Learning-based method for k-space trajectory design in MRI.

Variable density sampling of the k-space in MRI is an integral part of trajectory design. It has been observed that data-driven trajectory design methods provide a better image reconstruction as compared to trajectories obtained from a fixed or a parametric density function. In this paper, a data-driven strategy has been proposed to obtain non-Cartesian continuous k-space sampling trajectories for MRI under the compressed sensing framework (greedy non-Cartesian (GNC)).

Letter processing in upright bigrams predicts reading fluency variations in children.

Fluent reading is an important milestone in education, but we lack a clear understanding of why children vary so widely in attaining it. Language-related factors such as rapid automatized naming (RAN) and phonological awareness have been identified as important factors that explain reading fluency. However, whether any aspects of visual orthographic processing also explain reading fluency beyond phonology is unclear.

Towards a general framework for fast and feasible k-space trajectories for MRI based on projection methods.

The design of feasible trajectories to traverse the k-space for sampling in magnetic resonance imaging (MRI) is important while considering ways to reduce the scan time. Over the recent years, non-Cartesian trajectories have been observed to result in benign artifacts and being less sensitive to motion. In this paper, we propose a generalized framework that encompasses projection-based methods to generate feasible non-Cartesian k-space trajectories.

A compositional neural code in high-level visual cortex can explain jumbled word reading.

We read jubmled wrods effortlessly, but the neural correlates of this remarkable ability remain poorly understood. We hypothesized that viewing a jumbled word activates a visual representation that is compared to known words. To test this hypothesis, we devised a purely visual model in which neurons tuned to letter shape respond to longer strings in a compositional manner by linearly summing letter responses.

Reading Increases the Compositionality of Visual Word Representations.

Reading causes widespread changes in the brain, but its effect on visual word representations is unknown. Learning to read may facilitate visual processing by forming specialized detectors for longer strings or by making word responses more predictable from single letters-that is, by increasing compositionality. We provided evidence for the latter hypothesis using experiments that compared nonoverlapping groups of readers of two Indian languages (Telugu and Malayalam).