Improved fibre dispersion estimation using b-tensor encoding.

Measuring fibre dispersion in white matter with diffusion magnetic resonance imaging (MRI) is limited by an inherent degeneracy between fibre dispersion and microscopic diffusion anisotropy (i.e., the diffusion anisotropy expected for a single fibre orientation). This means that estimates of fibre dispersion rely on strong assumptions, such as constant microscopic anisotropy throughout the white matter or specific biophysical models. Here we present a simple approach for resolving this degeneracy using measurements that combine linear (conventional) and spherical tensor diffusion encoding. To test the accuracy of the fibre dispersion when our microstructural model is only an approximation of the true tissue structure, we simulate multi-compartment data and fit this with a single-compartment model. For such overly simplistic tissue assumptions, we show that the bias in fibre dispersion is greatly reduced (~5x) for single-shell linear and spherical tensor encoding data compared with single-shell or multi-shell conventional data. In in-vivo data we find a consistent estimate of fibre dispersion as we reduce the b-value from 3 to 1.5 ms/µm2, increase the repetition time, increase the echo time, or increase the diffusion time. We conclude that the addition of spherical tensor encoded data to conventional linear tensor encoding data greatly reduces the sensitivity of the estimated fibre dispersion to the model assumptions of the tissue microstructure.

Use of multi-flip angle measurements to account for transmit inhomogeneity and non-Gaussian diffusion in DW-SSFP.

Diffusion-weighted steady-state free precession (DW-SSFP) is an SNR-efficient diffusion imaging method. The improved SNR and resolution available at ultra-high field has motivated its use at 7T. However, these data tend to have severe B1 inhomogeneity, leading not only to spatially varying SNR, but also to spatially varying diffusivity estimates, confounding comparisons both between and within datasets. This study proposes the acquisition of DW-SSFP data at two-flip angles in combination with explicit modelling of non-Gaussian diffusion to address B1 inhomogeneity at 7T. Data were acquired from five fixed whole human post-mortem brains with a pair of flip angles that jointly optimize the diffusion contrast-to-noise (CNR) across the brain. We compared one- and two-flip angle DW-SSFP data using a tensor model that incorporates the full DW-SSFP Buxton signal, in addition to tractography performed over the cingulum bundle and pre-frontal cortex using a ball & sticks model. The two-flip angle DW-SSFP data produced angular uncertainty and tractography estimates close to the CNR optimal regions in the single-flip angle datasets. The two-flip angle tensor estimates were subsequently fitted using a modified DW-SSFP signal model that incorporates a gamma distribution of diffusivities. This allowed us to generate tensor maps at a single effective b-value yielding more consistent SNR across tissue, in addition to eliminating the B1 dependence on diffusion coefficients and orientation maps. Our proposed approach will allow the use of DW-SSFP at 7T to derive diffusivity estimates that have greater interpretability, both within a single dataset and between experiments.

Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes.

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, a single GPU achieves better performance than 200 CPU cores thanks to our parallel designs.

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.

Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey.

UNLABELLED: Tractography based on diffusion MRI offers the promise of characterizing many aspects of long-distance connectivity in the brain, but requires quantitative validation to assess its strengths and limitations. Here, we evaluate tractography's ability to estimate the presence and strength of connections between areas of macaque neocortex by comparing its results with published data from retrograde tracer injections. Probabilistic tractography was performed on high-quality postmortem diffusion imaging scans from two Old World monkey brains. Tractography connection weights were estimated using a fractional scaling method based on normalized streamline density. We found a correlation between log-transformed tractography and tracer connection weights of r = 0.59, twice that reported in a recent study on the macaque. Using a novel method to estimate interareal connection lengths from tractography streamlines, we regressed out the distance dependence of connection strength and found that the correlation between tractography and tracers remains positive, albeit substantially reduced. Altogether, these observations provide a valuable, data-driven perspective on both the strengths and limitations of tractography for analyzing interareal corticocortical connectivity in nonhuman primates and a framework for assessing future tractography methodological refinements objectively. SIGNIFICANCE STATEMENT: Tractography based on diffusion MRI has great potential for a variety of applications, including estimation of comprehensive maps of neural connections in the brain ("connectomes"). Here, we describe methods to assess quantitatively tractography's performance in detecting interareal cortical connections and estimating connection strength by comparing it against published results using neuroanatomical tracers. We found the correlation of tractography's estimated connection strengths versus tracer to be twice that of a previous study. Using a novel method for calculating interareal cortical distances, we show that tractography-based estimates of connection strength have useful predictive power beyond just interareal separation. By freely sharing these methods and datasets, we provide a valuable resource for future studies in cortical connectomics.

Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project.

Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually.

Comparison of CPU and GPU bayesian estimates of fibre orientations from diffusion MRI.

BACKGROUND: The correct estimation of fibre orientations is a crucial step for reconstructing human brain tracts. Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques (bedpostx) is able to estimate several fibre orientations and their diffusion parameters per voxel using Markov Chain Monte Carlo (MCMC) in a whole brain diffusion MRI data, and it is capable of running on GPUs, achieving speed-up of over 100 times compared to CPUs. However, few studies have looked at whether the results from the CPU and GPU algorithms differ. In this study, we compared CPU and GPU bedpostx outputs by running multiple trials of both algorithms on the same whole brain diffusion data and compared each distribution of output using Kolmogorov-Smirnov tests. RESULTS: We show that distributions of fibre fraction parameters and principal diffusion direction angles from bedpostx and bedpostx_gpu display few statistically significant differences in shape and are localized sparsely throughout the whole brain. Average output differences are small in magnitude compared to underlying uncertainty. CONCLUSIONS: Despite small amount of differences in output between CPU and GPU bedpostx algorithms, results are comparable given the difference in operation order and library usage between CPU and GPU bedpostx.

Running Neuroimaging Applications on Amazon Web Services: How, When, and at What Cost?

The contribution of this paper is to identify and describe current best practices for using Amazon Web Services (AWS) to execute neuroimaging workflows "in the cloud." Neuroimaging offers a vast set of techniques by which to interrogate the structure and function of the living brain. However, many of the scientists for whom neuroimaging is an extremely important tool have limited training in parallel computation. At the same time, the field is experiencing a surge in computational demands, driven by a combination of data-sharing efforts, improvements in scanner technology that allow acquisition of images with higher image resolution, and by the desire to use statistical techniques that stress processing requirements. Most neuroimaging workflows can be executed as independent parallel jobs and are therefore excellent candidates for running on AWS, but the overhead of learning to do so and determining whether it is worth the cost can be prohibitive. In this paper we describe how to identify neuroimaging workloads that are appropriate for running on AWS, how to benchmark execution time, and how to estimate cost of running on AWS. By benchmarking common neuroimaging applications, we show that cloud computing can be a viable alternative to on-premises hardware. We present guidelines that neuroimaging labs can use to provide a cluster-on-demand type of service that should be familiar to users, and scripts to estimate cost and create such a cluster.

Revista Cubana de Alimentación y Nutrición: índice analítico (1987-2002) / Revista Cubana de Alimentación y Nutrición: analytical index (1987-2002)

En 1987 surgió la Revista Cubana de Alimentación y Nutrición para beneplácito de los nutricionistas y otros profesionales de las ciencias de la salud vinculados a los problemas alimentarios y nutricionales, así como de otros especialistas que desarrollan su labor en el terreno de la alimentación y de la nutrición humana. Se revelan los antecedentes de la puesta en circulación de esta revista y se fundamentan los objetivos que cumplió durante los 16 años que se mantuvo como fuente de información y medio de divulgación principal de los resultados de las investigaciones en esta disciplina en Cuba. Se presenta un índice analítico donde se registran las referencias de todos los trabajos que guardan sus páginas e incluye los nombres de sus autores y de los asuntos tratados por ellos. Este inventario bibliográfico permite tener disponible en un solo cuerpo todo lo acontecido en esta revista en su tiempo de existencia, a la vez que constituye un modesto homenaje de recordación y reconocimiento a su principal gestor y primer director, el doctor John Gay Rodríguez(AU)

The Revista Cubana de Alimentación y Nutrición (Cuban Journal of Food and Nutrition) appeared for the first time in 1987 as a tool for nutritionists and other professionals of health sciences linked with food and nutritional problems, as well as of other specialists developing its job in the field of human food and nutrition. The antecedents of the circulation of this journal are revealed, and the goals fulfilled during the 16 years of existence as a source of information and as a main mean for spreading the research results achieved in this discipline in Cuba are founded. An analytic review, where all the references of the papers, including the names of the authors and the topics dealt with, is presented. This bibliographic inventory allows to have in only one body all the history of this journal, and it is a modest tribute to remember and recognize its main managing director, John Gay Rodríguez, M.D(AU)

El tema nutrición y alimentación en la bibliografía médica cubana del período colonial / The topic of nutrition and food in the Cuban medical bibliography of the colonial period

Tras una breve incursión introductoria en la bibliografía médica internacional que abordó el tema nutrición y alimentación entre los siglos XVII y XIX, se dan a conocer los primeros escritos producidos en Cuba sobre el asunto, que aparecen en las Actas Capitulares del Ayuntamiento de La Habana y de los cuales se hace una sucinta discusión relativa a las circunstancias de su surgimiento. Se presenta la lista de las primeras noticias y artículos redactados sobre la materia, registrados en el Papel Periódico de La Havana entre 1795 y 1844. Se relacionan las revistas médicas cubanas editadas durante el siglo XIX, en cuyas páginas se atesoran trabajos relacionados con la disciplina, y se ofrece el inventario por año de los artículos en ellas divulgados. Por último, se inscriben los títulos de los trabajos publicados sobre el tema en la isla o fuera de ella por autores cubanos, en forma de libros, monografías o folletos entre 1842 y 1900

El tema nutrición y alimentación en la bibliografía médica cubana del período colonial / The topic of nutrition and food in the Cuban medical bibliography of the colonial period

Tras una breve incursión introductoria en la bibliografía médica internacional que abordó el tema nutrición y alimentación entre los siglos XVII y XIX, se dan a conocer los primeros escritos producidos en Cuba sobre el asunto, que aparecen en las Actas Capitulares del Ayuntamiento de La Habana y de los cuales se hace una sucinta discusión relativa a las circunstancias de su surgimiento. Se presenta la lista de las primeras noticias y artículos redactados sobre la materia, registrados en el Papel Periódico de La Havana entre 1795 y 1844. Se relacionan las revistas médicas cubanas editadas durante el siglo XIX, en cuyas páginas se atesoran trabajos relacionados con la disciplina, y se ofrece el inventario por año de los artículos en ellas divulgados. Por último, se inscriben los títulos de los trabajos publicados sobre el tema en la isla o fuera de ella por autores cubanos, en forma de libros, monografías o folletos entre 1842 y 1900(AU)