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Purpose: To develop a new method for high‐fidelity, high‐resolution 3D multi‐slab diffusion MRI with minimal distortion and boundary slice aliasing. Methods: Our method modifies 3D multi‐slab imaging to integrate blip‐reversed acquisitions for distortion correction and oversampling in the slice direction (kz) for reducing boundary slice aliasing. Our aim is to achieve robust acceleration to keep the scan time the same as conventional 3D multi‐slab acquisitions, in which data are acquired with a single direction of blip traversal and without kz‐oversampling. We employ a two‐stage reconstruction. In the first stage, the blip‐up/down images are respectively reconstructed and analyzed to produce a field map for each diffusion direction. In the second stage, the blip‐reversed data and the field map are incorporated into a joint reconstruction to produce images that are corrected for distortion and boundary slice aliasing. Results: We conducted experiments at 7T in six healthy subjects. Stage 1 reconstruction produces images from highly under‐sampled data (R = 7.2) with sufficient quality to provide accurate field map estimation. Stage 2 joint reconstruction substantially reduces distortion artifacts with comparable quality to fully‐sampled blip‐reversed results (2.4× scan time). Whole‐brain in‐vivo results acquired at 1.22 mm and 1.05 mm isotropic resolutions demonstrate improved anatomical fidelity compared to conventional 3D multi‐slab imaging. Data demonstrate good reliability and reproducibility of the proposed method over multiple subjects. Conclusion: The proposed acquisition and reconstruction framework provide major reductions in distortion and boundary slice aliasing for 3D multi‐slab diffusion MRI without increasing the scan time, which can potentially produce high‐quality, high‐resolution diffusion MRI. [ABSTRACT FROM AUTHOR]

The self-diffusion tensor may be calculated from several echo-planar image acquisitions preceded by different diffusion gradients. Unfortunately, these diffusion gradients cause geometric distortion that must be corrected before estimation of the tensor. In the present paper we suggest and implement a method for retrospective correction of these distortions firmly based on a physical model for the diffusion-weighted images. This method simultaneously estimates subject movement and distortion parameters by finding the set of parameters that minimizes residual error when fitting data to the diffusion tensor model. We show how this notion can be formalized as a quadratic form thereby facilitating the implementation of a rapid algorithm. In addition, we suggest models for how distortions vary with slice position and gradient direction that allow us to substantially reduce the dimensionality of the parameter space. Our results indicate that we are able to estimate both eddy current-induced distortion and subject movement directly from the data without need of any additional measurements. [Copyright &y& Elsevier]

Accurate spatial alignment of MRI data acquired across multiple contrasts in the same subject is often crucial for data analysis and interpretation, but can be challenging in the presence of geometric distortions that differ between acquisitions. It is well known that single-shot echo-planar imaging (EPI) acquisitions suffer from distortion in the phase-encoding direction due to B 0 field inhomogeneities arising from tissue magnetic susceptibility differences and other sources, however there can be distortion in other encoding directions as well in the presence of strong field inhomogeneities. High-resolution ultrahigh-field MRI typically uses low bandwidth in the slice-encoding direction to acquire thin slices and, when combined with the pronounced B 0 inhomogeneities, is prone to an additional geometric distortion in the slice direction as well. Here we demonstrate the presence of this slice distortion in high-resolution 7T EPI acquired with a novel pulse sequence allowing for the reversal of the slice-encoding gradient polarity that enables the acquisition of pairs of images with equal magnitudes of distortion in the slice direction but with opposing polarities. We also show that the slice-direction distortion can be corrected using gradient reversal-based method applying the same software used for conventional corrections of phase-encoding direction distortion., (Copyright © 2022. Published by Elsevier Inc.)

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflicts of interest in relation to this manuscript., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

White matter (WM) plasticity supports skill learning and memory. Up- and downregulation of brain activity in animal models lead to WM alterations. But can bidirectional brain-activity manipulation change WM structure in the adult human brain? We employ fMRI neurofeedback to endogenously and directionally modulate activity in the sensorimotor cortices. Diffusion tensor imaging is acquired before and after two separate conditions, involving regulating sensorimotor activity either up or down using real or sham neurofeedback (n = 20 participants × 4 scans). We report rapid opposing changes in corpus callosum microstructure that depend on the direction of activity modulation. Our findings show that fMRI neurofeedback can be used to endogenously and directionally alter not only brain-activity patterns but also WM pathways connecting the targeted brain areas. The level of associated brain activity in connected areas is therefore a possible mediator of previously described learning-related changes in WM., Competing Interests: Declaration of interests R.G. is CEO and M.L. is an employee of Brain Innovation B.V., which makes the Turbo-Brain Voyager software used for the real-time fMRI analyses. The remaining authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds., (Copyright © 2020. Published by Elsevier Inc.)

Nonlinear registration is critical to many aspects of Neuroimaging research. It facilitates averaging and comparisons across multiple subjects, as well as reporting of data in a common anatomical frame of reference. It is, however, a fundamentally ill-posed problem, with many possible solutions which minimise a given dissimilarity metric equally well. We present a regularisation method capable of selectively driving solutions towards those which would be considered anatomically plausible by penalising unlikely lineal, areal and volumetric deformations. This penalty is symmetric in the sense that geometric expansions and contractions are penalised equally, which encourages inverse-consistency. We demonstrate that this method is able to significantly reduce local volume changes and shape distortions compared to state-of-the-art elastic (FNIRT) and plastic (ANTs) registration frameworks. Crucially, this is achieved whilst simultaneously matching or exceeding the registration quality of these methods, as measured by overlap scores of labelled cortical regions. Extensive leveraging of GPU parallelisation has allowed us to solve this highly computationally intensive optimisation problem while maintaining reasonable run times of under half an hour., Competing Interests: Declaration of competing interest We have no conflicts of interest to declare., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: Studies using diffusion tensor imaging (DTI) to investigate white matter (WM) microstructure in youths with conduct disorder (CD) have reported disparate findings. We investigated WM alterations in a large sample of youths with CD, and examined the influence of sex and callous-unemotional (CU) traits., Method: DTI data were acquired from 124 youths with CD (59 female) and 174 typically developing (TD) youths (103 female) 9 to 18 years of age. Tract-based spatial statistics tested for effects of diagnosis and sex-by-diagnosis interactions. Associations with CD symptoms, CU traits, a task measuring impulsivity, and the impact of comorbidity, and age- and puberty-related effects were examined., Results: Youths with CD exhibited higher axial diffusivity in the corpus callosum and lower radial diffusivity and mean diffusivity in the anterior thalamic radiation relative to TD youths. Female and male youths with CD exhibited opposite changes in the left hemisphere within the internal capsule, fornix, posterior thalamic radiation, and uncinate fasciculus. Within the CD group, CD symptoms and callous traits exerted opposing influences on corpus callosum axial diffusivity, with callous traits identified as the unique clinical feature predicting higher axial diffusivity and lower radial diffusivity within the corpus callosum and anterior thalamic radiation, respectively. In an exploratory analysis, corpus callosum axial diffusivity partially mediated the association between callous traits and impulsive responses to emotional faces. Results were not influenced by symptoms of comorbid disorders, and no age- or puberty-related interactions were observed., Conclusion: WM alterations within the corpus callosum represent a reliable neuroimaging marker of CD. Sex and callous traits are important factors to consider when examining WM in CD., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Image acceleration provides multiple benefits to diffusion MRI, with in-plane acceleration reducing distortion and slice-wise acceleration increasing the number of directions that can be acquired in a given scan time. However, as acceleration factors increase, the reconstruction problem becomes ill-conditioned, particularly when using both in-plane acceleration and simultaneous multislice imaging. In this work, we develop a novel reconstruction method for in vivo MRI acquisition that provides acceleration beyond what conventional techniques can achieve., Theory and Methods: We propose to constrain the reconstruction in the spatial (k) domain by incorporating information from the angular (q) domain. This approach exploits smoothness of the signal in q-space using Gaussian processes, as has previously been exploited in post-reconstruction analysis. We demonstrate in-plane undersampling exceeding the theoretical parallel imaging limits, and simultaneous multislice combined with in-plane undersampling at a total factor of 12. This reconstruction is cast within a Bayesian framework that incorporates estimation of smoothness hyper-parameters, with no need for manual tuning., Results: Simulations and in vivo results demonstrate superior performance of the proposed method compared with conventional parallel imaging methods. These improvements are achieved without loss of spatial or angular resolution and require only a minor modification to standard pulse sequences., Conclusion: The proposed method provides improvements over existing methods for diffusion acceleration, particularly for high simultaneous multislice acceleration with in-plane undersampling., (© 2019 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)