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Following early efforts in applying nuclear magnetic resonance (NMR) spectroscopy to study biological processes in intact systems, and particularly since the introduction of 4 T human scanners circa 1990, rapid progress was made in imaging and spectroscopy studies of humans at 4 T and animal models at 9.4 T, leading to the introduction of 7 T and higher magnetic fields for human investigation at about the turn of the century. Work conducted on these platforms has provided numerous technological solutions to challenges posed at these ultrahigh fields, and demonstrated the existence of significant advantages in signal-to-noise ratio and biological information content. Primary difference from lower fields is the deviation from the near field regime at the radiofrequencies (RF) corresponding to hydrogen resonance conditions. At such ultrahigh fields, the RF is characterized by attenuated traveling waves in the human body, which leads to image non-uniformities for a given sample-coil configuration because of destructive and constructive interferences. These non-uniformities were initially considered detrimental to progress of imaging at high field strengths. However, they are advantageous for parallel imaging in signal reception and transmission, two critical technologies that account, to a large extend, for the success of ultrahigh fields. With these technologies and improvements in instrumentation and imaging methods, today ultrahigh fields have provided unprecedented gains in imaging of brain function and anatomy, and started to make inroads into investigation of the human torso and extremities. As extensive as they are, these gains still constitute a prelude to what is to come given the increasingly larger effort committed to ultrahigh field research and development of ever better instrumentation and techniques. •Development of ultrahigh magnetic field MRI for human imaging.•Functional brain imaging (fMRI) at ultrahigh magnetic fields; cortical columns and layers.•Solutions to Ultrahigh field challenges: Power deposition (SAR), transmit RF and static magnetic field inhomogeneities.•Multichannel RF (B1) transmit and transmit pulses.•Highly accelerated imaging for high resolution whole brain coverage; Human Brain Connectome at ultrahigh fields.

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.

The spatial resolution of functional magnetic resonance imaging (fMRI) is fundamentally limited by effects from large draining veins. Here we describe an analysis method that provides data-driven estimates of these effects in task-based fMRI. The method involves fitting a one-dimensional manifold that characterizes variation in response timecourses observed in a given dataset, and then using identified early and late timecourses as basis functions for decomposing responses into components related to the microvasculature (capillaries and small venules) and the macrovasculature (large veins), respectively. We show the removal of late components substantially reduces the superficial cortical depth bias of fMRI responses and helps eliminate artifacts in cortical activity maps. This method provides insight into the origins of the fMRI signal and can be used to improve the spatial accuracy of fMRI. Temporal decomposition through manifold fitting (TDM) is an analysis technique that decomposes blood oxygenation level dependent (BOLD) responses in task-based fMRI into different components that likely correspond to microvasculature- and macrovasculature-driven signals.

•We propose a framework, NORDIC, for denoising complex valued dMRI data using Gaussian statistics.•The main feature of the proposed method is to only remove signal components which cannot be distinguished from thermal noise.•Quantitative evaluation of NORDIC is performed across different resolutions and SNR using human Connectome type acquisitions.•The proposed method outperforms a state-of-art methods for denoising dMRI in terms of fiber orientation dispersion.•Up to 6 fold improvement in apparent SNR for 0.9mm whole brain dMRI at 3T. Diffusion-weighted magnetic resonance imaging (dMRI) has found great utility for a wide range of neuroscientific and clinical applications. However, high-resolution dMRI, which is required for improved delineation of fine brain structures and connectomics, is hampered by its low signal-to-noise ratio (SNR). Since dMRI relies on the acquisition of multiple different diffusion weighted images of the same anatomy, it is well-suited for denoising methods that utilize correlations across the image series to improve the apparent SNR and the subsequent data analysis. In this work, we introduce and quantitatively evaluate a comprehensive framework, NOise Reduction with DIstribution Corrected (NORDIC) PCA method for processing dMRI. NORDIC uses low-rank modeling of g-factor-corrected complex dMRI reconstruction and non-asymptotic random matrix distributions to remove signal components which cannot be distinguished from thermal noise. The utility of the proposed framework for denoising dMRI is demonstrated on both simulations and experimental data obtained at 3 Tesla with different resolutions using human connectome project style acquisitions. The proposed framework leads to substantially enhanced quantitative performance for estimating diffusion tractography related measures and for resolving crossing fibers as compared to a conventional/state-of-the-art dMRI denoising method.

Whole-brain functional magnetic resonance imaging (fMRI), in conjunction with multiband acceleration, has played an important role in mapping the functional connectivity throughout the entire brain with both high temporal and spatial resolution. Ultrahigh magnetic field strengths (7T and above) allow functional imaging with even higher functional contrast-to-noise ratios for improved spatial resolution and specificity compared to traditional field strengths (1.5T and 3T). High-resolution 7T fMRI, however, has primarily been constrained to smaller brain regions given the amount of time it takes to acquire the number of slices necessary for high resolution whole brain imaging. Here we evaluate a range of whole-brain high-resolution resting state fMRI protocols (0.9, 1.25, 1.5, 1.6 and 2mm isotropic voxels) at 7T, obtained with both in-plane and slice acceleration parallel imaging techniques to maintain the temporal resolution and brain coverage typically acquired at 3T. Using the processing pipeline developed by the Human Connectome Project, we demonstrate that high resolution images acquired at 7T provide increased functional contrast to noise ratios with significantly less partial volume effects and more distinct spatial features, potentially allowing for robust individual subject parcellations and descriptions of fine-scaled patterns, such as visuotopic organization.

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.

Diffusion MRI of the cortical gray matter is challenging because the micro-environment probed by water molecules is much more complex than within the white matter. High spatial and angular resolutions are therefore necessary to uncover anisotropic diffusion patterns and laminar structures, which provide complementary (e.g. to anatomical and functional MRI) microstructural information about the cortex architectonic. Several ex-vivo and in-vivo MRI studies have recently addressed this question, however predominantly with an emphasis on specific cortical areas. There is currently no whole brain in-vivo data leveraging multi-shell diffusion MRI acquisition at high spatial resolution, and depth dependent analysis, to characterize the complex organization of cortical fibers. Here, we present unique in-vivo human 7T diffusion MRI data, and a dedicated cortical depth dependent analysis pipeline. We leverage the high spatial (1.05 mm isotropic) and angular (198 diffusion gradient directions) resolution of this whole brain dataset to improve cortical fiber orientations mapping, and study neurites (axons and/or dendrites) trajectories across cortical depths. Tangential fibers in superficial cortical depths and crossing fiber configurations in deep cortical depths are identified. Fibers gradually inserting into the gyral walls are visualized, which contributes to mitigating the gyral bias effect. Quantitative radiality maps and histograms in individual subjects and cortex-based aligned datasets further support our results.

Functional (f)MRI has revolutionized the field of human brain research. fMRI can noninvasively map the spatial architecture of brain function via localized increases in blood flow after sensory or cognitive stimulation. Recent advances in fMRI have led to enhanced sensitivity and spatial accuracy of the measured signals, indicating the possibility of detecting small neuronal ensembles that constitute fundamental computational units in the brain, such as cortical columns. Orientation columns in visual cortex are perhaps the best known example of such a functional organization in the brain. They cannot be discerned via anatomical characteristics, as with ocular dominance columns. Instead, the elucidation of their organization requires functional imaging methods. However, because of insufficient sensitivity, spatial accuracy, and image resolution of the available mapping techniques, thus far, they have not been detected in humans. Here, we demonstrate, by using high-field (7-T) fMRI, the existence and spatial features of orientation- selective columns in humans. Striking similarities were found with the known spatial features of these columns in monkeys. In addition, we found that a larger number of orientation columns are devoted to processing orientations around 90° (vertical stimuli with horizontal motion), whereas relatively similar fMRI signal changes were observed across any given active column. With the current proliferation of high-field MRI systems and constant evolution of fMRI techniques, this study heralds the exciting prospect of exploring unmapped and/or unknown columnar level functional organizations in the human brain.

Gradient and spin echo (GRE and SE, respectively) weighted magnetic resonance images report on neuronal activity via changes in deoxygenated hemoglobin content and cerebral blood volume induced by alterations in neuronal activity. Hence, vasculature plays a critical role in these functional signals. However, how the different blood vessels (e.g. arteries, arterioles, capillaries, venules and veins) quantitatively contribute to the functional MRI (fMRI) signals at each field strength, and consequently, how spatially specific these MRI signals are remain a source of discussion. In this study, we utilize an integrative model of the fMRI signals up to 16.4 T, exploiting the increasing body of published information on relevant physiological parameters. Through simulations, extra- and intravascular functional signal contributions were determined as a function of field strength, echo time (TE) and MRI sequence used. The model predicted previously reported effects, such as feasibility of optimization of SE but not the GRE approach to yield larger micro-vascular compared to macro-vascular weighting. In addition, however, micro-vascular effects were found to peak with increasing magnetic fields even in the SE approach, and further increases in magnetic fields imparted no additional benefits besides beyond the inherent signal-to-noise (SNR) gains. Furthermore, for SE, using a TE larger than the tissue T 2 enhances micro-vasculature signal relatively, though compromising SNR for spatial specificity. In addition, the intravascular SE MRI signals do not fully disappear even at high field strength as arteriolar and capillary contributions persist. The model, and the physiological considerations presented here can also be applied in contrast agent experiments and to other models, such as calibrated BOLD approach and vessel size imaging.

We evaluate residual aliasing among simultaneously excited and acquired slices in slice accelerated multiband (MB) echo planar imaging (EPI). No in-plane accelerations were used in order to maximize and evaluate achievable slice acceleration factors at 3T. We propose a novel leakage (L-) factor to quantify the effects of signal leakage between simultaneously acquired slices. With a standard 32-channel receiver coil at 3T, we demonstrate that slice acceleration factors of up to eight (MB=8) with blipped controlled aliasing in parallel imaging (CAIPI), in the absence of in-plane accelerations, can be used routinely with acceptable image quality and integrity for whole brain imaging. Spectral analyses of single-shot fMRI time series demonstrate that temporal fluctuations due to both neuronal and physiological sources were distinguishable and comparable up to slice-acceleration factors of nine (MB=9). The increased temporal efficiency could be employed to achieve, within a given acquisition period, higher spatial resolution, increased fMRI statistical power, multiple TEs, faster sampling of temporal events in a resting state fMRI time series, increased sampling of q-space in diffusion imaging, or more quiet time during a scan. [Display omitted] •High slice accelerations using multiband (MB) GRE-EPI with blipped CAIPI.•Acceptable MB factors up to 8 with a 32-channel receiver coil at 3T.•Neuronal and physiological sources are distinguishable at high MB factors.•Leakage (L-) factor evaluates residual aliasing among simultaneously acquired slices.•High temporal efficiency with MB-EPI benefits various applications.