The impact of ultra-high field MRI on cognitive and computational neuroimaging

A 128-channel receive array with enhanced SNR performance for 10.5 tesla brain imaging

Purpose

To develop and characterize the performance of a 128-channel head array for brain imaging at 10.5 tesla and evaluate the potential of brain imaging at this unique, >10 tesla magnetic field.

Methods

The coil is composed of a 16-channel self-decoupled loop transmit/receive array with a 112-loop receive-only (Rx) insert. Interactions between the outer transmitter and the inner 112Rx insert were mitigated using coaxial cable traps placed every 1/16 of a wavelength on each feed cable, locating most preamplifier boards outside the transmitter field and miniaturizing those placed directly on individual coils.

Results

The 128-channel array described herein achieved 77% of ultimate intrinsic SNR in the center of the brain. Transmit field maps obtained experimentally on a phantom with and without the receive array were similar and matched EM simulations, leading to FDA approval for human imaging. Anatomical and functional data, including with power demanding sequences, were acquired successfully on human volunteers.

Conclusions

Counterintuitive to expectations based on magnetic fields ≤7T, the higher channel counts provided SNR gains centrally, capturing ∼80% uiSNR. Fraction of uiSNR achieved centrally in 64Rx, 80Rx, and 128Rx arrays suggested that a plateau was being reached at 80%. At this plateau, linear to approximately quadratic B 0 dependent SNR gains for the periphery and the center, respectively, were observed for 10.5T relative 7T.

RF coil design strategies for improving SNR at the ultrahigh magnetic field of 10.5T

PURPOSE

Toward pushing the boundaries of ultrahigh fields for human brain imaging, we wish to evaluate experimentally achievable SNR relative to ultimate intrinsic SNR (uiSNR) at 10.5T, develop design strategies toward approaching the latter, quantify magnetic field-dependent SNR gains, and demonstrate the feasibility of whole-brain, high-resolution human brain imaging at this uniquely high field strength.

METHODS

A dual row 16-channel self-decoupled transmit (Tx) and receive (Rx) array was developed for 10.5T using custom Tx/Rx switches. A 64-channel receive-only array was built to fit into the 16-channel Tx/Rx array. Electromagnetic modeling and experiments were used to define safe operational power limits. Experimental SNR was evaluated relative to uiSNR at 10.5T and 7T.

RESULTS

The 64-channel Rx array alone captured approximately 50% of the central uiSNR at 10.5T, while an identical array developed for 7T captured about 76% of uiSNR at 7T. The 16-channel Tx/80-channel Rx configuration brought the fraction of uiSNR captured at 10.5T to levels comparable to the 64-channel Rx array at 7T. SNR data displayed an approximateB 0 2 $$ {\mathrm{B}}_0^2 $$ dependence over a large central region when evaluated in the context of uiSNR. Whole-brain, high-resolutionT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted and T1-weighted anatomical and gradient-recalled-echo BOLD-EPI functional MRI images were obtained at 10.5T for the first time with such an advanced array.

CONCLUSION

We demonstrated the ability to approach the uiSNR at 10.5T over the human brain, achieving large SNR gains over 7T, currently the most commonly used ultrahigh-field platform. Whole-brain, high-resolution anatomical and EPI-based functional MRI data were obtained at 10.5T, illustrating the promise of greater than 10T fields in studying the human brain.

FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes

Invasive and non-invasive electrophysiological measurements during "cocktail-party"-like listening indicate that neural activity in the human auditory cortex (AC) "tracks" the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl's gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal's sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the single speaker and concurrent speakers conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.

Performance of receive head arrays versus ultimate intrinsic SNR at 7 T and 10.5 T

PURPOSE

We examined magnetic field dependent SNR gains and ability to capture them with multichannel receive arrays for human head imaging in going from 7 T, the most commonly used ultrahigh magnetic field (UHF) platform at the present, to 10.5 T, which represents the emerging new frontier of >10 T in UHFs.

METHODS

Electromagnetic (EM) models of 31-channel and 63-channel multichannel arrays built for 10.5 T were developed for 10.5 T and 7 T simulations. A 7 T version of the 63-channel array with an identical coil layout was also built. Array performance was evaluated in the EM model using a phantom mimicking the size and electrical properties of the human head and a digital human head model. Experimental data was obtained at 7 T and 10.5 T with the 63-channel array. Ultimate intrinsic SNR (uiSNR) was calculated for the two field strengths using a voxelized cloud of dipoles enclosing the phantom or the digital human head model as a reference to assess the performance of the two arrays and field depended SNR gains.

RESULTS

uiSNR calculations in both the phantom and the digital human head model demonstrated SNR gains at 10.5 T relative to 7 T of 2.6 centrally, ˜2 at the location corresponding to the edge of the brain, ˜1.4 at the periphery. The EM models demonstrated that, centrally, both arrays captured ˜90% of the uiSNR at 7 T, but only ˜65% at 10.5 T, leading only to ˜2-fold gain in array SNR in going from 7 to 10.5 T. This trend was also observed experimentally with the 63-channel array capturing a larger fraction of the uiSNR at 7 T compared to 10.5 T, although the percentage of uiSNR captured were slightly lower at both field strengths compared to EM simulation results.

CONCLUSIONS

Major uiSNR gains are predicted for human head imaging in going from 7 T to 10.5 T, ranging from ˜2-fold at locations corresponding to the edge of the brain to 2.6-fold at the center, corresponding to approximately quadratic increase with the magnetic field. Realistic 31- and 63-channel receive arrays, however, approach the central uiSNR at 7 T, but fail to do so at 10.5 T, suggesting that more coils and/or different type of coils will be needed at 10.5 T and higher magnetic fields.

Precision fMRI and cluster-failure in the individual brain

Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal-to-noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level-dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single-subject analysis. We introduce adaptive-weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster-corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole-brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family-wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download.

RF coil design strategies for improving SNR at the ultrahigh magnetic field of 10.5 Tesla

Purpose

To develop multichannel transmit and receive arrays towards capturing the ultimate-intrinsic-SNR (uiSNR) at 10.5 Tesla (T) and to demonstrate the feasibility and potential of whole-brain, high-resolution human brain imaging at this high field strength.

Methods

A dual row 16-channel self-decoupled transmit (Tx) array was converted to a 16Tx/Rx transceiver using custom transmit/receive switches. A 64-channel receive-only (64Rx) array was built to fit into the 16Tx/Rx array. Electromagnetic modeling and experiments were employed to define safe operation limits of the resulting 16Tx/80Rx array and obtain FDA approval for human use.

Results

The 64Rx array alone captured approximately 50% of the central uiSNR at 10.5T while the identical 7T 64Rx array captured ∼76% of uiSNR at this lower field strength. The 16Tx/80Rx configuration brought the fraction of uiSNR captured at 10.5T to levels comparable to the performance of the 64Rx array at 7T. SNR data obtained at the two field strengths with these arrays displayed dependent increases over a large central region. Whole-brain high resolution T 2 * and T 1 weighted anatomical and gradient-recalled echo EPI BOLD fMRI images were obtained at 10.5T for the first time with such an advanced array, illustrating the promise of >10T fields in studying the human brain.

Conclusion

We demonstrated the ability to approach the uiSNR at 10.5T over the human brain with a novel, high channel count array, achieving large SNR gains over 7T, currently the most commonly employed ultrahigh field platform, and demonstrate high resolution and high contrast anatomical and functional imaging at 10.5T.

Neuronal modeling of magnetoencephalography responses in auditory cortex to auditory and visual stimuli

Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) have suggested a bottom-up feedforward (FF) type laminar profile for auditory evoked but top-down feedback (FB) type for cross-sensory visual evoked activity in the auditory cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex region of interest, auditory evoked responses showed peaks at 37 and 90 ms and cross-sensory visual responses at 125 ms. The inputs to the auditory cortex were then modeled through FF and FB type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which consists of a neocortical circuit model linking the cellular- and circuit-level mechanisms to MEG. The HNN models suggested that the measured auditory response could be explained by an FF input followed by an FB input, and the crosssensory visual response by an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG/EEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.

Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC

Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise - the dominant contributing noise component in high resolution fMRI. NORDIC PCA is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. As investigating auditory functional responses poses unique challenges, we anticipated that the benefit of this technique would be especially pronounced. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we also observed a reduction in the average response amplitude (percent signal), which may suggest that a small amount of signal was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.

Effects of ultra-high field MRI environment on cognitive performance in healthy participants

INTRODUCTION

Ultra-high field MRI (UHF MRI) is rapidly becoming an essential part of our toolbox within health care and research studies; therefore, we need to get a deeper understanding of the physiological effects of ultra-high field. This study aims to investigate the cognitive performance of healthy participants in a 7 T (T) MRI environment in connection with subjectively experienced effects.

METHODS

We measured cognitive performance before and after a 1-h 7T MRI scanning session using a Digit Symbol Substitution Test (DSST) in 42 subjects. Furthermore, a computer-based survey regarding the subjectively experienced effects in connection with the MRI examination was distributed. Similarly, two DSSTs were also performed by a control group of 40 participants.

RESULTS

Even though dizziness was the strongest sensory perception in connection to the MRI scanning, we did not find any correlation between dizziness and cognitive performance. Whilst the control group improved (p=<0.001) on their second DSST the MRI group showed no significant difference (p=0.741) in the DSST before and after MRI scanning.

CONCLUSION

Transient effect on cognition after undergoing MRI scanning can't be ruled out as the expected learning effect on the DSST was not observed.

IMPLICATIONS FOR PRACTICE

Increasing understanding of the possible adverse effects may guide operators in performing UHF MRI in a safe way and with person-centered care. Furthermore, it can guide researchers in setting up research protocols to minimize confounding factors in their fMRI studies due to the transient adverse effects of the UHF environment.

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T

At ultrahigh field strengths images of the body are hampered by B1 -field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a "bias field" to the ideal image. Current bias field correction methods, such as the N4 algorithm, assume a low frequency bias field, which is not sufficiently valid for T2w images at 7 T. In this work we propose a deep learning based bias field correction method to address this issue for T2w prostate images at 7 T. By combining simulated B1 -field distributions of a multi-transmit setup at 7 T with T2w prostate images at 1.5 T, we generated artificial 7 T images for which the homogeneous counterpart was available. Using these paired data, we trained a neural network to correct the bias field. We predicted either a homogeneous image (t-Image neural network) or the bias field (t-Biasf neural network). In addition, we experimented with the single-channel images of the receive array and the corresponding sum of magnitudes of this array as the input image. Testing was carried out on four datasets: the test split of the synthetic training dataset, volunteer and patient images at 7 T, and patient images at 3 T. For the test split, the performance was evaluated using the structural similarity index measure, Wasserstein distance, and root mean squared error. For all other test data, the features Homogeneity and Energy derived from the gray level co-occurrence matrix (GLCM) were used to quantify the improvement. For each test dataset, the proposed method was compared with the current gold standard: the N4 algorithm. Additionally, a questionnaire was filled out by two clinical experts to assess the homogeneity and contrast preservation of the 7 T datasets. All four proposed neural networks were able to substantially reduce the B1 -field induced inhomogeneities in T2w 7 T prostate images. By visual inspection, the images clearly look more homogeneous, which is confirmed by the increase in Homogeneity and Energy in the GLCM, and the questionnaire scores from two clinical experts. Occasionally, changes in contrast within the prostate were observed, although much less for the t-Biasf network than for the t-Image network. Further, results on the 3 T dataset demonstrate that the proposed learning based approach is on par with the N4 algorithm. The results demonstrate that the trained networks were capable of reducing the B1 -field induced inhomogeneities for prostate imaging at 7 T. The quantitative evaluation showed that all proposed learning based correction techniques outperformed the N4 algorithm. Of the investigated methods, the single-channel t-Biasf neural network proves most reliable for bias field correction.

Not all discounts are created equal: Regional activity and brain networks in temporal and effort discounting

Reward outcomes associated with costs like time delay and effort investment are generally discounted in decision-making. Standard economic models predict rewards associated with different types of costs are devalued in a similar manner. However, our review of rodent lesion studies indicated partial dissociations between brain regions supporting temporal- and effort-based decision-making. Another debate is whether options involving low and high costs are processed in different brain substrates (dual-system) or in the same regions (single-system). This research addressed these issues using coordinate-based, connectivity-based, and activation network-based meta-analyses to identify overlapping and separable neural systems supporting temporal (39 studies) and effort (20 studies) discounting. Coordinate-based activation likelihood estimation and resting-state connectivity analyses showed immediate-small reward and delayed-large reward choices engaged distinct regions with unique connectivity profiles, but their activation network mapping was found to engage the default mode network. For effort discounting, salience and sensorimotor networks supported low-effort choices, while the frontoparietal network supported high-effort choices. There was little overlap between the temporal and effort networks. Our findings underscore the importance of differentiating different types of costs in decision-making and understanding discounting at both regional and network levels.

Improved dynamic distortion correction for fMRI using single-echo EPI and a readout-reversed first image (REFILL)

The boundaries between tissues with different magnetic susceptibilities generate inhomogeneities in the main magnetic field which change over time due to motion, respiration and system instabilities. The dynamically changing field can be measured from the phase of the fMRI data and corrected. However, methods for doing so need multi-echo data, time-consuming reference scans and/or involve error-prone processing steps, such as phase unwrapping, which are difficult to implement robustly on the MRI host. The improved dynamic distortion correction method we propose is based on the phase of the single-echo EPI data acquired for fMRI, phase offsets calculated from a triple-echo, bipolar reference scan of circa 3-10 s duration using a method which avoids the need for phase unwrapping and an additional correction derived from one EPI volume in which the readout direction is reversed. This Reverse-Encoded First Image and Low resoLution reference scan (REFILL) approach is shown to accurately measure B0 as it changes due to shim, motion and respiration, even with large dynamic changes to the field at 7 T, where it led to a > 20% increase in time-series signal to noise ratio compared to data corrected with the classic static approach. fMRI results from REFILL-corrected data were free of stimulus-correlated distortion artefacts seen when data were corrected with static field mapping. The method is insensitive to shim changes and eddy current differences between the reference scan and the fMRI time series, and employs calculation steps that are simple and robust, allowing most data processing to be performed in real time on the scanner image reconstruction computer. These improvements make it feasible to routinely perform dynamic distortion correction in fMRI.

Micron-resolution Imaging of Cortical Bone under 14 T Ultrahigh Magnetic Field

Compact, mineralized cortical bone tissues are often concealed on magnetic resonance (MR) images. Recent development of MR instruments and pulse techniques has yielded significant advances in acquiring anatomical and physiological information from cortical bone despite its poor 1 H signals. This work demonstrates the first MR research on cortical bones under an ultrahigh magnetic field of 14 T. The 1 H signals of different mammalian species exhibit multi-exponential decays of three characteristic T2 or T2 * values: 0.1-0.5 ms, 1-4 ms, and 4-8 ms. Systematic sample comparisons attribute these T2 /T2 * value ranges to collagen-bound water, pore water, and lipids, respectively. Ultrashort echo time (UTE) imaging under 14 T yielded spatial resolutions of 20-80 microns, which resolves the 3D anatomy of the Haversian canals. The T2 * relaxation characteristics further allow spatial classifications of collagen, pore water and lipids in human specimens. The study achieves a record of the spatial resolution for MR imaging in bone and shows that ultrahigh-field MR has the unique ability to differentiate the soft and organic compartments in bone tissues.

Imaging the columnar functional organization of human area MT+ to axis-of-motion stimuli using VASO at 7 Tesla

Cortical columns of direction-selective neurons in the motion sensitive area (MT) have been successfully established as a microscopic feature of the neocortex in animals. The same property has been investigated at mesoscale (<1 mm) in the homologous brain area (hMT+, V5) in living humans by using ultra-high field functional magnetic resonance imaging (fMRI). Despite the reproducibility of the selective response to axis-of-motion stimuli, clear quantitative evidence for the columnar organization of hMT+ is still lacking. Using cerebral blood volume (CBV)-sensitive fMRI at 7 Tesla with submillimeter resolution and high spatial specificity to microvasculature, we investigate the columnar functional organization of hMT+ in 5 participants perceiving axis-of-motion stimuli for both blood oxygenation level dependent (BOLD) and vascular space occupancy (VASO) contrast mechanisms provided by the used slice-selective slab-inversion (SS-SI)-VASO sequence. With the development of a new searchlight algorithm for column detection, we provide the first quantitative columnarity map that characterizes the entire 3D hMT+ volume. Using voxel-wise measures of sensitivity and specificity, we demonstrate the advantage of using CBV-sensitive fMRI to detect mesoscopic cortical features by revealing higher specificity of axis-of-motion cortical columns for VASO as compared to BOLD contrast. These voxel-wise metrics also provide further insights on how to mitigate the highly debated draining veins effect. We conclude that using CBV-VASO fMRI together with voxel-wise measurements of sensitivity, specificity and columnarity offers a promising avenue to quantify the mesoscopic organization of hMT+ with respect to axis-of-motion stimuli. Furthermore, our approach and methodological developments are generalizable and applicable to other human brain areas where similar mesoscopic research questions are addressed.

High-resolution motion- and phase-corrected functional MRI at 7 T using shuttered multishot echo-planar imaging

PURPOSE

To achieve high-resolution multishot echo-planar imaging (EPI) for functional MRI (fMRI) with reduced sensitivity to in-plane motion and between-shot phase variations.

METHODS

Two-dimensional radiofrequency pulses were incorporated in a multishot EPI sequence at 7T which selectively excited a set of in-plane bands (shutters) in the phase encoding direction, which moved between shots to cover the entire slice. A phase- and motion-corrected reconstruction was implemented for the acquisition. Brain imaging experiments were performed with instructed motion to evaluate image quality for conventional multishot and shuttered EPI. Temporal stability was assessed in three subjects by quantifying temporal SNR (tSNR) and artifact levels, and fMRI activation experiments using visual stimulation were performed to assess the strength and distribution of activation, using both conventional multishot and shuttered EPI.

RESULTS

In the instructed motion experiment, ghosting was lower in shuttered EPI images without or with corrections and image quality metrics were improved with motion correction. tSNR was improved by phase correction in both conventional multishot and shuttered EPI and the acquisitions had similar tSNR without and with phase correction. However, while phase correction was necessary to maximize tSNR in conventional multishot EPI, it also increased intermittent ghosting, but did not increase intermittent ghosting in shuttered EPI. Phase correction increased activation strength in both conventional multishot and shuttered EPI, but caused increased spurious activation outside the brain and in frontal brain regions in conventional multishot EPI.

CONCLUSION

Shuttered EPI supports multishot segmented EPI acquisitions with lower sensitivity to artifacts from motion for high-resolution fMRI.

Spatiotemporal Precision of Neuroimaging in Psychiatry

Aberrant patterns of cognition, perception, and behavior seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between spatial and temporal resolutions inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography, often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illnesses such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications that seek to drive a mechanistic understanding of psychopathology and the realization of preclinical translation.

A vision of 14 T MR for fundamental and clinical science

OBJECTIVE

We outline our vision for a 14 Tesla MR system. This comprises a novel whole-body magnet design utilizing high temperature superconductor; a console and associated electronic equipment; an optimized radiofrequency coil setup for proton measurement in the brain, which also has a local shim capability; and a high-performance gradient set.

RESEARCH FIELDS

The 14 Tesla system can be considered a 'mesocope': a device capable of measuring on biologically relevant scales. In neuroscience the increased spatial resolution will anatomically resolve all layers of the cortex, cerebellum, subcortical structures, and inner nuclei. Spectroscopic imaging will simultaneously measure excitatory and inhibitory activity, characterizing the excitation/inhibition balance of neural circuits. In medical research (including brain disorders) we will visualize fine-grained patterns of structural abnormalities and relate these changes to functional and molecular changes. The significantly increased spectral resolution will make it possible to detect (dynamic changes in) individual metabolites associated with pathological pathways including molecular interactions and dynamic disease processes.

CONCLUSIONS

The 14 Tesla system will offer new perspectives in neuroscience and fundamental research. We anticipate that this initiative will usher in a new era of ultra-high-field MR.

Germany's journey toward 14 Tesla human magnetic resonance

Multiple sites within Germany operate human MRI systems with magnetic fields either at 7 Tesla or 9.4 Tesla. In 2013, these sites formed a network to facilitate and harmonize the research being conducted at the different sites and make this technology available to a larger community of researchers and clinicians not only within Germany, but also worldwide. The German Ultrahigh Field Imaging (GUFI) network has defined a strategic goal to establish a 14 Tesla whole-body human MRI system as a national research resource in Germany as the next progression in magnetic field strength. This paper summarizes the history of this initiative, the current status, the motivation for pursuing MR imaging and spectroscopy at such a high magnetic field strength, and the technical and funding challenges involved. It focuses on the scientific and science policy process from the perspective in Germany, and is not intended to be a comprehensive systematic review of the benefits and technical challenges of higher field strengths.

Commissioning of the Iseult CEA 11.7 T whole-body MRI: current status, gradient-magnet interaction tests and first imaging experience

OBJECTIVES

The Iseult MRI is an actively shielded whole-body magnet providing a homogeneous and stable magnetic field of 11.7 T. After nearly 20 years of research and development, the magnet successfully reached its target field strength for the first time in 2019. This article reviews its commissioning status, the gradient-magnet interaction test results and first imaging experience.

MATERIALS AND METHODS

Vibration, acoustics, power deposition in the He bath, and field monitoring measurements were carried out. Magnet safety system was tested against outer magnetic perturbations, and calibrated to define a safe operation of the gradient coil. First measurements using parallel transmission were also performed on an ex-vivo brain to mitigate the RF field inhomogeneity effect.

RESULTS

Acoustics measurements show promising results with sound pressure levels slightly above the enforced limits only at certain frequency intervals. Vibrations of the gradient coil revealed a linear trend with the B₀ field only in the worst case. Field monitoring revealed some resonances at some frequencies that are still under investigation.

DISCUSSION

Gradient-magnet interaction tests at up to 11.7 T are concluded. The scanner is now kept permanently at field and the final calibrations are on-going to pave the road towards the first acquisitions on volunteers.

Cerebral blood volume sensitive layer-fMRI in the human auditory cortex at 7T: Challenges and capabilities

The development of ultra high field fMRI signal readout strategies and contrasts has led to the possibility of imaging the human brain in vivo and non-invasively at increasingly higher spatial resolutions of cortical layers and columns. One emergent layer-fMRI acquisition method with increasing popularity is the cerebral blood volume sensitive sequence named vascular space occupancy (VASO). This approach has been shown to be mostly sensitive to locally-specific changes of laminar microvasculature, without unwanted biases of trans-laminar draining veins. Until now, however, VASO has not been applied in the technically challenging cortical area of the auditory cortex. Here, we describe the main challenges we encountered when developing a VASO protocol for auditory neuroscientific applications and the solutions we have adopted. With the resulting protocol, we present preliminary results of laminar responses to sounds and as a proof of concept for future investigations, we map the topographic representation of frequency preference (tonotopy) in the auditory cortex.

Improved laminar specificity and sensitivity by combining SE and GE BOLD signals

The most widely used gradient-echo (GE) blood oxygenation level-dependent (BOLD) contrast has high sensitivity, but low specificity due to draining vein contributions, while spin-echo (SE) BOLD approach at ultra-high magnetic fields is highly specific to neural active sites but has lower sensitivity. To obtain high specificity and sensitivity, we propose to utilize a vessel-size-sensitive filter to the GE-BOLD signal, which suppresses macrovascular contributions and to combine selectively retained microvascular GE-BOLD signals with the SE-BOLD signals. To investigate our proposed idea, fMRI with 0.8 mm isotropic resolution was performed on the primary motor and sensory cortices in humans at 7 T by implementing spin- and gradient-echo (SAGE) echo planar imaging (EPI) acquisition. Microvascular-passed sigmoidal filters were designed based upon the vessel-size-sensitive ΔR₂*/ΔR₂ value for retaining GE-BOLD signals originating from venous vessels with ≤ 45 μm and ≤ 65 μm diameter. Unlike GE-BOLD fMRI, the laminar profile of SAGE-BOLD fMRI with the vessel-size-sensitive filter peaked at ∼ 1.0 mm from the surface of the primary motor and sensory cortices, demonstrating an improvement of laminar specificity over GE-BOLD fMRI. Also, the functional sensitivity of SAGE BOLD at middle layers (0.75-1.5 mm) was improved by ∼ 80% to ∼100% when compared with SE BOLD. In summary, we showed that combined GE- and SE-BOLD fMRI with the vessel-size-sensitive filter indeed yielded improved laminar specificity and sensitivity and is therefore an excellent tool for high spatial resolution ultra-high filed (UHF)-fMRI studies for resolving mesoscopic functional units.

Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T

The model-free algorithms of "reinforcement learning" (RL) have gained clout across disciplines, but so too have model-based alternatives. The present study emphasizes other dimensions of this model space in consideration of associative or discriminative generalization across states and actions. This "generalized reinforcement learning" (GRL) model, a frugal extension of RL, parsimoniously retains the single reward-prediction error (RPE), but the scope of learning goes beyond the experienced state and action. Instead, the generalized RPE is efficiently relayed for bidirectional counterfactual updating of value estimates for other representations. Aided by structural information but as an implicit rather than explicit cognitive map, GRL provided the most precise account of human behavior and individual differences in a reversal-learning task with hierarchical structure that encouraged inverse generalization across both states and actions. Reflecting inference that could be true, false (i.e., overgeneralization), or absent (i.e., undergeneralization), state generalization distinguished those who learned well more so than action generalization. With high-resolution high-field fMRI targeting the dopaminergic midbrain, the GRL model's RPE signals (alongside value and decision signals) were localized within not only the striatum but also the substantia nigra and the ventral tegmental area, including specific effects of generalization that also extend to the hippocampus. Factoring in generalization as a multidimensional process in value-based learning, these findings shed light on complexities that, while challenging classic RL, can still be resolved within the bounds of its core computations.

Characterization of the blood oxygen level dependent hemodynamic response function in human subcortical regions with high spatiotemporal resolution

Subcortical brain regions are absolutely essential for normal human function. These phylogenetically early brain regions play critical roles in human behaviors such as the orientation of attention, arousal, and the modulation of sensory signals to cerebral cortex. Despite the critical health importance of subcortical brain regions, there has been a dearth of research on their neurovascular responses. Blood oxygen level dependent (BOLD) functional MRI (fMRI) experiments can help fill this gap in our understanding. The BOLD hemodynamic response function (HRF) evoked by brief (<4 s) neural activation is crucial for the interpretation of fMRI results because linear analysis between neural activity and the BOLD response relies on the HRF. Moreover, the HRF is a consequence of underlying local blood flow and oxygen metabolism, so characterization of the HRF enables understanding of neurovascular and neurometabolic coupling. We measured the subcortical HRF at 9.4T and 3T with high spatiotemporal resolution using protocols that enabled reliable delineation of HRFs in individual subjects. These results were compared with the HRF in visual cortex. The HRF was faster in subcortical regions than cortical regions at both field strengths. There was no significant undershoot in subcortical areas while there was a significant post-stimulus undershoot that was tightly coupled with its peak amplitude in cortex. The different BOLD temporal dynamics indicate different vascular dynamics and neurometabolic responses between cortex and subcortical nuclei.

The influence of the subcortex and brain stem on overeating: How advances in functional neuroimaging can be applied to expand neurobiological models to beyond the cortex

Functional neuroimaging has become a widely used tool in obesity and eating disorder research to explore the alterations in neurobiology that underlie overeating and binge eating behaviors. Current and traditional neurobiological models underscore the importance of impairments in brain systems supporting reward, cognitive control, attention, and emotion regulation as primary drivers for overeating. Due to the technical limitations of standard field strength functional magnetic resonance imaging (fMRI) scanners, human neuroimaging research to date has focused largely on cortical and basal ganglia effects on appetitive behaviors. The present review draws on animal and human research to highlight how neural signaling encoding energy regulation, reward-learning, and habit formation converge on hypothalamic, brainstem, thalamic, and striatal regions to contribute to overeating in humans. We also consider the role of regions such as the mediodorsal thalamus, ventral striatum, lateral hypothalamus and locus coeruleus in supporting habit formation, inhibitory control of food craving, and attentional biases. Through these discussions, we present proposals on how the neurobiology underlying these processes could be examined using functional neuroimaging and highlight how ultra-high field 7-Tesla (7 T) fMRI may be leveraged to elucidate the potential functional alterations in subcortical networks. Focus is given to how interactions of these regions with peripheral endocannabinoids and neuropeptides, such as orexin, could be explored. Technical and methodological aspects regarding the use of ultra-high field 7 T fMRI to study eating behaviors are also reviewed.

Magnetic field strength dependent SNR gain at the center of a spherical phantom and up to 11.7T

PURPOSE

The SNR at the center of a spherical phantom of known electrical properties was measured in quasi-identical experimental conditions as a function of magnetic field strength between 3 T and 11.7 T.

METHODS

The SNR was measured at the center of a spherical water saline phantom with a gradient recalled echo sequence. Measurements were performed at NeuroSpin at 3, 7, and 11.7 T. The phantom was then shipped to Maastricht University and then to the University of Minnesota for additional data points at 7, 9.4, and 10.5 T. Experiments were carried out with the exact same type of birdcage volume coil (except at 3 T, where a similar coil was used) to attempt at isolating the evolution of SNR with field strength alone. Phantom electrical properties were characterized over the corresponding frequency range.

RESULTS

Electrical properties were found to barely vary over the frequency range. Removing the influence of the flip-angle excitation inhomogeneity was crucial, as expected. After such correction, measurements revealed a gain of SNR growing as B₀ ^{1.94 ± 0.16} compared with B₀ ^2.13 according to ultimate intrinsic SNR theory.

CONCLUSIONS

By using quasi-identical experimental setups (RF volume coil, phantom, electrical properties, and protocol), this work reports experimental data between 3 T and 11.7 T, enabling the comparison with SNR theories in which conductivity and permittivity can be assumed to be constant with respect to field strength. According to ultimate SNR theory, these results can be reasonably extrapolated to the performance of receive arrays with greater than about 32 elements for central SNR in the same spherical phantom.

Towards a cross-level understanding of Bayesian inference in the brain

Perception emerges from unconscious probabilistic inference, which guides behaviour in our ubiquitously uncertain environment. Bayesian decision theory is a prominent computational model that describes how people make rational decisions using noisy and ambiguous sensory observations. However, critical questions have been raised about the validity of the Bayesian framework in explaining the mental process of inference. Firstly, some natural behaviours deviate from Bayesian optimum. Secondly, the neural mechanisms that support Bayesian computations in the brain are yet to be understood. Taking Marr's cross level approach, we review the recent progress made in addressing these challenges. We first review studies that combined behavioural paradigms and modelling approaches to explain both optimal and suboptimal behaviours. Next, we evaluate the theoretical advances and the current evidence for ecologically feasible algorithms and neural implementations in the brain, which may enable probabilistic inference. We argue that this cross-level approach is necessary for the worthwhile pursuit to uncover mechanistic accounts of human behaviour.

Simultaneous pure T2 and varying T2'-weighted BOLD fMRI using Echo Planar Time-resolved Imaging for mapping cortical-depth dependent responses

Spin-echo (SE) BOLD fMRI has high microvascular specificity, and thus provides a more reliable means to localize neural activity compared to conventional gradient-echo BOLD fMRI. However, the most common SE BOLD acquisition method, SE-EPI, is known to suffer from T2' contrast contamination with undesirable draining vein bias. To address this, in this study, we extended a recently developed distortion/blurring-free multi-shot EPI technique, Echo-Planar Time-resolved Imaging (EPTI), to cortical-depth dependent SE-fMRI at 7T to test whether it could provide purer SE BOLD contrast with minimal T2' contamination for improved neuronal specificity. From the same acquisition, the time-resolved feature of EPTI also provides a series of asymmetric SE (ASE) images with varying T2' weightings, and enables extraction of data equivalent to conventional SE EPI with different echo train lengths (ETLs). This allows us to systematically examine how T2'-contribution affects different SE acquisition strategies using a single dataset. A low-rank spatiotemporal subspace reconstruction was implemented for the SE-EPTI acquisition, which incorporates corrections for both shot-to-shot phase variations and dynamic B0 drifts. SE-EPTI was used in a visual task fMRI experiment to demonstrate that i) the pure SE image provided by EPTI results in the highest microvascular specificity; ii) the ASE EPTI series, with a graded introduction of T2' weightings at time points farther away from the pure SE, show a gradual sensitivity increase along with increasing draining vein bias; iii) the longer ETL seen in conventional SE EPI acquisitions will induce more draining vein bias. Consistent results were observed across multiple subjects, demonstrating the robustness of the proposed technique for SE-BOLD fMRI with high specificity.

Predicting neuronal response properties from hemodynamic responses in the auditory cortex

Recent functional MRI (fMRI) studies have highlighted differences in responses to natural sounds along the rostral-caudal axis of the human superior temporal gyrus. However, due to the indirect nature of the fMRI signal, it has been challenging to relate these fMRI observations to actual neuronal response properties. To bridge this gap, we present a forward model of the fMRI responses to natural sounds combining a neuronal model of the auditory cortex with physiological modeling of the hemodynamic BOLD response. Neuronal responses are modeled with a dynamic recurrent firing rate model, reflecting the tonotopic, hierarchical processing in the auditory cortex along with the spectro-temporal tradeoff in the rostral-caudal axis of its belt areas. To link modeled neuronal response properties with human fMRI data in the auditory belt regions, we generated a space of neuronal models, which differed parametrically in spectral and temporal specificity of neuronal responses. Then, we obtained predictions of fMRI responses through a biophysical model of the hemodynamic BOLD response (P-DCM). Using Bayesian model comparison, our results showed that the hemodynamic BOLD responses of the caudal belt regions in the human auditory cortex were best explained by modeling faster temporal dynamics and broader spectral tuning of neuronal populations, while rostral belt regions were best explained through fine spectral tuning combined with slower temporal dynamics. These results support the hypotheses of complementary neural information processing along the rostral-caudal axis of the human superior temporal gyrus.

Using high spatial resolution fMRI to understand representation in the auditory network

Following rapid methodological advances, ultra-high field (UHF) functional and anatomical magnetic resonance imaging (MRI) has been repeatedly and successfully used for the investigation of the human auditory system in recent years. Here, we review this work and argue that UHF MRI is uniquely suited to shed light on how sounds are represented throughout the network of auditory brain regions. That is, the provided gain in spatial resolution at UHF can be used to study the functional role of the small subcortical auditory processing stages and details of cortical processing. Further, by combining high spatial resolution with the versatility of MRI contrasts, UHF MRI has the potential to localize the primary auditory cortex in individual hemispheres. This is a prerequisite to study how sound representation in higher-level auditory cortex evolves from that in early (primary) auditory cortex. Finally, the access to independent signals across auditory cortical depths, as afforded by UHF, may reveal the computations that underlie the emergence of an abstract, categorical sound representation based on low-level acoustic feature processing. Efforts on these research topics are underway. Here we discuss promises as well as challenges that come with studying these research questions using UHF MRI, and provide a future outlook.

A nine-channel transmit/receive array for spine imaging at 10.5 T: Introduction to a nonuniform dielectric substrate antenna

PURPOSE

The purpose of this study is to introduce a new antenna element with improved transmit performance, named the nonuniform dielectric substrate (NODES) antenna, for building transmit arrays at ultrahigh-field.

METHODS

We optimized a dipole antenna at 10.5 Tesla by maximizing the B 1 + -SAR efficiency in a phantom for a human spine target. The optimization parameters included permittivity variation in the substrate, substrate thickness, antenna length, and conductor geometry. We conducted electromagnetic simulations as well as phantom experiments to compare the transmit/receive performance of the proposed NODES antenna design with existing coil elements from the literature.

RESULTS

Single NODES element showed up to 18% and 30% higher B 1 + -SAR efficiency than the fractionated dipole and loop elements, respectively. The new element is substantially shorter than a commonly used dipole, which enables z-stacked array formation; it is additionally capable of providing a relatively uniform current distribution along its conductors. The nine-channel transmit/receive NODES array achieved 7.5% higher B 1 + homogeneity than a loop array with the same number of elements. Excitation with the NODES array resulted in 33% lower peak 10g-averaged SAR and required 34% lower input power than the loop array for the target anatomy of the spine.

CONCLUSION

In this study, we introduced a new RF coil element: the NODES antenna. NODES antenna outperformed the widely used loop and dipole elements and may provide improved transmit/receive performance for future ultrahigh field MRI applications.

Imaging human engrams using 7 Tesla magnetic resonance imaging

The investigation of the physical traces of memories (engrams) has made significant progress in the last decade due to optogenetics and fluorescent cell tagging applied in rodents. Engram cells were identified. The ablation of engram cells led to the loss of the associated memory, silent memories were reactivated, and artificial memories were implanted in the brain. Human engram research lags behind engram research in rodents due to methodological and ethical constraints. However, advances in multivariate analysis techniques of functional magnetic resonance imaging (fMRI) data and machine learning algorithms allowed the identification of stable engram patterns in humans. In addition, MRI scanners with an ultrahigh field strength of 7 Tesla (T) have left their prototype state and became more common around the world to assist human engram research. Although most engram research in humans is still being performed with a field strength of 3T, fMRI at 7T will push engram research. Here, we summarize the current state and findings of human engram research and discuss the advantages and disadvantages of applying 7 versus 3T fMRI to image human memory traces.

Laminar processing of numerosity supports a canonical cortical microcircuit in human parietal cortex

As neural signals travel through the visual hierarchy, spatial precision decreases and specificity for stimulus features increases.^1-4 A similar hierarchy has been found for laminar processing in V1, where information from the thalamus predominantly targets the central layers, while spatial precision decreases and feature specificity increases toward superficial and deeper layers.^5-17 This laminar processing scheme is proposed to represent a canonical cortical microcircuit that is similar across the cortex.^11,18-21 Here, we go beyond early visual cortex and investigate whether processing of numerosity (the set size of a group of items) across cortical depth in the parietal association cortex follows this hypothesis. Numerosity processing is implicated in many tasks such as multiple object tracking,²² mathematics,^23-25 decision making,²⁶ and dividing attention.²⁷ Neurons in the parietal association cortex are tuned to numerosity, with both a preferred numerosity tuning and tuning width (i.e., specificity).^28-30 We quantified preferred numerosity responses across cortical depth in the parietal association cortex with ultra-high field fMRI and population receptive field-based numerosity modeling.^1,28,31 We find that numerosity responses sharpen, i.e., become increasingly specific, moving away from the central layers. This suggests that the laminar processing scheme for numerosity processing in the parietal cortex is similar to primary visual cortex, providing support for the canonical cortical microcircuit hypothesis beyond primary visual cortex.

Progress in Imaging the Human Torso at the Ultrahigh Fields of 7 and 10.5 T

Especially after the launch of 7 T, the ultrahigh magnetic field (UHF) imaging community achieved critically important strides in our understanding of the physics of radiofrequency interactions in the human body, which in turn has led to solutions for the challenges posed by such UHFs. As a result, the originally obtained poor image quality has progressed to the high-quality and high-resolution images obtained at 7 T and now at 10.5 T in the human torso. Despite these tremendous advances, work still remains to further improve the image quality and fully capitalize on the potential advantages UHF has to offer.

Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI

Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.

Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) has become an indispensable tool for investigating the human brain. However, the inherently poor signal-to-noise-ratio (SNR) of the fMRI measurement represents a major barrier to expanding its spatiotemporal scale as well as its utility and ultimate impact. Here we introduce a denoising technique that selectively suppresses the thermal noise contribution to the fMRI experiment. Using 7-Tesla, high-resolution human brain data, we demonstrate improvements in key metrics of functional mapping (temporal-SNR, the detection and reproducibility of stimulus-induced signal changes, and accuracy of functional maps) while leaving the amplitude of the stimulus-induced signal changes, spatial precision, and functional point-spread-function unaltered. We demonstrate that the method enables the acquisition of ultrahigh resolution (0.5 mm isotropic) functional maps but is also equally beneficial for a large variety of fMRI applications, including supra-millimeter resolution 3- and 7-Tesla data obtained over different cortical regions with different stimulation/task paradigms and acquisition strategies.

The signal-to-noise ratio is a key consideration when selecting a magnetic resonance imaging protocol. Thermal noise is major issue, especially in high resolution functional images. Here the authors introduce a method to suppress thermal noise in functional images without losses in spatial precision, increasing the signal-to-noise ratio.

Population Models, Not Analyses, of Human Neuroscience Measurements

Selectivity for many basic properties of visual stimuli, such as orientation, is thought to be organized at the scale of cortical columns, making it difficult or impossible to measure directly with noninvasive human neuroscience measurement. However, computational analyses of neuroimaging data have shown that selectivity for orientation can be recovered by considering the pattern of response across a region of cortex. This suggests that computational analyses can reveal representation encoded at a finer spatial scale than is implied by the spatial resolution limits of measurement techniques. This potentially opens up the possibility to study a much wider range of neural phenomena that are otherwise inaccessible through noninvasive measurement. However, as we review in this article, a large body of evidence suggests an alternative hypothesis to this superresolution account: that orientation information is available at the spatial scale of cortical maps and thus easily measurable at the spatial resolution of standard techniques. In fact, a population model shows that this orientation information need not even come from single-unit selectivity for orientation tuning, but instead can result from population selectivity for spatial frequency. Thus, a categorical error of interpretation can result whereby orientation selectivity can be confused with spatial frequency selectivity. This is similarly problematic for the interpretation of results from numerous studies of more complex representations and cognitive functions that have built upon the computational techniques used to reveal stimulus orientation. We suggest in this review that these interpretational ambiguities can be avoided by treating computational analyses as models of the neural processes that give rise to measurement. Building upon the modeling tradition in vision science using considerations of whether population models meet a set of core criteria is important for creating the foundation for a cumulative and replicable approach to making valid inferences from human neuroscience measurements. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Displacement current distribution on a high dielectric constant helmet and its effect on RF field at 10.5 T (447 MHz)

PURPOSE

Investigating the designs and effects of high dielectric constant (HDC) materials in the shape of a conformal helmet on the enhancement of RF field and reduction of specific absorption rate at 10.5 T for human brain studies.

METHODS

A continuous and a segmented four-piece HDC helmet fit to a human head inside an eight-channel fractionated-dipole array were constructed and studied with a phantom and a human head model using computer electromagnetic simulations. The simulated transmit efficiency and receive sensitivity were experimentally validated using a phantom with identical electric properties and helmet-coil configurations of the computer model. The temporal and spatial distributions of displacement currents on the HDC helmets were analyzed.

RESULTS

Using the continuous HDC helmet, simulation results in the human head model demonstrated an average transmit efficiency enhancement of 66%. A propagating displacement current was induced on the continuous helmet, leading to an inhomogeneous RF field enhancement in the brain. Using the segmented four-piece helmet design to reduce this effect, an average 55% and 57% enhancement in the transmit efficiency and SNR was achieved in human head, respectively, along with 8% and 28% reductions in average and maximum local specific absorption rate.

CONCLUSION

The HDC helmets enhanced the transmit efficiency and SNR of the dipole array coil in the human head at 10.5 T. The segmentation of the helmet to disrupt the continuity of circumscribing displacement currents in the helmet produced a more uniform distribution of the transmit field and lower specific absorption rate in the human head compared with the continuous helmet design.

ULTRAHIGH FIELD and ULTRAHIGH RESOLUTION fMRI

Functional magnetic resonance imaging (fMRI) has become one of the most powerful tools for investigating the human brain. Ultrahigh magnetic field (UHF) of 7 Tesla has played a critical role in enabling higher resolution and more accurate (relative to the neuronal activity) functional maps. However, even with these gains, the fMRI approach is challenged relative to the spatial scale over which brain function is organized. Therefore, going forward, significant advances in fMRI are still needed. Such advances will predominantly come from magnetic fields significantly higher than 7 Tesla, which is the most commonly used UHF platform today, and additional technologies that will include developments in pulse sequences, image reconstruction, noise suppression, and image analysis in order to further enhance and augment the gains than can be realized by going to higher magnetic fields.

Individualized cognitive neuroscience needs 7T: Comparing numerosity maps at 3T and 7T MRI

The field of cognitive neuroscience is weighing evidence about whether to move from the current standard field strength of 3 Tesla (3T) to ultra-high field (UHF) of 7T and above. The present study contributes to the evidence by comparing a computational cognitive neuroscience paradigm at 3T and 7T. The goal was to evaluate the practical effects, i.e. model predictive power, of field strength on a numerosity task using accessible pre-processing and analysis tools. Previously, using 7T functional magnetic resonance imaging and biologically-inspired analyses, i.e. population receptive field modelling, we discovered topographical organization of numerosity-selective neural populations in human parietal cortex. Here we show that these topographic maps are also detectable at 3T. However, averaging of many more functional runs was required at 3T to reliably reconstruct numerosity maps. On average, one 7T run had about four times the model predictive power of one 3T run. We believe that this amount of scanning would have made the initial discovery of the numerosity maps on 3T highly infeasible in practice. Therefore, we suggest that the higher signal-to-noise ratio and signal sensitivity of UHF MRI is necessary to build mechanistic models of the organization and function of our cognitive abilities in individual participants.

Cortical and subcortical signatures of conscious object recognition

The neural mechanisms underlying conscious recognition remain unclear, particularly the roles played by the prefrontal cortex, deactivated brain areas and subcortical regions. We investigated neural activity during conscious object recognition using 7 Tesla fMRI while human participants viewed object images presented at liminal contrasts. Here, we show both recognized and unrecognized images recruit widely distributed cortical and subcortical regions; however, recognized images elicit enhanced activation of visual, frontoparietal, and subcortical networks and stronger deactivation of the default-mode network. For recognized images, object category information can be decoded from all of the involved cortical networks but not from subcortical regions. Phase-scrambled images trigger strong involvement of inferior frontal junction, anterior cingulate cortex and default-mode network, implicating these regions in inferential processing under increased uncertainty. Our results indicate that content-specific activity in both activated and deactivated cortical networks and non-content-specific subcortical activity support conscious recognition.

Cortical and subcortical neural activity supporting conscious object recognition has not yet been well defined. Here, the authors describe these networks and show recognition-related category information can be decoded from widespread cortical activity but not subcortical activity.

Determining laminar neuronal activity from BOLD fMRI using a generative model

Laminar fMRI using the BOLD contrast enables the non-invasive investigation of mesoscopic functional circuits in the human brain. However, the laminar neuronal activity is spatiotemporally biased in the observed cortical depth profiles of the BOLD signal. In this study, we propose a generative fMRI signal model, comprehensively covering the relationship between cortical depth-dependent changes in excitatory and inhibitory neuronal activity with the sampling of the BOLD signal with finite voxels. The generative model allowed us to investigate pertinent questions regarding the accuracy of the laminar BOLD signal relative to the neuronal activity, and we found that: a) condition differences in laminar BOLD signals may be more reflective of neuronal activity than single condition BOLD signal depth profiles; b) angular dependence of the BOLD signal induces significant signal variability, which can mask underlying activity profiles; c) even if only three neuronal depths are of interest, more BOLD signal depths should be considered in the analysis. In addition, we recommend that the laminar BOLD data should be displayed using the centroid method to appreciate its spatial distribution in the original resolution. Finally, we showed that Bayesian model inversion of the generative model can improve sensitivity and specificity of assessing depth-dependent neuronal changes both for steady-state and dynamically.

Long-term behavioral effects observed in mice chronically exposed to static ultra-high magnetic fields

PURPOSE

The primary goal of this study was to investigate whether chronic exposures to ultra-high B₀ fields can induce long-term cognitive, behavioral, or biological changes in C57BL/6 mice.

METHODS

C57BL/6 mice were chronically exposed to 10.5-T or 16.4-T magnetic fields (3-h exposures, two exposure sessions per week, 4 or 8 weeks of exposure). In vivo single-voxel ¹ H magnetic resonance spectroscopy was used to investigate possible neurochemical changes in the hippocampus. In addition, a battery of behavioral tests, including the Morris water-maze, balance-beam, rotarod, and fear-conditioning tests, were used to examine long-term changes induced by B₀ exposures.

RESULTS

Hippocampal neurochemical profile, cognitive, and basic motor functions were not impaired by chronic magnetic field exposures. However, the balance-beam-walking test and the Morris water-maze testing revealed B₀ -induced changes in motor coordination and balance. The tight-circling locomotor behavior during Morris water-maze tests was found as the most sensitive factor indexing B₀ -induced changes. Long-term behavioral changes were observed days or even weeks subsequent to the last B₀ exposure at 16.4 T but not at 10.5 T. Fast motion of mice in and out of the 16.4-T magnet was not sufficient to induce such changes.

CONCLUSION

Observed results suggest that the chronic exposure to a magnetic field as high as 16.4 T may result in long-term impairment of the vestibular system in mice. Although observation of mice may not directly translate to humans, nevertheless, they indicate that studies focused on human safety at very high magnetic fields are necessary.

A self-decoupled 32-channel receive array for human-brain MRI at 10.5 T

PURPOSE

Receive array layout, noise mitigation, and B₀ field strength are crucial contributors to SNR and parallel-imaging performance. Here, we investigate SNR and parallel-imaging gains at 10.5 T compared with 7 T using 32-channel receive arrays at both fields.

METHODS

A self-decoupled 32-channel receive array for human brain imaging at 10.5 T (10.5T-32Rx), consisting of 31 loops and one cloverleaf element, was co-designed and built in tandem with a 16-channel dual-row loop transmitter. Novel receive array design and self-decoupling techniques were implemented. Parallel imaging performance, in terms of SNR and noise amplification (g-factor), of the 10.5T-32Rx was compared with the performance of an industry-standard 32-channel receiver at 7 T (7T-32Rx) through experimental phantom measurements.

RESULTS

Compared with the 7T-32Rx, the 10.5T-32Rx provided 1.46 times the central SNR and 2.08 times the peripheral SNR. Minimum inverse g-factor value of the 10.5T-32Rx (min[1/g] = 0.56) was 51% higher than that of the 7T-32Rx (min[1/g] = 0.37) with R = 4 × 4 2D acceleration, resulting in significantly enhanced parallel-imaging performance at 10.5 T compared with 7 T. The g-factor values of 10.5 T-32 Rx were on par with those of a 64-channel receiver at 7 T (eg, 1.8 vs 1.9, respectively, with R = 4 × 4 axial acceleration).

CONCLUSION

Experimental measurements demonstrated effective self-decoupling of the receive array as well as substantial gains in SNR and parallel-imaging performance at 10.5 T compared with 7 T.

High-resolution fMRI at 7 Tesla: challenges, promises and recent developments for individual-focused fMRI studies

Limited detection power has been a bottleneck for subject-specific functional MRI (fMRI) studies, however the higher signal-to-noise ratio afforded by ultra-high magnetic fields (≥ 7 Tesla) provides levels of sensitivity and resolution needed to study individual subjects. What may be surprising is that higher imaging resolution may provide both higher specificity and sensitivity due to reductions in partial volume effects and reduced physiological noise. However, challenges remain to ensure high data quality and to reduce variability in ultra-high field fMRI. We discuss session-specific biases including those caused by factors related to instrumentation, anatomy, and physiology-which can translate into variability across sessions-and how to minimize these to help ultra-high field fMRI reach its full potential for individual-focused studies.

Validating Linear Systems Analysis for Laminar fMRI: Temporal Additivity for Stimulus Duration Manipulations

Advancements in ultra-high field (7 T and higher) magnetic resonance imaging (MRI) scanners have made it possible to investigate both the structure and function of the human brain at a sub-millimeter scale. As neuronal feedforward and feedback information arrives in different layers, sub-millimeter functional MRI has the potential to uncover information processing between cortical micro-circuits across cortical depth, i.e. laminar fMRI. For nearly all conventional fMRI analyses, the main assumption is that the relationship between local neuronal activity and the blood oxygenation level dependent (BOLD) signal adheres to the principles of linear systems theory. For laminar fMRI, however, directional blood pooling across cortical depth stemming from the anatomy of the cortical vasculature, potentially violates these linear system assumptions, thereby complicating analysis and interpretation. Here we assess whether the temporal additivity requirement of linear systems theory holds for laminar fMRI. We measured responses elicited by viewing stimuli presented for different durations and evaluated how well the responses to shorter durations predicted those elicited by longer durations. We find that BOLD response predictions are consistently good predictors for observed responses, across all cortical depths, and in all measured visual field maps (V1, V2, and V3). Our results suggest that the temporal additivity assumption for linear systems theory holds for laminar fMRI. We thus show that the temporal additivity assumption holds across cortical depth for sub-millimeter gradient-echo BOLD fMRI in early visual cortex.

Ultra-high field fMRI reveals origins of feedforward and feedback activity within laminae of human ocular dominance columns

Ultra-high field MRI can functionally image the cerebral cortex of human subjects at the submillimeter scale of cortical columns and laminae. Here, we investigate both in concert, by imaging ocular dominance columns (ODCs) in primary visual cortex (V1) across different cortical depths. We ensured that putative ODC patterns in V1 (a) are stable across runs, sessions, and scanners located in different continents, (b) have a width (~1.3 mm) expected from post-mortem and animal work and (c) are absent at the retinotopic location of the blind spot. We then dissociated the effects of bottom-up thalamo-cortical input and attentional feedback processes on activity in V1 across cortical depth. Importantly, the separation of bottom-up information flows into ODCs allowed us to validly compare attentional conditions while keeping the stimulus identical throughout the experiment. We find that, when correcting for draining vein effects and using both model-based and model-free approaches, the effect of monocular stimulation is largest at deep and middle cortical depths. Conversely, spatial attention influences BOLD activity exclusively near the pial surface. Our findings show that simultaneous interrogation of columnar and laminar dimensions of the cortical fold can dissociate thalamocortical inputs from top-down processing, and allow the investigation of their interactions without any stimulus manipulation.

Ultra-high field (10.5 T) resting state fMRI in the macaque

Resting state functional connectivity refers to the temporal correlations between spontaneous hemodynamic signals obtained using functional magnetic resonance imaging. This technique has demonstrated that the structure and dynamics of identifiable networks are altered in psychiatric and neurological disease states. Thus, resting state network organizations can be used as a diagnostic, or prognostic recovery indicator. However, much about the physiological basis of this technique is unknown. Thus, providing a translational bridge to an optimal animal model, the macaque, in which invasive circuit manipulations are possible, is of utmost importance. Current approaches to resting state measurements in macaques face unique challenges associated with signal-to-noise, the need for contrast agents limiting translatability, and within-subject designs. These limitations can, in principle, be overcome through ultra-high magnetic fields. However, imaging at magnetic fields above 7T has yet to be adapted for fMRI in macaques. Here, we demonstrate that the combination of high channel count transmitter and receiver arrays, optimized pulse sequences, and careful anesthesia regimens, allows for detailed single-subject resting state analysis at high resolutions using a 10.5 Tesla scanner. In this study, we uncover thirty spatially detailed resting state components that are highly robust across individual macaques and closely resemble the quality and findings of connectomes from large human datasets. This detailed map of the rsfMRI 'macaque connectome' will be the basis for future neurobiological circuit manipulation work, providing valuable biological insights into human connectomics.

Forging a path to mesoscopic imaging success with ultra-high field functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) studies with ultra-high field (UHF, 7+ Tesla) technology enable the acquisition of high-resolution images. In this work, we discuss recent achievements in UHF fMRI at the mesoscopic scale, on the order of cortical columns and layers, and examine approaches to addressing common challenges. As researchers push to smaller and smaller voxel sizes, acquisition and analysis decisions have greater potential to degrade spatial accuracy, and UHF fMRI data must be carefully interpreted. We consider the impact of acquisition decisions on the spatial specificity of the MR signal with a representative dataset with 0.8 mm isotropic resolution. We illustrate the trade-offs in contrast with noise ratio and spatial specificity of different acquisition techniques and show that acquisition blurring can increase the effective voxel size by as much as 50% in some dimensions. We further describe how different sources of degradations to spatial resolution in functional data may be characterized. Finally, we emphasize that progress in UHF fMRI depends not only on scientific discovery and technical advancement, but also on informal discussions and documentation of challenges researchers face and overcome in pursuit of their goals. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

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.