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Lars Kasper

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12 papers

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YNIMG Journal 2022 Journal Article

Advances in spiral fMRI: A high-resolution study with single-shot acquisition

Lars Kasper
Maria Engel
Jakob Heinzle
Matthias Mueller-Schrader
Nadine N. Graedel
Jonas Reber
Thomas Schmid
Christoph Barmet

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.

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YNIMG Journal 2021 Journal Article

A Hilbert-based method for processing respiratory timeseries

Samuel J. Harrison
Samuel Bianchi
Jakob Heinzle
Klaas Enno Stephan
Sandra Iglesias
Lars Kasper

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation is publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).

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YNIMG Journal 2021 Journal Article

Cholinergic and dopaminergic effects on prediction error and uncertainty responses during sensory associative learning

Sandra Iglesias
Lars Kasper
Samuel J. Harrison
Robert Manka
Christoph Mathys
Klaas E. Stephan

Navigating the physical world requires learning probabilistic associations between sensory events and their change in time (volatility). Bayesian accounts of this learning process rest on hierarchical prediction errors (PEs) that are weighted by estimates of uncertainty (or its inverse, precision). In a previous fMRI study we found that low-level precision-weighted PEs about visual outcomes (that update beliefs about associations) activated the putative dopaminergic midbrain; by contrast, precision-weighted PEs about cue-outcome associations (that update beliefs about volatility) activated the cholinergic basal forebrain. These findings suggested selective dopaminergic and cholinergic influences on precision-weighted PEs at different hierarchical levels. Here, we tested this hypothesis, repeating our fMRI study under pharmacological manipulations in healthy participants. Specifically, we performed two pharmacological fMRI studies with a between-subject double-blind placebo-controlled design: study 1 used antagonists of dopaminergic (amisulpride) and muscarinic (biperiden) receptors, study 2 used enhancing drugs of dopaminergic (levodopa) and cholinergic (galantamine) modulation. Pooled across all pharmacological conditions of study 1 and study 2, respectively, we found that low-level precision-weighted PEs activated the midbrain and high-level precision-weighted PEs the basal forebrain as in our previous study. However, we found pharmacological effects on brain activity associated with these computational quantities only when splitting the precision-weighted PEs into their PE and precision components: in a brainstem region putatively containing cholinergic (pedunculopontine and laterodorsal tegmental) nuclei, biperiden (compared to placebo) enhanced low-level PE responses and attenuated high-level PE activity, while amisulpride reduced high-level PE responses. Additionally, in the putative dopaminergic midbrain, galantamine compared to placebo enhanced low-level PE responses (in a body-weight dependent manner) and amisulpride enhanced high-level precision activity. Task behaviour was not affected by any of the drugs. These results do not support our hypothesis of a clear-cut dichotomy between different hierarchical inference levels and neurotransmitter systems, but suggest a more complex interaction between these neuromodulatory systems and hierarchical Bayesian quantities. However, our present results may have been affected by confounds inherent to pharmacological fMRI. We discuss these confounds and outline improved experimental tests for the future.

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YNIMG Journal 2021 Journal Article

Feasibility of spiral fMRI based on an LTI gradient model

Nadine N. Graedel
Lars Kasper
Maria Engel
Jennifer Nussbaum
Bertram J. Wilm
Klaas P. Pruessmann
S. Johanna Vannesjo

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 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.

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YNIMG Journal 2021 Journal Article

Whole-brain estimates of directed connectivity for human connectomics

Stefan Frässle
Zina M. Manjaly
Cao T. Do
Lars Kasper
Klaas P. Pruessmann
Klaas E. Stephan

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.

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YNIMG Journal 2018 Journal Article

A generative model of whole-brain effective connectivity

Stefan Frässle
Ekaterina I. Lomakina
Lars Kasper
Zina M. Manjaly
Alex Leff
Klaas P. Pruessmann
Joachim M. Buhmann
Klaas E. Stephan

The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters. This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10, 000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data – in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling – for example, for phenotyping individual patients in terms of whole-brain network structure.

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YNIMG Journal 2018 Journal Article

Rapid anatomical brain imaging using spiral acquisition and an expanded signal model

Lars Kasper
Maria Engel
Christoph Barmet
Maximilian Haeberlin
Bertram J. Wilm
Benjamin E. Dietrich
Thomas Schmid
Simon Gross

We report the deployment of spiral acquisition for high-resolution structural imaging at 7T. Long spiral readouts are rendered manageable by an expanded signal model including static off-resonance and B0 dynamics along with k-space trajectories and coil sensitivity maps. Image reconstruction is accomplished by inversion of the signal model using an extension of the iterative non-Cartesian SENSE algorithm. Spiral readouts up to 25 ms are shown to permit whole-brain 2D imaging at 0. 5 mm in-plane resolution in less than a minute. A range of options is explored, including proton-density and T2* contrast, acceleration by parallel imaging, different readout orientations, and the extraction of phase images. Results are shown to exhibit competitive image quality along with high geometric consistency.

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YNIMG Journal 2017 Journal Article

Analysis and correction of field fluctuations in fMRI data using field monitoring

Saskia Bollmann
Lars Kasper
S. Johanna Vannesjo
Andreea O. Diaconescu
Benjamin E. Dietrich
Simon Gross
Klaas E. Stephan
Klaas P. Pruessmann

This work investigates the role of magnetic field fluctuations as a confound in fMRI. In standard fMRI experiments with single-shot EPI acquisition at 3 Tesla the uniform and gradient components of the magnetic field were recorded with NMR field sensors. By principal component analysis it is found that differences of field evolution between the EPI readouts are explainable by few components relating to slow and within-shot field dynamics of hardware and physiological origin. The impact of fluctuating field components is studied by selective data correction and assessment of its influence on image fluctuation and SFNR. Physiological field fluctuations, attributed to breathing, were found to be small relative to those of hardware origin. The dominant confounds were hardware-related and attributable to magnet drift and thermal changes. In raw image time series, field fluctuation caused significant SFNR loss, reflected by a 67% gain upon correction. Large part of this correction can be accomplished by traditional image realignment, which addresses slow and spatially uniform field changes. With realignment, explicit field correction increased the SFNR on the order of 6%. In conclusion, field fluctuations are a relevant confound in fMRI and can be addressed effectively by retrospective data correction. Based on the physics involved it is anticipated that the advantage of full field correction increases with field strength, with non-Cartesian readouts, and upon phase-sensitive BOLD analysis.

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YNIMG Journal 2017 Journal Article

Physiology recording with magnetic field probes for fMRI denoising

Simon Gross
Laetitia Vionnet
Lars Kasper
Benjamin E. Dietrich
Klaas P. Pruessmann

Physiological noise originating in cardiovascular and respiratory processes is a substantial confound in BOLD fMRI. When unaccounted for it reduces the temporal SNR and causes error in inferred brain activity and connectivity. Physiology correction typically relies on auxiliary measurements with peripheral devices such as ECG, pulse oximeters, and breathing belts. These require direct skin contact or at least a tight fit, impairing subject comfort and adding to the setup time. In this work, we explore a touch-free alternative for physiology recording, using magnetic detection with NMR field probes. Placed close to the chest such probes offer high sensitivity to cardiovascular and respiratory dynamics without mechanical contact. This is demonstrated by physiology regression in a typical fMRI scenario at 7T, including validation against standard devices. The study confirms essentially equivalent performance of noise models based on conventional recordings and on field probes. It is shown that the field probes may be positioned in the subject's back such that they could be readily integrated in the patient table.

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YNIMG Journal 2014 Journal Article

Matched-filter acquisition for BOLD fMRI

Lars Kasper
Maximilian Haeberlin
Benjamin E. Dietrich
Simon Gross
Christoph Barmet
Bertram J. Wilm
S. Johanna Vannesjo
David O. Brunner

We introduce matched-filter fMRI, which improves BOLD (blood oxygen level dependent) sensitivity by variable-density image acquisition tailored to subsequent image smoothing. Image smoothing is an established post-processing technique used in the vast majority of fMRI studies. Here we show that the signal-to-noise ratio of the resulting smoothed data can be substantially increased by acquisition weighting with a weighting function that matches the k-space filter imposed by the smoothing operation. We derive the theoretical SNR advantage of this strategy and propose a practical implementation of 2D echo-planar acquisition matched to common Gaussian smoothing. To reliably perform the involved variable-speed trajectories, concurrent magnetic field monitoring with NMR probes is used. Using this technique, phantom and in vivo measurements confirm reliable SNR improvement in the order of 30% in a “resting-state” condition and prove robust in different regimes of physiological noise. Furthermore, a preliminary task-based visual fMRI experiment equally suggests a consistent BOLD sensitivity increase in terms of statistical sensitivity (average t-value increase of about 35%). In summary, our study suggests that matched-filter acquisition is an effective means of improving BOLD SNR in studies that rely on image smoothing at the post-processing level.

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YNIMG Journal 2009 Journal Article

MRI of cellular layers in mouse brain in vivo

Susann Boretius
Lars Kasper
Roland Tammer
Thomas Michaelis
Jens Frahm

Noninvasive imaging of the brain of animal models demands the detection of increasingly smaller structures by in vivo MRI. The purpose of this work was to elucidate the spatial resolution and structural contrast that can be obtained for studying the brain of C57BL/6J mice by optimized T2-weighted fast spin-echo MRI at 9. 4 T. As a prerequisite for high-resolution imaging in vivo, motion artifacts were abolished by combining volatile anesthetics and positive pressure ventilation with a specially designed animal bed for fixation. Multiple substructures in the cortex, olfactory bulb, hippocampus, and cerebellum were resolved at 30 to 40 μm in-plane resolution and 200 to 300 μm section thickness as well as for relatively long echo times of 65 to 82 ms. In particular, the approach resulted in the differentiation of up to five cortical layers. In the olfactory bulb the images unraveled the mitral cell layer which has a thickness of mostly single cells. In the hippocampus at least five substructures could be separated. The molecular layer, Purkinje layer, and granular layer of the cerebellum could be clearly differentiated from the white matter. In conclusion, even without the use of a contrast agent, suitable adjustments of a widely available T2-weighted MRI sequence at high field allow for structural MRI of living mice at near single-cell layer resolution.

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YNIMG Journal 2008 Journal Article

Nonlinear dynamic causal models for fMRI

Klaas Enno Stephan
Lars Kasper
Lee M. Harrison
Jean Daunizeau
Hanneke E.M. den Ouden
Michael Breakspear
Karl J. Friston

Models of effective connectivity characterize the influence that neuronal populations exert over each other. Additionally, some approaches, for example Dynamic Causal Modelling (DCM) and variants of Structural Equation Modelling, describe how effective connectivity is modulated by experimental manipulations. Mathematically, both are based on bilinear equations, where the bilinear term models the effect of experimental manipulations on neuronal interactions. The bilinear framework, however, precludes an important aspect of neuronal interactions that has been established with invasive electrophysiological recording studies; i. e. , how the connection between two neuronal units is enabled or gated by activity in other units. These gating processes are critical for controlling the gain of neuronal populations and are mediated through interactions between synaptic inputs (e. g. by means of voltage-sensitive ion channels). They represent a key mechanism for various neurobiological processes, including top-down (e. g. attentional) modulation, learning and neuromodulation. This paper presents a nonlinear extension of DCM that models such processes (to second order) at the neuronal population level. In this way, the modulation of network interactions can be assigned to an explicit neuronal population. We present simulations and empirical results that demonstrate the validity and usefulness of this model. Analyses of synthetic data showed that nonlinear and bilinear mechanisms can be distinguished by our extended DCM. When applying the model to empirical fMRI data from a blocked attention to motion paradigm, we found that attention-induced increases in V5 responses could be best explained as a gating of the V1→V5 connection by activity in posterior parietal cortex. Furthermore, we analysed fMRI data from an event-related binocular rivalry paradigm and found that interactions amongst percept-selective visual areas were modulated by activity in the middle frontal gyrus. In both practical examples, Bayesian model selection favoured the nonlinear models over corresponding bilinear ones.

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