Biology subjects

Graedel, N. N.

Publications and source records attributed to Graedel, N. N..

3 recordsLinked to original sources

bioRxiv2024

Communication of perceptual predictions from the hippocampus to the deep layers of the parahippocampal cortex

Current evidence points to the hippocampus as an essential region coordinating learning and exploiting predictive relationships in the service of perception. However, it remains unclear whether the hippocampus drives the communication of predictions to the sensory cortex or acts as a recipient of predictions from else-where. Here, we collected sub-millimetre 7T fMRI data to investigate neural signals in the medial temporal lobe (MTL). We used layer-specific fMRI to infer the direction of communication between the hippocam-pus and cortex. Specifically, superficial layers of the MTL cortex project to the hippocampus, while deep MTL layers receive feedback projections. Participants performed a task in which auditory cues predicted abstract shapes. Crucially, we omitted the expected shape on 25% of trials, thus isolating the prediction signal from bottom-up input and allowing us to ask: In which direction are predictions communicated between the hippocampus and neocortex? Neural patterns in CA23, pre/parasubiculum and the parahippocampal cortex (PHC) reflected shape-specific predictions. Layer-specific informational connectivity analyses revealed that communication between CA23 and PHC was specific to the deep layers of PHC. These findings are in line with the hippocampus generating predictions through pattern completion in CA23 and feeding these pre-dictions back to the neocortex.

neuroscience↗

bioRxiv2020

Subspace-constrained approaches to low-rank fMRI acceleration

Acceleration methods in fMRI aim to reconstruct high fidelity images from undersampled k-space, allowing fMRI datasets to achieve higher temporal resolution, reduced physiological noise aliasing, and increased statistical degrees of freedom. While low levels of acceleration are typically part of standard fMRI protocols through parallel imaging, there exists the potential for approaches that allow much greater acceleration. One such existing approach is k-t FASTER, which exploits the inherent low-rank nature of fMRI. In this paper, we present a reformulated version of k-t FASTER which includes additional L2 constraints within a low-rank framework. We evaluated the effect of three different constraints against existing low-rank approaches to fMRI reconstruction: Tikhonov constraints, low-resolution priors, and temporal subspace smoothness. The different approaches are separately tested for robustness to undersampling and thermal noise levels, in both retrospectively and prospectively-undersampled finger-tapping task fMRI data. Reconstruction quality is evaluated by accurate reconstruction of low-rank subspaces and activation maps. The use of L2 constraints were found to achieve consistently improved results, producing high fidelity reconstructions of statistical parameter maps at higher acceleration factors and lower SNR values than existing methods, but at a cost of longer computation time. In particular, the Tikhonov constraint proved very robust across all tested datasets, and the temporal subspace smoothness constraint provided the best reconstruction scores in the prospectively-undersampled dataset. These results demonstrate that regularized low-rank reconstruction of fMRI data can recover functional information at high acceleration factors without the use of any model-based spatial constraints. Highlights O_LIWe introduce an alternate implementation of low-rank fMRI reconstruction by using alternating minimization, which allows for easy integration of the subspace-specific L2 constraints C_LIO_LIWe use the alternating minimization approach to accelerate FMRI by exploiting coil sensitivity, low-rank structures, and additional L2 constraints C_LIO_LIWe found Tikhonov and Temporal Subspace Smoothness constraints show improved performance over other methods for R=15-30 C_LIO_LITikhonov Constraints were the most robust of the constrained-subspace methods, with the shortest reconstruction time C_LIO_LITemporal Subspace Smoothness produced the highest reconstruction scores in the prospectively under-sampled data C_LI

bioinformatics↗

bioRxiv2019

Feasibility of spiral fMRI based on an LTI gradient model

Spiral imaging is very well suited for functional MRI, however its use has been limited by the fact that artifacts caused by gradient imperfections and B0 inhomogeneity are more difficult to correct compared to EPI. Effective correction requires accurate knowledge of the traversed k-space trajectory. With the goal of making spiral fMRI more accessible, we have evaluated image reconstruction using trajectories predicted by the gradient impulse response function (GIRF), which can be determined in a one-time calibration step. GIRF-predicted reconstruction was tested for high-resolution (0.8 mm) fMRI at 7T. Image quality and functional results of the reconstructions using GIRF-prediction were compared to reconstructions using the delay-corrected nominal trajectory and concurrent field monitoring. The reconstructions using nominal spiral trajectories contain substantial artifacts and the activation maps contain misplaced activation. Image artifacts are substantially reduced when using the GIRF-predicted reconstruction, and the activation maps for the GIRF-predicted and monitored reconstructions largely overlap. The GIRF reconstruction provides a large increase in the spatial specificity of the activation compared to the nominal reconstruction. The GIRF-reconstruction generates image quality and fMRI results similar to using a concurrently monitored trajectory. The presented approach does not prolong or complicate the fMRI acquisition. Using GIRF-predicted trajectories has the potential to enable high-quality spiral fMRI in situations where concurrent trajectory monitoring is not available. HighlightsO_LIThis work investigates the feasibility of using a one-time system calibration to account for k-space trajectory deviations in spiral fMRI. C_LIO_LIThis versatile calibration is based on a linear time-invariant gradient model, the gradient impulse response function (GIRF). C_LIO_LIWe show that the image quality and the spatial specificity of the fMRI activation are substantially improved when using the GIRF-predicted trajectories. C_LIO_LIBasing reconstructions on nominal gradient inputs, on the other hand, induces image artifacts and misplaced fMRI activation. C_LIO_LIWe demonstrate that system characterization via the GIRF can enable spiral fMRI in situations where concurrent trajectory monitoring is unavailable. C_LI

neuroscience↗