Biology subjects

Pruessmann, K. P.

Publications and source records attributed to Pruessmann, K. P..

3 recordsLinked to original sources

bioRxiv2020

Whole-brain estimates of directed connectivity for human connectomics

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.

neuroscience↗

bioRxiv2019

Advances in Spiral fMRI: A High-resolution Study with Single-shot Acquisition

Spiral fMRI has been put forward as a viable alternative to rectilinear echo-planar imaging, in particular due to its enhanced average k-space speed and thus high acquisition efficiency. This renders spirals attractive for contemporary fMRI applications that require high spatiotemporal resolution, such as laminar or columnar fMRI. However, in practice, spiral fMRI is typically hampered by its reduced robustness and ensuing blurring artifacts, which arise from imperfections in both static and dynamic magnetic fields. Recently, these limitations have been overcome by the concerted application of an expanded signal model that accounts for such field imperfections, and its inversion by iterative image reconstruction. In the challenging ultra-high field environment of 7 Tesla, where field inhomogeneity effects are aggravated, both multi-shot and single-shot 2D spiral imaging at sub-millimeter resolution was demonstrated with high depiction quality and anatomical congruency. In this work, we further these advances towards a time series application of spiral readouts, namely, single-shot spiral BOLD fMRI at 0.8 mm in-plane resolution. We demonstrate that high-resolution spiral fMRI at 7 T is not only feasible, but delivers both excellent image quality, BOLD sensitivity, and spatial specificity of the activation maps, with little artifactual blurring. Furthermore, we show the versatility of the approach with a combined in/out spiral readout at a more typical resolution (1.5 mm), where the high acquisition efficiency allows to acquire two images per shot for improved sensitivity by echo combination. HighlightsO_LIThis work reports the first fMRI study at 7T with high-resolution spiral readout gradient waveforms. C_LIO_LIWe achieve spiral fMRI with sub-millimeter resolution (0.8 mm, FOV 230 mm), acquired in a single shot (36 slices in 3.3 s). C_LIO_LISpiral images exhibit intrinsic geometric congruency to anatomical scans, and spatially specific activation patterns. C_LIO_LIImage reconstruction rests on a signal model expanded by measured trajectories and static field maps, inverted by cg-SENSE. C_LIO_LIWe assess generalizability of the approach for spiral in/out readouts, providing two images per shot (1.5 mm resolution). C_LI

neuroscience↗

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↗