Data of NODDI diffusion metrics in the brain and computer simulation of hybrid diffusion imaging (HYDI) acquisition scheme

This article provides NODDI diffusion metrics in the brains of 52 healthy participants and computer simulation data to support compatibility of hybrid diffusion imaging (HYDI), "Hybrid diffusion imaging"[1] acquisition scheme in fitting neurite orientation dispersion and density imaging (NODDI) model, "NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain"[2]. HYDI is an extremely versatile diffusion magnetic resonance imaging (dMRI) technique that enables various analyzes methods using a single diffusion dataset. One of the diffusion data analysis methods is the NODDI computation, which models the brain tissue with three compartments: fast isotropic diffusion (e.g., cerebrospinal fluid), anisotropic hindered diffusion (e.g., extracellular space), and anisotropic restricted diffusion (e.g., intracellular space). The NODDI model produces microstructural metrics in the developing brain, aging brain or human brain with neurologic disorders. The first dataset provided here are the means and standard deviations of NODDI metrics in 48 white matter region-of-interest (ROI) averaging across 52 healthy participants. The second dataset provided here is the computer simulation with initial conditions guided by the first dataset as inputs and gold standard for model fitting. The computer simulation data provide a direct comparison of NODDI indices computed from the HYDI acquisition [1] to the NODDI indices computed from the originally proposed acquisition [2]. These data are related to the accompanying research article "Age Effects and Sex Differences in Human Brain White Matter of Young to Middle-Aged Adults: A DTI, NODDI, and q-Space Study" [3]

Rotating single-shot acquisition (RoSA) with composite reconstruction for fast high-resolution diffusion imaging

PURPOSE: To accelerate high-resolution diffusion imaging, rotating single-shot acquisition (RoSA) with composite reconstruction is proposed. Acceleration was achieved by acquiring only one rotating single-shot blade per diffusion direction, and high-resolution diffusion-weighted (DW) images were reconstructed by using similarities of neighboring DW images. A parallel imaging technique was implemented in RoSA to further improve the image quality and acquisition speed. RoSA performance was evaluated by simulation and human experiments. METHODS: A brain tensor phantom was developed to determine an optimal blade size and rotation angle by considering similarity in DW images, off-resonance effects, and k-space coverage. With the optimal parameters, RoSA MR pulse sequence and reconstruction algorithm were developed to acquire human brain data. For comparison, multishot echo planar imaging (EPI) and conventional single-shot EPI sequences were performed with matched scan time, resolution, field of view, and diffusion directions. RESULTS: The simulation indicated an optimal blade size of 48 × 256 and a 30 ° rotation angle. For 1 × 1 mm2 in-plane resolution, RoSA was 12 times faster than the multishot acquisition with comparable image quality. With the same acquisition time as SS-EPI, RoSA provided superior image quality and minimum geometric distortion. CONCLUSION: RoSA offers fast, high-quality, high-resolution diffusion images. The composite image reconstruction is model-free and compatible with various diffusion computation approaches including parametric and nonparametric analyses. Magn Reson Med 79:264-275, 2018. © 2017 International Society for Magnetic Resonance in Medicine

Age Effects and Sex Differences in Human Brain White Matter of Young to Middle-Aged Adults: A DTI, NODDI, and q-Space Study

Microstructural changes in human brain white matter of young to middle-aged adults were studied using advanced diffusion Magnetic Resonance Imaging (dMRI). Multiple shell diffusion-weighted data were acquired using the Hybrid Diffusion Imaging (HYDI). The HYDI method is extremely versatile and data were analyzed using Diffusion Tensor Imaging (DTI), Neurite Orientation Dispersion and Density Imaging (NODDI), and q-space imaging approaches. Twenty-four females and 23 males between 18 and 55years of age were included in this study. The impact of age and sex on diffusion metrics were tested using least squares linear regressions in 48 white matter regions of interest (ROIs) across the whole brain and adjusted for multiple comparisons across ROIs. In this study, white matter projections to either the hippocampus or the cerebral cortices were the brain regions most sensitive to aging. Specifically, in this young to middle-aged cohort, aging effects were associated with more dispersion of white matter fibers while the tissue restriction and intra-axonal volume fraction remained relatively stable. The fiber dispersion index of NODDI exhibited the most pronounced sensitivity to aging. In addition, changes of the DTI indices in this aging cohort were correlated mostly with the fiber dispersion index rather than the intracellular volume fraction of NODDI or the q-space measurements. While men and women did not differ in the aging rate, men tend to have higher intra-axonal volume fraction than women. This study demonstrates that advanced dMRI using a HYDI acquisition and compartmental modeling of NODDI can elucidate microstructural alterations that are sensitive to age and sex. Finally, this study provides insight into the relationships between DTI diffusion metrics and advanced diffusion metrics of NODDI model and q-space imaging

Hybrid Diffusion Imaging to Detect Acute White Matter Injury after Mild TBI

poster abstractIntroduction: In the present study we used multi-shell Hybrid Diffusion Imaging (HYDI) to study white matter changes in the acute stage of mild traumatic brain injury (mTBI). Non-parametric diffusion analysis, q-space imaging as well as parametric analyses including conventional DTI and novel neurite orientation dispersion and density imaging (NODDI) were used to analyze the HYDI data. Method: Nineteen mTBI patients and 23 trauma-controlled subjects were recruited from the Emergency Department. Participants received T1W SPGR and HYDI in a Philips 3T Achieve TX scanner with 8-channel head coil and SENSE parallel imaging. The diffusion-weighting (DW) pulse sequence scan-time was about 24 min similar to (1). Results: Forty-eight WM ROIs were defined in the standard MNI space by intersecting subjects’ mean WM skeleton with WM atlas of Johns Hopkins University (JHU) ICBM-DTI-81(2). Linear model analysis was used to test the significance of diffusion metrics between mTBI and trauma-controlled groups with gender and age as covariates (model 3 in Table 1). Maps of DTI, q-space and NODDI diffusion metrics of an mTBI subject are shown in Figure 1. Among various diffusion metrics, only the NODDI derived parenchymal axonal density (Vic) was sensitive to mTBI with significant decreases in 60% of WM ROIs (Table 1). The mTBI subjects had an approximately 4% decrease in Vic. The affected WM tracts concentrated on pyramidal tracts and its cortical projections (bilateral corona radiatae). Most of the cerebella related tracts and hippocampal tracts are spared. Conclusion: HYDI and its diffusion metrics provide insights about microstructural changes of WM in the acute stage of mTBI and may prove useful as a marker of injury

Deficits in neurite density underlie white matter structure abnormalities in first-episode psychosis

Background: Structural abnormalities across multiple white matter tracts are recognised in people with early psychosis, consistent with dysconnectivity as a neuropathological account of symptom expression. We applied advanced neuroimaging techniques to characterise microstructural white matter abnormalities for a deeper understanding of the developmental aetiology of psychosis. Methods: Thirty-five first-episode psychosis patients, and 19 healthy controls, participated in a quantitative neuroimaging study using Neurite Orientation Dispersion and Density Imaging (NODDI), a multi-shell diffusion-weighted MRI technique that distinguishes white matter fibre arrangement and geometry from changes in neurite density. Fractional anisotropy (FA) and mean diffusivity images were also derived. Tract-based spatial statistics compared white matter structure between patients and controls and tested associations with age, symptom severity and medication. Results: Patients with first-episode psychosis had lower regional FA in multiple commissural, corticospinal, and association tracts. These abnormalities predominantly colocalized with regions of reduced neurite density, rather than aberrant fibre bundle arrangement (orientation dispersion index). There was no direct relationship with active symptomatology. FA decreased and orientation dispersion index increased with age in patients, but not controls, suggesting accelerated effects of white matter geometry change. Conclusions: Deficits in neurite density appear fundamental to abnormalities in white matter integrity in early psychosis. In the first application of NODDI in psychosis, we found that processes compromising axonal fibre number, density, and myelination, rather than processes leading to spatial disruption of fibre organisation, are implicated in the aetiology of the disorder. This accords with a neurodevelopmental origin of aberrant brain-wide structural connectivity predisposing individuals to psychosis

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works

Ageing and brain white matter structure in 3513 UK Biobank participants

Quantifying the microstructural properties of the human brain's connections is necessary for understanding normal ageing and disease. Here we examine brain white matter magnetic resonance imaging (MRI) data in 3,513 generally healthy people aged 44.64–77.12 years from the UK Biobank. Using conventional water diffusion measures and newer, rarely studied indices from neurite orientation dispersion and density imaging, we document large age associations with white matter microstructure. Mean diffusivity is the most age-sensitive measure, with negative age associations strongest in the thalamic radiation and association fibres. White matter microstructure across brain tracts becomes increasingly correlated in older age. This may reflect an age-related aggregation of systemic detrimental effects. We report several other novel results, including age associations with hemisphere and sex, and comparative volumetric MRI analyses. Results from this unusually large, single-scanner sample provide one of the most extensive characterizations of age associations with major white matter tracts in the human brain

Predicting age from cortical structure across the lifespan

Despite inter-individual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. The present study assessed how accurately an individual’s age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification, and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from 1 region to 1000 regions. The age-prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated non-linear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology