Temporal Signal-to-Noise Changes in Combined Multislice- and In-Plane-Accelerated Echo-Planar Imaging with a 20- and 64-Channel Coil

Local and global effects of sedation in resting-state fMRI: a randomized, placebo-controlled comparison between etifoxine and alprazolam

TSPO ligands are promising alternatives to benzodiazepines in the treatment of anxiety, as they display less pronounced side effects such as sedation, cognitive impairment, tolerance development and abuse potential. In a randomized double-blind repeated-measures study we compare a benzodiazepine (alprazolam) to a TSPO ligand (etifoxine) by assessing side effects and acquiring resting-state fMRI data from 34 healthy participants after 5 days of taking alprazolam, etifoxine or a placebo. To study the effects of the pharmacological interventions in fMRI in detail and across different scales, we combine in our study complementary analysis strategies related to whole-brain functional network connectivity, local connectivity analysis expressed in regional homogeneity, fluctuations in low-frequency BOLD amplitudes and coherency of independent resting-state networks. Participants reported considerable adverse effects such as fatigue, sleepiness and concentration impairments, related to the administration of alprazolam compared to placebo. In resting-state fMRI we found a significant decrease in functional connection density, network efficiency and a decrease in the networks rich-club coefficient related to alprazolam. While observing a general decrease in regional homogeneity in high-level brain networks in the alprazolam condition, we simultaneously could detect an increase in regional homogeneity and resting-state network coherence in low-level sensory regions. Further we found a general increase in the low-frequency compartment of the BOLD signal. In the etifoxine condition, participants did not report any significant side effects compared to the placebo, and we did not observe any corresponding modulations in our fMRI metrics. Our results are consistent with the idea that sedation globally disconnects low-level functional networks, but simultaneously increases their within-connectivity. Further, our results point towards the potential of TSPO ligands in the treatment of anxiety and depression.

Inhalation-modulated detection of olfactory BOLD responses in the human brain

Introduction

In contrast to other sensory domains, detection of primary olfactory processes using functional magnetic resonance imaging has proven to be notably challenging with conventional block designs. This difficulty arises from significant habituation and hemodynamic responses in olfactory areas that do not appear to align with extended boxcar functions convolved with a generic hemodynamic response model. Consequently, some researchers have advocated for a transition to event-related designs, despite their known lower detection power compared to block designs.

Methods

Here, we conducted a block design experiment with 16s of continuous odorant stimulation alternating with 16s of continuous odorless air stimulation in 33 healthy participants. We compared four statistical analyses that relied either on standard block designs (SBD1-2) or on block designs that were modulated by the participants' individual breathing patterns (MBD1-2).

Results

We found that such modulated block designs were comparatively more powerful than standard block designs, despite having a substantially lower design efficiency. Using whole-brain effect size maps, we observed that the right insular and medial aspects of the left piriform cortex exhibited a preference for a breathing-modulated analysis approach.

Discussion

Research in olfaction that necessitates designs with longer-lasting blocks, such as those employed in the investigation of state-dependent processing, will benefit from the breathing-modulated analyses outlined in this study.

Locally Low-Rank Denoising of Multi-Echo Functional MRI Data With Application in Resting-State Analysis

OBJECTIVES

Locally low-rank (LLR) denoising of functional magnetic resonance imaging (fMRI) time series image data is extended to multi-echo (ME) data. The proposed method extends the capabilities of non-physiologic noise suppression beyond single-echo applications with a dedicated ME algorithm.

MATERIALS AND METHODS

Following an institutional review board (IRB) approved protocol, resting-state fMRI data were acquired in 7 healthy subjects. A compact 3T scanner enabled whole-brain acquisition of multiband ME fMRI data at high spatial resolution (1.4 × 1.4 × 2.8 mm 3 ) with a 1810 ms repetition time (TR). Image data were denoised with ME-LLR preceding functional processing. The results of connectivity maps generated from denoised data were compared with maps generated with equivalent processing of non-denoised images. To assess ME-LLR as a method to reduce scan time, comparisons were made between maps computed from image data with full and retrospectively truncated durations. Assessments were completed with seed-based connectivity analyses using echo-combined image data. In a feasibility assessment, nondenoised and denoised full-duration echo-combined data were equivalently processed with independent component analysis (ICA) and compared.

RESULTS

ME-LLR denoising yielded strengthened resting-state network connectivity maps after nuisance regression and seed-based connectivity analysis. In assessing ME-LLR as a scan reduction mechanism, maps generated from denoised data at half scan time showed comparable quality with maps generated from full-duration, non-denoised data, at both single subject and group levels. ME-LLR substantially increased temporal signal-to-noise ratio (tSNR) for image data respective to each individual echo and for image data after nuisance regression. Among echo-specific image volumes, increases in tSNR yielded by ME-LLR were most pronounced for image data with the longest echo time and thereby lowest SNR. ICA showed resting-state networks consistently identified between non-denoised and denoised data, with clearer demarcation of networks for ME-LLR.

CONCLUSIONS

ME-LLR is demonstrated to suppress non-physiologic noise, enhance functional connectivity map quality, and could potentially facilitate scan time reduction in ME-fMRI.

Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data

OBJECTIVE

This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps.

METHODS

Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked t-statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject.

RESULTS

fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant (p = 4.88×10-4 to p = 0.042; one p = 0.062) increases in consensus t-statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t-statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data.

CONCLUSION

LLR denoising affords robust increases in t-statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.

Recommendations of Choice of Head Coil and Prescan Normalize Filter Depend on Region of Interest and Task

The performance of MRI head coils together with the influence of the prescan normalize filter in different brain regions was evaluated. Functional and structural data were recorded from 26 participants performing motor, auditory, and visual tasks in different conditions: with the 20- and 64-channel Siemens head/neck coil and the prescan normalize filter turned ON or OFF. Data were analyzed with the MRIQC tool to evaluate data quality differences. The functional data were statistically evaluated by comparison of the β estimates and the time-course signal-to-noise ratio (tSNR) in four regions of interest, i.e., the auditory, visual, and motor cortices and the thalamus. The MRIQC tool indicated a better data quality for both functional and structural data with the prescan normalize filter, with an advantage for the 20-channel head coil in functional data and an advantage for the 64-channel head coil in structural measurements. Nevertheless, recommendations for the functional data regarding choice of head coils and prescan normalize filter depend on the brain regions of interest. Higher β estimates and tSNR values occurred in the auditory cortex and thalamus with the prescan normalize filter, whereas the contrary was true for the visual and motor cortices. Due to higher β estimates in the visual cortex in the 64-channel head coil, this head coil is recommended for studies investigating the visual cortex. For most of the research questions, the 20-channel head coil is better suited for functional experiments, with the prescan normalize filter, especially when investigating deep brain areas. For anatomical studies, the 64-channel head coil seemed to be the better choice.

Which multiband factor should you choose for your resting-state fMRI study?

Multiband acquisition, also called simultaneous multislice, has become a popular technique in resting-state functional connectivity studies. Multiband (MB) acceleration leads to a higher temporal resolution but also leads to spatially heterogeneous noise amplification, suggesting the costs may be greater in areas such as the subcortex. We evaluate MB factors of 2, 3, 4, 6, 8, 9, and 12 with 2 mm isotropic voxels, and additionally 2 mm and 3.3 mm single-band acquisitions, on a 32-channel head coil. Noise amplification was greater in deeper brain regions, including subcortical regions. Correlations were attenuated by noise amplification, which resulted in spatially varying biases that were more severe at higher MB factors. Temporal filtering decreased spatial biases in correlations due to noise amplification, but also tended to decrease effect sizes. In seed-based correlation maps, left-right putamen connectivity and thalamo-motor connectivity were highest in the single-band 3.3 mm protocol. In correlation matrices, MB 4, 6, and 8 had a greater number of significant correlations than the other acquisitions (both with and without temporal filtering). We recommend single-band 3.3 mm for seed-based subcortical analyses, and MB 4 provides a reasonable balance for studies analyzing both seed-based correlation maps and connectivity matrices. In multiband studies including secondary analyses of large-scale datasets, we recommend reporting effect sizes or test statistics instead of correlations. If correlations are reported, temporal filtering (or another method for thermal noise removal) should be used. The Emory Multiband Dataset is available on OpenNeuro.

Streamlined magnetic resonance fingerprinting: Fast whole-brain coverage with deep-learning based parameter estimation

Magnetic resonance fingerprinting (MRF) is a quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters as well as metrics of field inhomogeneity in a single acquisition. However, current challenges exist in the forms of (1) scan time; (2) need for custom image reconstruction; (3) large dictionary sizes; (4) long dictionary-matching time. This study aims to introduce a novel streamlined magnetic-resonance fingerprinting (sMRF) framework based on a single-shot echo-planar imaging (EPI) sequence to simultaneously estimate tissue T1, T2, and T2* with integrated B1⁺ correction. Encouraged by recent work on EPI-based MRF, we developed a method that combines spin-echo EPI with gradient-echo EPI to achieve T2 in addition to T1 and T2* quantification. To this design, we add simultaneous multi-slice (SMS) acceleration to enable full-brain coverage in a few minutes. Moreover, in the parameter-estimation step, we use deep learning to train a deep neural network (DNN) to accelerate the estimation process by orders of magnitude. Notably, due to the high image quality of the EPI scans, the training process can rely simply on Bloch-simulated data. The DNN also removes the need for storing large dictionaries. Phantom scans along with in-vivo multi-slice scans from seven healthy volunteers were acquired with resolutions of 1.1×1.1×3 mm³ and 1.7×1.7×3 mm³, and the results were validated against ground truth measurements. Excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches. In the phantom scan, a strong linear relationship (R = 1-1.04, R²>0.96) was found for all parameter estimates, with a particularly high agreement for T2 estimation (R²>0.99). Similar findings are reported for the in-vivo human data for all of our parameter estimates. Incorporation of DNN results in a reduction of parameter estimation time on the order of 1000 x and a reduction in storage requirements on the order of 2500 x while achieving highly similar results as conventional dictionary matching (%differences of 7.4 ± 0.4%, 3.6 ± 0.3% and 6.0 ± 0.4% error in T1, T2, and T2* estimation). Thus, sMRF has the potential to be the method of choice for future MRF studies by providing ease of implementation, fast whole-brain coverage, and ultra-fast T1/T2/T2* estimation.

The benefit of high-performance gradients on echo planar imaging for BOLD-based resting-state functional MRI

Improved gradient performance in an MRI system reduces distortion in echo planar imaging (EPI), which has been a key imaging method for functional studies. A lightweight, low-cryogen compact 3T MRI scanner (C3T) is capable of achieving 80 mT m^-1 gradient amplitude with 700 T m^-1 s^-1 slew rate, in comparison with a conventional whole-body 3T MRI scanner (WB3T, 50 mT m^-1 with 200 T m^-1 s^-1). We investigated benefits of the high-performance gradients in a high-spatial-resolution (1.5 mm isotropic) functional MRI study. Reduced echo spacing in the EPI pulse sequence inherently leads to less severe geometric distortion, which provided higher accuracy than with WB3T for registration between EPI and anatomical images. The cortical coverage of C3T datasets was improved by more accurate signal depiction (i.e. less dropout or pile-up). Resting-state functional analysis results showed that greater magnitude and extent in functional connectivity (FC) for the C3T than the WB3T when the selected seed region is susceptible to distortions, while the FC matrix for well-known brain networks showed little difference between the two scanners. This shows that the improved quality in EPI is particularly valuable for studying certain brain regions typically obscured by severe distortion.

Supracategorical fear information revealed by aversively conditioning multiple categories

Fear-generalization is a critical function for survival, in which an organism extracts information from a specific instantiation of a threat (e.g., the western diamondback rattlesnake in my front yard on Sunday) and learns to fear - and accordingly respond to - pertinent higher-order information (e.g., snakes live in my yard). Previous work investigating fear-conditioning in humans has used functional magnetic resonance imaging (fMRI) to demonstrate that activity patterns representing stimuli from an aversively-conditioned category (CS+) are more similar to each other than those of a neutral category (CS-). Here we used fMRI and multiple aversively-conditioned categories to ask whether we would find only similarity increases within the CS+ categories or also similarity increases between the CS+ categories. Using representational similarity analysis, we correlated several models to activity patterns underlying different brain regions and found that, following fear-conditioning, between-category and within-category similarity increased for the CS+ categories in the insula, superior frontal gyrus (SFG), and the right temporal pole. When specifically investigating fear-generalization, these between- and within-category effects were detected in the SFG. These results advance prior pattern-based neuroimaging work by exploring the effect of aversively-conditioning multiple categories and indicate an extended role for such regions in potentially representing supracategorical information during fear-learning.

New acquisition techniques and their prospects for the achievable resolution of fMRI

This work reviews recent advances in technologies for functional magnetic resonance imaging (fMRI) of the human brain and highlights the push for higher functional specificity based on increased spatial resolution and specific MR contrasts to reveal previously undetectable functional properties of small-scale cortical structures. We discuss how the combination of MR hardware, advanced acquisition techniques and various MR contrast mechanisms have enabled recent progress in functional neuroimaging. However, these advanced fMRI practices have only been applied to a handful of neuroscience questions to date, with the majority of the neuroscience community still using conventional imaging techniques. We thus discuss upcoming challenges and possibilities for fMRI technology development in human neuroscience. We hope that readers interested in functional brain imaging acquire an understanding of current and novel developments and potential future applications, even if they don't have a background in MR physics or engineering. We summarize the capabilities of standard fMRI acquisition schemes with pointers to relevant literature and comprehensive reviews and introduce more recent developments.