For human brain magnetic resonance imaging (MRI), high channel count ( ≥ 32 ) radiofrequency receiver coil arrays are utilized to achieve maximum signal-to-noise ratio (SNR) and to accelerate parallel imaging techniques. With ultra-high field (UHF) MRI at 7 tesla (T) and higher, dipole antenna arrays have been shown to generate high SNR in the deep regions of the brain, however the array elements exhibit increased electromagnetic coupling with one another, making array construction more difficult with the increasing number of elements. Compared to a classical dipole antenna array, a sleeve antenna array incorporates the coaxial ground into the feed-point, resulting in a modified asymmetric antenna structure with improved intra-element decoupling. Here, we extended our previous 16-channel sleeve transceiver work and developed a 32-channel azimuthally arranged sleeve antenna receive-only array for 10.5 T human brain imaging. We experimentally compared the achievable SNR of the sleeve antenna array at 10.5 T to a more traditional 32-channel loop array bult onto a human head-shaped former. The results obtained with a head shaped phantom clearly demonstrated that peripheral intrinsic SNR can be significantly improved compared to a loop array with the same number of elements- except for the superior part of the phantom where sleeve antenna elements are not located.

Multi-element transmit arrays with low peak 10 g specific absorption rate (SAR) and high SAR efficiency (defined as ( [Formula: see text]SAR [Formula: see text] are essential for ultra-high field (UHF) magnetic resonance imaging (MRI) applications. Recently, the adaptation of dipole antennas used as MRI coil elements in multi-channel arrays has provided the community with a technological solution capable of producing uniform images and low SAR efficiency at these high field strengths. However, human head-sized arrays consisting of dipole elements have a practical limitation to the number of channels that can be used due to radiofrequency (RF) coupling between the antenna elements, as well as, the coaxial cables necessary to connect them. Here we suggest an asymmetric sleeve antenna as an alternative to the dipole antenna. When used in an array as MRI coil elements, the asymmetric sleeve antenna can generate reduced peak 10 g SAR and improved SAR efficiency. To demonstrate the advantages of an array consisting of our suggested design, we compared various performance metrics produced by 16-channel arrays of asymmetric sleeve antennas and dipole antennas with the same dimensions. Comparison data were produced on a phantom in electromagnetic (EM) simulations and verified with experiments at 10.5 Tesla (T). The results produced by the 16-channel asymmetric sleeve antenna array demonstrated 28 % lower peak 10 g SAR and 18.6 % higher SAR efficiency when compared to the 16-channel dipole antenna array.

MOTIVATION: Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and identify the roots of psychiatric diseases. However, the results from most neuroimaging studies, i.e. activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. RESULTS: We describe a brain annotation toolbox that generates functional and genetic annotations for neuroimaging results. The voxel-level functional description from the Neurosynth database and gene expression profile from the Allen Human Brain Atlas are used to generate functional/genetic information for region-level neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and genetic similarity network are highly correlated for major brain atlases. One application of brain annotation toolbox is to help provide functional/genetic annotations for newly discovered regions with unknown functions, e.g. the 97 new regions identified in the Human Connectome Project. Importantly, this toolbox can help understand differences between psychiatric patients and controls, and this is demonstrated using schizophrenia and autism data, for which the functional and genetic annotations for the neuroimaging changes in patients are consistent with each other and help interpret the results. AVAILABILITY AND IMPLEMENTATION: BAT is implemented as a free and open-source MATLAB toolbox and is publicly available at http://123.56.224.61:1313/post/bat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Magnetic Resonance Imaging (MRI) is one of the leading modalities for medical imaging, providing excellent soft-tissue contrast without exposure to ionizing radiation. Despite continuing advances in MRI, long scan times remain a major limitation in clinical applications. Parallel imaging is a technique for scan time acceleration in MRI, which utilizes the spatial variations in the reception profiles of receiver coil arrays to reconstruct images from undersampled Fourier space, i.e. k-space. One of the most commonly used parallel imaging techniques employs interpolation of missing k-space information by using linear shift-invariant convolutional kernels. These kernels are trained on a limited amount of autocalibration signal (ACS) for each scan. We propose a novel method for parallel imaging,Robust Artificial-neural-networks for k-space Interpolation (RAKI), which uses scan-specific convolutional neural networks (CNNs) to perform improved k-space interpolation. Three-layer CNNs are trained using only scan-specific ACS data, alleviating the need for large training databases. The proposed method was tested in ultra-high resolution brain MRI and quantitative cardiac MRI, acquired with various acceleration rates. Improved noise resilience as compared to existing parallel imaging methods was observed for high acceleration rates or in the presence of low signal-to-noise ratio (SNR). Furthermore, RAKI successfully reconstructed images for quantitative cardiac MRI, even when using the same CNN across images with varying contrasts. These results indicate that RAKI achieves improved noise performance without overfitting to specific image contents, and offers great promise for improved acceleration in a wide range of MRI applications.

Functional neuroimaging research provides detailed observations of the response patterns that natural sounds (e.g. human voices and speech, animal cries, environmental sounds) evoke in the human brain. The computational and representational mechanisms underlying these observations, however, remain largely unknown. Here we combine high spatial resolution (3 and 7 Tesla) functional magnetic resonance imaging (fMRI) with computational modeling to reveal how natural sounds are represented in the human brain. We compare competing models of sound representations and select the model that most accurately predicts fMRI response patterns to natural sounds. Our results show that the cortical encoding of natural sounds entails the formation of multiple representations of sound spectrograms with different degrees of spectral and temporal resolution. The cortex derives these multi-resolution representations through frequency-specific neural processing channels and through the combined analysis of the spectral and temporal modulations in the spectrogram. Furthermore, our findings suggest that a spectral-temporal resolution trade-off may govern the modulation tuning of neuronal populations throughout the auditory cortex. Specifically, our fMRI results suggest that neuronal populations in posterior/dorsal auditory regions preferably encode coarse spectral information with high temporal precision. Vice-versa, neuronal populations in anterior/ventral auditory regions preferably encode fine-grained spectral information with low temporal precision. We propose that such a multi-resolution analysis may be crucially relevant for flexible and behaviorally-relevant sound processing and may constitute one of the computational underpinnings of functional specialization in auditory cortex.

The trade-off between signal-to-noise ratio (SNR) and spatial specificity governs the choice of spatial resolution in magnetic resonance imaging (MRI); diffusion-weighted (DW) MRI is no exception. Images of lower resolution have higher signal to noise ratio, but also more partial volume artifacts. We present a data-fusion approach for tackling this trade-off by combining DW MRI data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fiber patterns and diffusion parameters. The proposed model, therefore, combines the benefits of each acquisition. We show that fiber crossings at the highest spatial resolution can be inferred more robustly and accurately using such a model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time.

In the past decade, magnetic resonance imaging (MRI) research has been focused on the acquisition of physiological and biochemical information noninvasively. Probably the most notable accomplishment in this general effort has been the introduction of the MR approaches to map brain function. This capability, often referred to as functional magnetic resonance imaging, or fMRI, is based on the sensitivity of MR signals to secondary metabolic and hemodynamic responses that accompany increased neuronal activity. Despite this indirect link to neurotransmission, recent studies demonstrate that under appropriate conditions, these fMRI maps have accuracy at the scale of submillimeter neuronal organizations such as the orientation columns of the visual cortex, and are directly proportional in magnitude to electrical signals generated by the neurons. High magnetic fields have been critical in achieving such specificity in functional maps because they provide advantages through increased signal-to-noise ratio, diminishing blood-related contributions to mapping signals, and enhanced sensitivity to microvasculature. Equally important is MR spectroscopy studies, which, at high magnetic fields, provide for the first time the opportunity to measure local metabolic correlates of human brain function and neurotransmission rates. Together, these MR methods provide a complementary set of approaches for probing important aspects of the nervous system.

In laboratories around the world, neuroscientists from diverse disciplines are exploring various aspects of brain structure. Because of the size of the domain, neuroscientists must specialize, making it difficult to fit results together, causing some research efforts to be duplicated because of lack of sharing of information. The authors have begun a long-term project to build a neuroscience research database for brain structure. One aspect of the database is the ability to visualize high-quality, high-resolution micrographs montaged together into 3-D structures as they were in the living brain. As demonstrated in this paper's analysis, realistic presentation of these visualizations across computer networks will stress current and proposed gigabit networks. Image compression can reduce network loads, but wide-spread use of the visualizations will still require networks capable of sustaining terabits per second of throughput.>