Retro-cue effect: The retro-cue is effective when and only when working memory consolidation is inadequate.

The retro-cue effect (RCE) describes the finding that participants' working memory performance is enhanced when their attention is directed to the to-be-tested position by a spatial cue during the retention interval. Here, we explore the relationship between RCE and working memory consolidation. A sequential display retro-cue paradigm is used for the present study. In Experiments 1A and 1B, longer consolidation time (CT) completely erased the standard RCE. In Experiment 2, longer CT diminished the RCE in a standard simultaneous display retro-cue paradigm. In Experiment 3, the post-cue time was used by participants to further consolidate memory traces. In Experiment 4, longer CT protected memory representations from invalid cue costs. Our results support a consolidation account of the RCE: the retro-cue is effective when and only when the working memory consolidation is inadequate. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

In vivo T1 mapping of neonatal brain tissue at 64 mT.

PURPOSE: Ultralow-field (ULF) point-of-care MRI systems allow image acquisition without interrupting medical provision, with neonatal clinical care being an important potential application. The ability to measure neonatal brain tissue T1 is a key enabling technology for subsequent structural image contrast optimization, as well as being a potential biomarker for brain development. Here we describe an optimized strategy for neonatal T1 mapping at ULF. METHODS: Examinations were performed on a 64-mT portable MRI system. A phantom validation experiment was performed, and a total of 33 in vivo exams were acquired from 28 neonates with postmenstrual age ranging from 31+4 to 49+0  weeks. Multiple inversion-recovery turbo spin-echo sequences were acquired with differing inversion and repetition times. An analysis pipeline incorporating inter-sequence motion correction generated proton density and T1 maps. Regions of interest were placed in the cerebral deep gray matter, frontal white matter, and cerebellum. Weighted linear regression was used to predict T1 as a function of postmenstrual age. RESULTS: Reduction of T1 with postmenstrual age is observed in all measured brain tissue; the change in T1 per week and 95% confidence intervals is given by dT1  = -21 ms/week [-25, -16] (cerebellum), dT1  = -14 ms/week [-18, -10] (deep gray matter), and dT1  = -35 ms/week [-45, -25] (white matter). CONCLUSION: Neonatal T1 values at ULF are shorter than those previously described at standard clinical field strengths, but longer than those of adults at ULF. T1 reduces with postmenstrual age and is therefore a candidate biomarker for perinatal brain development.

Chunking in Visual Working Memory: Are Visual Features of Real-World Objects Stored in Chunks?

Are visual features of real-world objects stored as bound units? Previous research has shown that simple visual features (e.g., colored squares or geometric shapes) can be effectively bound together when forming predictable pairs in memory tasks. Through a "memory compression" process, observers can take advantage of these features to compress them into a chunk. However, a recent study found that visual features in real-world objects are stored independently. In the present study, we explored this issue by using drawings of fruits as memory stimuli, presenting four pictures of fruit in separate test trials in which we required observers to remember eight total features (i.e., four colors and four shapes). In the congruent trials, the color of the fruit matched its natural appearance (e.g., a red apple), while in incongruent trials, the color of the fruit mismatched its natural appearance (e.g., a red banana). We paired the shape of the fruits randomly with a color (without replacement). According to chunking theory, if visual features of real-world objects are stored in a chunk, the highest memory capacity should be accompanied by the longest response time in congruent trials due to an extra decoding process required from the chunk. We did find that participants had the highest memory capacity in the congruent condition, but their response times in the congruent condition were significantly faster than in the incongruent condition. Thus, observers did not undergo a decoding process in the congruent condition, and we concluded that visual features in real-world objects are not stored in a chunk.

B-Spline Parameterized Joint Optimization of Reconstruction and K-Space Trajectories (BJORK) for Accelerated 2D MRI.

Optimizing k-space sampling trajectories is a promising yet challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method and sampling trajectories jointly concerning image reconstruction quality in a supervised learning manner. We parameterize trajectories with quadratic B-spline kernels to reduce the number of parameters and apply multi-scale optimization, which may help to avoid sub-optimal local minima. The algorithm includes an efficient non-Cartesian unrolled neural network-based reconstruction and an accurate approximation for backpropagation through the non-uniform fast Fourier transform (NUFFT) operator to accurately reconstruct and back-propagate multi-coil non-Cartesian data. Penalties on slew rate and gradient amplitude enforce hardware constraints. Sampling and reconstruction are trained jointly using large public datasets. To correct for possible eddy-current effects introduced by the curved trajectory, we use a pencil-beam trajectory mapping technique. In both simulations and in- vivo experiments, the learned trajectory demonstrates significantly improved image quality compared to previous model-based and learning-based trajectory optimization methods for 10× acceleration factors. Though trained with neural network-based reconstruction, the proposed trajectory also leads to improved image quality with compressed sensing-based reconstruction.

Joint Design of RF and Gradient Waveforms via Auto-differentiation for 3D Tailored Excitation in MRI.

This paper proposes a new method for joint design of radiofrequency (RF) and gradient waveforms in Magnetic Resonance Imaging (MRI), and applies it to the design of 3D spatially tailored saturation and inversion pulses. The joint design of both waveforms is characterized by the ODE Bloch equations, to which there is no known direct solution. Existing approaches therefore typically rely on simplified problem formulations based on, e.g., the small-tip approximation or constraining the gradient waveforms to particular shapes, and often apply only to specific objective functions for a narrow set of design goals (e.g., ignoring hardware constraints). This paper develops and exploits an auto-differentiable Bloch simulator to directly compute Jacobians of the (Bloch-simulated) excitation pattern with respect to RF and gradient waveforms. This approach is compatible with arbitrary sub-differentiable loss functions, and optimizes the RF and gradients directly without restricting the waveform shapes. For computational efficiency, we derive and implement explicit Bloch simulator Jacobians (approximately halving computation time and memory usage). To enforce hardware limits (peak RF, gradient, and slew rate), we use a change of variables that makes the 3D pulse design problem effectively unconstrained; we then optimize the resulting problem directly using the proposed auto-differentiation framework. We demonstrate our approach with two kinds of 3D excitation pulses that cannot be easily designed with conventional approaches: Outer-volume saturation (90° flip angle), and inner-volume inversion.

Spatial Ability and Theory of Mind: A Mediating Role of Visual Perspective Taking.

This research tests the role of visual perspective taking (VPT) in mediating the relation between spatial ability and theory of mind (ToM). Study 1 demonstrated such mediation correlationally in seventy 3.5- to 4-year olds. In Study 2, twenty-three 3.5- to 4-year-olds were trained on using play blocks to copy preassembled models as a way to promote spatial ability. Resultant increases in VPT and ToM were compared to those from a control group learning to draw instead (n = 23). Both studies showed that the effect of spatial ability on ToM depended on VPT, suggesting a role of embodiment in ToM development in early childhood. These findings provide an alternative way to think about ToM development and the psychological mechanism that may be involved.

A GRAPPA algorithm for arbitrary 2D/3D non-Cartesian sampling trajectories with rapid calibration.

PURPOSE: GRAPPA is a popular reconstruction method for Cartesian parallel imaging, but is not easily extended to non-Cartesian sampling. We introduce a general and practical GRAPPA algorithm for arbitrary non-Cartesian imaging. METHODS: We formulate a general GRAPPA reconstruction by associating a unique kernel with each unsampled k-space location with a distinct constellation, that is, local sampling pattern. We calibrate these generalized kernels using the Fourier transform phase shift property applied to fully gridded or separately acquired Cartesian Autocalibration signal (ACS) data. To handle the resulting large number of different kernels, we introduce a fast calibration algorithm based on nonuniform FFT (NUFFT) and adoption of circulant ACS boundary conditions. We applied our method to retrospectively under-sampled rotated stack-of-stars/spirals in vivo datasets, and to a prospectively under-sampled rotated stack-of-spirals functional MRI acquisition with a finger-tapping task. RESULTS: We reconstructed all datasets without performing any trajectory-specific manual adaptation of the method. For the retrospectively under-sampled experiments, our method achieved image quality (i.e., error and g-factor maps) comparable to conjugate gradient SENSE (cg-SENSE) and SPIRiT. Functional activation maps obtained from our method were in good agreement with those obtained using cg-SENSE, but required a shorter total reconstruction time (for the whole time-series): 3 minutes (proposed) vs 15 minutes (cg-SENSE). CONCLUSIONS: This paper introduces a general 3D non-Cartesian GRAPPA that is fast enough for practical use on today's computers. It is a direct generalization of original GRAPPA to non-Cartesian scenarios. The method should be particularly useful in dynamic imaging where a large number of frames are reconstructed from a single set of ACS data.