while maximizing T2* contrast so that the temporal and pseudo signal-to-noise ratios (SNRs) can be attained, which could be used to quantify experimental parameters for future fMRI experiments in the mid-field. To guide the optimization, T2* measurements of the cortical gray matter are conducted, focusing on specific regions of interest (ROIs). Temporal and pseudo SNR are calculated with the measured time-series EPI data to observe the echo times at which the maximum T2* contrast efficiency is achieved. T2* for a specific cortical ROI is reported at 0.5 T. The results suggest the optimized echo time for the EPI protocols is shorter than the effective T2* of that region. The effective reduction of dead time prior to the echo train is feasible with an optimized EPI protocol, which will increase the overall scan efficiency for several EPI-based applications at 0.5 T.

Functional magnetic resonance imaging (fMRI) research is routinely criticized for being statistically underpowered due to characteristically small sample sizes and much larger sample sizes are being increasingly recommended. Additionally, various sources of artifact inherent in fMRI data can have detrimental impact on effect size estimates and statistical power. Here we show how specific removal of non-BOLD artifacts can improve effect size estimation and statistical power in task-fMRI contexts, with particular application to the social-cognitive domain of mentalizing/theory of mind. Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA). Without smoothing, group-level effect size estimates on two different mentalizing tasks were enhanced by ME-ICA at a median rate of 24% in regions canonically associated with mentalizing, while much more substantial boosts (40–149%) were observed in non-canonical cerebellar areas. Effect size boosting occurs via reduction of non-BOLD noise at the subject-level and consequent reductions in between-subject variance at the group-level. Smoothing can attenuate ME-ICA-related effect size improvements in certain circumstances. Power simulations demonstrate that ME-ICA-related effect size enhancements enable much higher-powered studies at traditional sample sizes. Cerebellar effects observed after applying ME-ICA may be unobservable with conventional imaging at traditional sample sizes. Thus, ME-ICA allows for principled design-agnostic non-BOLD artifact removal that can substantially improve effect size estimates and statistical power in task-fMRI contexts. ME-ICA could mitigate some issues regarding statistical power in fMRI studies and enable novel discovery of aspects of brain organization that are currently under-appreciated and not well understood. •ME-ICA enhances effect size estimates in block-design task-based fMRI studies•Effect sizes in canonical mentalizing regions are boosted at median rate of 24%•Cerebellar effect size estimates are boosted by 40–149%•Enhanced effect size enables highly-powered studies at traditional sample sizes•Enables potential for novel discoveries hidden in small underpowered studies

In blood-oxygen-level-dependent (BOLD)-based resting-state functional (RS-fMRI) studies, usage of multi-echo echo-planar-imaging (ME-EPI) is limited due to unacceptable late echo times when high spatial resolution is used. Equipped with high-performance gradients, the compact 3T MRI system (C3T) enables a three-echo whole-brain ME-EPI protocol with smaller than 2.5 mm isotropic voxel and shorter than 1 s repetition time, as required in landmark fMRI studies. The performance of the ME-EPI was comprehensively evaluated with signal variance reduction and region-of-interest-, seed- and independent-component-analysis-based functional connectivity analyses and compared with a counterpart of single-echo EPI with the shortest TR possible. Through the multi-echo combination, the thermal noise level is reduced. Functional connectivity, as well as signal intensity, are recovered in the medial orbital sulcus and anterior transverse collateral sulcus in ME-EPI. It is demonstrated that ME-EPI provides superior sensitivity and accuracy for detecting functional connectivity and/or brain networks in comparison with single-echo EPI. In conclusion, the high-performance gradient enabled high-spatial-temporal resolution ME-EPI would be the method of choice for RS-fMRI study on the C3T.

We compared the sensitivity of standard single-shot 2D echo planar imaging (EPI) to three advanced EPI sequences, i.e., 2D multi-echo EPI, 3D high resolution EPI and 3D dual-echo fast EPI in fixed effect and random effects group level fMRI analyses at 3T. The study focused on how well the variance reduction in fixed effect analyses achieved by advanced EPI sequences translates into increased sensitivity in the random effects group level analysis. The sensitivity was estimated in a functional MRI experiment of an emotional learning and a reward based learning tasks in a group of 24 volunteers. Each experiment was acquired with the four different sequences. The task-related response amplitude, contrast level and respective t-value were proxies for the functional sensitivity across the brain. All three advanced EPI methods increased the sensitivity in the fixed effects analyses, but standard single-shot 2D EPI provided a comparable performance in random effects group analysis when whole brain coverage and moderate resolution are required. In this experiment inter-subject variability determined the sensitivity of the random effects analysis for most brain regions, making the impact of EPI pulse sequence improvements less relevant or even negligible for random effects analyses. An exception concerns the optimization of EPI reducing susceptibility-related signal loss that translates into an enhanced sensitivity e.g. in the orbitofrontal cortex for multi-echo EPI. Thus, future optimization strategies may best aim at reducing inter-subject variability for higher sensitivity in standard fMRI group studies at moderate spatial resolution. •We compared the sensitivity of standard and three advanced EPI sequences in group level fMRI analysis.•Advanced EPI methods increased the sensitivity in the fixed effect group analyses.•All EPI methods provided similar sensitivity in random effect group analysis.•Inter-subject variance determined the sensitivity of the random effect analysis.•Future sequence optimization best aim at reducing inter-subject rather then intra-subject variance.

Standard T2* weighted functional magnetic resonance imaging (fMRI) performed with echo-planar imaging (EPI) suffers from signal loss in the ventromedial prefrontal cortex (vmPFC) due to macroscopic field inhomogeneity. However, this region is of special interest to affective neuroscience and psychiatry. The Multi-echo EPI (MEPI) approach has several advantages over EPI but its performance against EPI in the vmPFC has not yet been examined in a study with sufficient statistical power using a task specifically eliciting activity in this region. We used a fear conditioning task with MEPI to compare the performance of MEPI and EPI in vmPFC and control regions in 32 healthy young subjects. We analyzed activity associated with short (12ms), standard (29ms) and long (46ms) echo times, and a voxel-wise combination of these three echo times. Behavioral data revealed successful differentiation of the conditioned versus safety stimulus; activity in the vmPFC was shown by the contrast “safety stimulus > conditioned stimulus” as in previous research and proved significantly stronger with the combined MEPI than standard single-echo EPI. Then, we aimed to demonstrate that the additional cluster extent (ventral extension) detected in the vmPFC with MEPI reflects activation in a relevant cluster (i.e., not just non-neuronal noise). To do this, we used resting state data from the same subjects to show that the time-course of this region was both connected to bilateral amygdala and the default mode network. Overall, we demonstrate that MEPI (by means of the weighted sum combination approach) outperforms standard EPI in vmPFC; MEPI performs always at least as good as the best echo time for a given brain region but provides all necessary echo times for an optimal BOLD sensitivity for the whole brain. This is relevant for affective neuroscience and psychiatry given the critical role of the vmPFC in emotion regulation.

Echo-planar imaging (EPI) is a standard procedure in functional magnetic resonance imaging (fMRI) for measuring changes in the blood oxygen level-dependent (BOLD) signal associated with neuronal activity. The images obtained from fMRI with EPI, however, exhibit signal dropouts and geometric distortions. Parallel imaging (PI), due to its short readout, accelerates image acquisition and might reduce dephasing in phase-encoding direction. The concomitant loss of signal-to-noise ratio (SNR) might be compensated through single-shot multi-echo EPI (mEPI). We systematically compared the temporal SNR and BOLD sensitivity of single echoes (TE=15, 45, and 75ms) and contrast-optimized mEPI with and without PI and mEPI-based denoising. Audio-visual stimulation under natural viewing conditions activated distributed neural networks. Heterogeneous SNR, noise gain, and sensitivity maps emerged. In single echoes, SNR and BOLD sensitivity followed the predicted dependency on echo time (TE) and were reduced under PI. However, the combination of echoes with mEPI recovered the quality parameters and increased BOLD signal changes at circumscribed fronto-polar and deep brain structures. We suggest applying PI only in combination with mEPI to reduce imaging artifacts and conserve BOLD sensitivity. •Parallel imaging reduces SNR in fMRI globally with local improvements.•Multi-echo EPI recovers quality parameters and yields locally improved BOLD signal.•Combined multi-echo and parallel imaging EPI optimizes BOLD imaging.•Natural viewing activates distributed networks for BOLD sensitivity mapping.•Multi-echo EPI can be used for denoising.

Dual-echo EPI is based on the acquisition of two images with different echo times per excitation, thus allowing for the calculation of purely T2 * weighted data. The technique can be used for the measurement of functional activation whenever the prerequisite of constant equilibrium magnetization cannot be fulfilled due to variable inter-volume delays. The latter is the case when image acquisition is triggered by physiological parameters (e.g. cardiac gating) or by the subject's response. Despite its frequent application, there is currently no standardized way of combining the information obtained from the two acquired echoes. The goal of this study was to quantify the implication of different echo combination methods (quotients of echoes and quantification of T 2 *) and calculation modalities, either pre-smoothing data before combination or subjecting unsmoothed combined data to masking (no masking, volume-wise masking, joint masking), on the theoretically predicted signal-to-noise ratio (SNR) of the BOLD response and on activation results of two fMRI experiments using finger tapping and visual stimulation in one group ( n = 5) and different motor paradigms to activate motor areas in the cortex and the brainstem in another group ( n = 21). A significant impact of echo combination and masking procedure was found for both SNR and activation results. The recommended choice is a direct calculation of T 2 * values, either using joint masking on unsmoothed data, or pre-smoothing images prior to T 2 * calculation. This method was most beneficial in areas close to the surface of the brain or adjacent to the ventricles and may be especially relevant to brainstem fMRI.

•Introduction of multi-echo center-out EPI for investigating concomitant CBF and BOLD measurement.•ΔCBF timecourses closely follow those of ΔT2* in regions of positive (PBR) and negative BOLD response (NBR).•Decreases in CBF appear to warrant a larger change in NBR than CBF increases in PBR regions.•Consideration of baseline CBF important in comparisons of relative coupling ratios between brain regions.•Indications of differing inhibitory contributions and control of CBF in NBR regions. Unlike the positive blood oxygenation level-dependent (BOLD) response (PBR), commonly taken as an indication of an ‘activated’ brain region, the physiological origin of negative BOLD signal changes (i.e. a negative BOLD response, NBR), also referred to as ‘deactivation’ is still being debated. In this work, an attempt was made to gain a better understanding of the underlying mechanism by obtaining a comprehensive measure of the contributing cerebral blood flow (CBF) and its relationship to the NBR in the human visual cortex, in comparison to a simultaneously induced PBR in surrounding visual regions. To overcome the low signal-to-noise ratio (SNR) of CBF measurements, a newly developed multi-echo version of a center-out echo planar-imaging (EPI) readout was employed with pseudo-continuous arterial spin labeling (pCASL). It achieved very short echo and inter-echo times and facilitated a simultaneous detection of functional CBF and BOLD changes at 3 T with improved sensitivity. Evaluations of the absolute and relative changes of CBF and the effective transverse relaxation rate, R2*, the coupling ratios, and their dependence on CBF at rest, CBFrest, indicated differences between activated and deactivated regions. Analysis of the shape of the respective functional responses also revealed faster negative responses with more pronounced post-stimulus transients. Resulting differences in the flow-metabolism coupling ratios were further examined for potential distinctions in the underlying neuronal contributions. [Display omitted]