Merry Mani

Neural bases of selective attention in action video game players

Over the past few years, the very act of playing action video games has been shown to enhance several different aspects of visual selective attention, yet little is known about the neural mechanisms that mediate such attentional benefits. A review of the aspects of attention enhanced in action game players suggests there are changes in the mechanisms that control attention allocation and its efficiency (Hubert-Wallander, Green, & Bavelier, 2010). The present study used brain imaging to test this hypothesis by comparing attentional network recruitment and distractor processing in action gamers versus non-gamers as attentional demands increased. Moving distractors were found to elicit lesser activation of the visual motion-sensitive area (MT/MST) in gamers as compared to non-gamers, suggestive of a better early filtering of irrelevant information in gamers. As expected, a fronto-parietal network of areas showed greater recruitment as attentional demands increased in non-gamers. In contrast, gamers barely engaged this network as attentional demands increased. This reduced activity in the fronto-parietal network that is hypothesized to control the flexible allocation of top-down attention is compatible with the proposal that action game players may allocate attentional resources more automatically, possibly allowing more efficient early filtering of irrelevant information.

Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI

Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the “HARDI reconstruction challenge” organized in the context of the “ISBI 2012” conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach. This information can be useful not only for enhancing current algorithms and develop the next generation of reconstruction methods, but also to assist physicians in the choice of the most adequate technique for their studies.

Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data

To accelerate the acquisition of simultaneously high spatial and angular resolution diffusion imaging.

Multi-shot sensitivity-encoded diffusion data recovery using structured low-rank matrix completion (MUSSELS)

To introduce a novel method for the recovery of multi‐shot diffusion weighted (MS‐DW) images from echo‐planar imaging (EPI) acquisitions.

Efficient parallel reconstruction for high resolution multishot spiral diffusion data with low rank constraint.

To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging.

Fast iterative algorithm for the reconstruction of multishot non-cartesian diffusion data.

To accelerate the motion‐compensated iterative reconstruction of multishot non‐Cartesian diffusion data.

Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing

Achieving simultaneously high angular and spatial resolution in diffusion imaging is challenging because of the long acquisition times involved. We propose a novel compressed sensing method to acquire high angular and spatial resolution diffusion imaging data, while keeping the scan time reasonable. We show that joint under sampling of 6-D k-q space is more efficient than undersampling only one of the dimensions. We use a sparse Gaussian mixture model and an iterative reconstruction scheme to recover the peaks of the orientation distribution functions (ODF) with high accuracy. We show that at least 6-fold acceleration of acquisition is possible, thereby enabling high angular and spatial resolution diffusion imaging in a reasonable scan time.

Accelerating non-Cartesian sense for large coil arrays: Application to motion compensation in multishot DWI

Multi-shot sequences are often combined with techniques such as parallel imaging to achieve high fidelity images. For non-Cartesian sampling schemes, the reconstruction of such data becomes extremely computationally intensive. The problem of motion compensated SENSE reconstruction of non-Cartesian diffusion weighted images falls into a similar setting, when phase corrections are involved. Here we propose a new pipeline for the fast reconstruction of such data. The large array of composite sensitivity functions are replaced by a low-dimensional set of virtual sensitivity functions using a principal component analysis, thus enabling the evaluation of very few Fourier transforms. The time consuming gridding steps are replaced by a more efficient multiplication in the k-space, enabling further simplifications. Significant acceleration of reconstruction time is shown to be achieved with the new scheme. The algorithm in the general setting can accelerate SENSE reconstruction for large coil arrays.

Alterations of the cerebellum and basal ganglia in bipolar disorder mood states detected by quantitative T1ρ mapping

Quantitative mapping of T1 relaxation in the rotating frame (T1ρ) is a magnetic resonance imaging technique sensitive to pH and other cellular and microstructural factors, and is a potentially valuable tool for identifying brain alterations in bipolar disorder. Recently, this technique identified differences in the cerebellum and cerebral white matter of euthymic patients vs healthy controls that were consistent with reduced pH in these regions, suggesting an underlying metabolic abnormality. The current study built upon this prior work to investigate brain T1ρ differences across euthymic, depressed, and manic mood states of bipolar disorder.

Comprehensive reconstruction of multi-shot multi-channel diffusion data using mussels

Echo planar imaging (EPI)-based magnetic resonance imaging (MRI) data are often corrupted by Nyquist ghost artifacts resulting from odd-even shifts of the EPI read-outs. Algorithms that corrects for the Nyquist ghost artifacts rely on calibration scans that are collected prior to the data acquisition. However, a more complex pattern of ghosting artifacts arises when diffusion-weighted data are acquired using segmented k-space EPI read-outs. The additional under-sampling present in the segmented acquisitions and the inter-shot motion during diffusion weighted acquistion cause ghosting artifacts in addition to the EPI ghosting arising from odd-even shifts. We propose a comprehensive method that can remove the Nyquist-ghosting artifacts as well as the inter-shot motion-induced ghosting artifacts in diffusion weighted images in a single step from partial Fourier data without the need for a calibration scan. We show very high quality diffusion data recovery using the proposed method.