Compressive sensing based Q-space resampling for handling fast bulk motion in hardi acquisitions

Diffusion-weighted (DW) MRI has become a widely adopted imaging modality to reveal the underlying brain connectivity. Long acquisition times and/or non-cooperative patients increase the chances of motion-related artifacts. Whereas slow bulk motion results in inter-gradient misalignment which can be handled via retrospective motion correction algorithms, fast bulk motion usually affects data during the application of a single diffusion gradient causing signal dropout artifacts. Common practices opt to discard gradients bearing signal attenuation due to the difficulty of their retrospective correction, with the disadvantage to lose full gradients for further processing. Nonetheless, such attenuation might only affect limited number of slices within a gradient volume. Q-space resampling has recently been proposed to recover corrupted slices while saving gradients for subsequent reconstruction. However, few corrupted gradients are implicitly assumed which might not hold in case of scanning unsedated infants or patients in pain. In this paper, we propose to adopt recent advances in compressive sensing based reconstruction of the diffusion orientation distribution functions (ODF) with under sampled measurements to resample corrupted slices. We make use of Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) basis functions which can analytically model ODF from arbitrary sampled signals. We demonstrate the impact of the proposed resampling strategy compared to state-of-art resampling and gradient exclusion on simulated intra-gradient motion as well as samples from real DWI data

Individual Differences in Impulsivity and Mesocorticolimbic Connectivity Strength in Pre-adolescence

Individual differences in pre-adolescent impulsivity, or the preference for smaller immediate rewards over larger delayed rewards, has been related to a multitude of outcomes measured later in life, such as physical and psychological health, substance dependence, financial well-being, academic achievement, social adjustment, and criminal behaviour. The mesocorticolimbic dopamine pathway (MCLP), a neural circuitry involved in reward motivated behaviours and decision-making, has been extensively linked to the delay discounting task, an effective tool for quantifying trait impulsivity. While previous research has demonstrated a negative correlation between the structural connectivity strength of the right dorsolateral prefrontal tract and the functional activity of striatum throughout development, the differences in tract strength within the MCLP and the relation to interindividual differences in impulsive behaviour in pre-adolescence has been understudied. The current study hypothesized that MCLP white fiber tract strength is related to interindividual differences of trait impulsivity in participants aged 9 to 12 years old. A probabilistic tractography approach, where every seed region voxel is sampled 1000 times for streamlines to the target of interest, was used to assess tract connectivity in a 58 X 58 whole-brain matrix. After correcting for multiple comparisons, the results demonstrated no significant correlations between white matter connectivity and individual differences in the delay discounting task. Given the small sample size and univariate approach, this large scale analysis was not sufficiently powered to detect any relationship between white matter and impulsivity. Future studies should apply further steps, such as correction for susceptibility induced distortions, to the constructed pipeline and investigate white matter differences with a variety of tensor metrics, such as fractional anisotropy and mean diffusivity

Bayesian uncertainty quantification in linear models for diffusion MRI

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.Comment: Added results from a group analysis and a comparison with residual bootstra

Retrospective head motion estimation in structural brain MRI with 3D CNNs

Head motion is one of the most important nuisance variables in neuroimaging, particularly in studies of clinical or special populations, such as children. However, the possibility of estimating motion in structural MRI is limited to a few specialized sites using advanced MRI acquisition techniques. Here we propose a supervised learning method to retrospectively estimate motion from plain MRI. Using sparsely labeled training data, we trained a 3D convolutional neural network to assess if voxels are corrupted by motion or not. The output of the network is a motion probability map, which we integrate across a region of interest (ROI) to obtain a scalar motion score. Using cross-validation on a dataset of n=48 healthy children scanned at our center, and the cerebral cortex as ROI, we show that the proposed measure of motion explains away 37% of the variation in cortical thickness. We also show that the motion score is highly correlated with the results from human quality control of the scans. The proposed technique can not only be applied to current studies, but also opens up the possibility of reanalyzing large amounts of legacy datasets with motion into consideration: we applied the classifier trained on data from our center to the ABIDE dataset (autism), and managed to recover group differences that were confounded by motion