Lars Kasper

Last Name

Kasper

First Name

Lars

ORCID

0000-0001-7667-603X

Show all metadata

Search Results

Publications1 - 10 of 15

A Hilbert-based method for processing respiratory timeseries
Harrison, Samuel J.; Bianchi, Samuel; Heinzle, Jakob; et al. (2021)
NeuroImage
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).
Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging
Renton, Angela I.; Dao, Thuy T.; Johnstone, Tom; et al. (2024)
Nature Methods
Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform (https://www.neurodesk.org/) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.
Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN
Valevicius, Darius; Beck, Natacha; Kasper, Lars; et al. (2023)
Frontiers in Neuroinformatics
Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN’s unique features and infrastructure were leveraged to integrate TAPAS PhysIO, an open-source MATLAB toolbox for physiological noise modeling in fMRI data. This case study highlights three key elements of CBRAIN’s infrastructure that enable streamlined, multimodal tool integration: a user-friendly GUI, a Brain Imaging Data Structure (BIDS) data-entry schema, and convenient in-browser visualization of results. By incorporating PhysIO into CBRAIN, we achieved significant improvements in the speed, ease of use, and scalability of physiological preprocessing. Researchers now have access to a uniform and intuitive interface for analyzing data, which facilitates remote and collaborative evaluation of results. With these improvements, CBRAIN aims to become an essential open-science tool for integrative neuroimaging research, supporting FAIR principles and enabling efficient workflows for complex analysis pipelines.
The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data
Kasper, Lars; Bollmann, Steffen; Diaconescu, Andreea O.; et al. (2017)
Journal of Neuroscience Methods
Background Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies. New methods We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps − from flexible read-in of data formats to GLM regressor/contrast creation − without any manual intervention. Results We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N = 35). Comparison with existing methods The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data. Conclusions Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.
Feasibility of spiral fMRI based on an LTI gradient model
Graedel, Nadine N.; Kasper, Lars; Engel, Maria; et al. (2020)
Hierarchical prediction errors in midbrain and septum during social learning
Diaconescu, Andreea O.; Mathys, Christoph; Weber, Lilian A.E.; et al. (2017)
Social Cognitive and Affective Neuroscience
Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling and genetics to address this question in two separate samples (N = 35, N = 47). Participants played a game requiring inference on an adviser’s intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser’s trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support ‘theory of mind’, but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs (‘expected uncertainty’) about the adviser’s fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others.
Neural arbitration between social and individual learning systems
Diaconescu, Andreea Oliviana; Stecy, Madeline; Kasper, Lars; et al. (2020)
eLife
Decision making requires integrating knowledge gathered from personal experiences with advice from others. The neural underpinnings of the process of arbitrating between information sources has not been fully elucidated. In this study, we formalized arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modeling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our definition of arbitration as a ratio of precisions. Functional neuroimaging demonstrated that arbitration signals were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favor of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favor of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioral and neural levels.
Matched Filter Anatomical Brain Imaging
Kasper, Lars; Häberlin, Maximilian; Wilm, Bertram J.; et al. (2012)
Magnetic Resonance Materials in Physics, Biology, and Medicine
Mono-planar T-Hex: Speed and flexibility for high-resolution 3D imaging
Engel, Maria; Kasper, Lars; Wilm, Bertram; et al. (2021)
Magnetic Resonance in Medicine
Purpose The aim of this work is the reconciliation of high spatial and temporal resolution for MRI. For this purpose, a novel sampling strategy for 3D encoding is proposed, which provides flexible k-space segmentation along with uniform sampling density and benign filtering effects related to signal decay. Methods For time-critical MRI applications such as functional MRI (fMRI), 3D k-space is usually sampled by stacking together 2D trajectories such as echo planar imaging (EPI) or spiral readouts, where each shot covers one k-space plane. For very high temporal and medium to low spatial resolution, tilted hexagonal sampling (T-Hex) was recently proposed, which allows the acquisition of a larger k-space volume per excitation than can be covered with a planar readout. Here, T-Hex is described in a modified version where it instead acquires a smaller k-space volume per shot for use with medium temporal and high spatial resolution. Results Mono-planar T-Hex sampling provides flexibility in the choice of speed, signal-to-noise ratio (SNR), and contrast for rapid MRI acquisitions. For use with a conventional gradient system, it offers the greatest benefit in a regime of high in-plane resolution <1 mm. The sampling scheme is combined with spirals for high sampling speed as well as with more conventional EPI trajectories. Conclusion Mono-planar T-Hex sampling combines fast 3D encoding with SNR efficiency and favorable depiction characteristics regarding noise amplification and filtering effects from urn:x-wiley:07403194:media:mrm28979:mrm28979-math-0003 decay, thereby providing flexibility in the choice of imaging parameters. It is attractive both for high-resolution time series such as fMRI and for applications that require rapid anatomical imaging.
A method for correcting breathing-induced field fluctuations in T2*-weighted spinal cord imaging using a respiratory trace
Vannesjo, S. Johanna; Clare, Stuart; Kasper, Lars; et al. (2019)
Magnetic Resonance in Medicine
Purpose Spinal cord MRI at ultrahigh field is hampered by time‐varying magnetic fields associated with the breathing cycle, giving rise to ghosting artifacts in multi‐shot acquisitions. Here, we suggest a correction approach based on linking the signal from a respiratory bellows to field changes inside the spinal cord. The information is used to correct the data at the image reconstruction level. Methods The correction was demonstrated in the context of multi‐shot T2*‐weighted imaging of the cervical spinal cord at 7T. A respiratory trace was acquired during a high‐resolution multi‐echo gradient‐echo sequence, used for structural imaging and quantitative T2* mapping, and a multi‐shot EPI time series, as would be suitable for fMRI. The coupling between the trace and the breathing‐induced fields was determined by a short calibration scan in each individual. Images were reconstructed with and without trace‐based correction. Results In the multi‐echo acquisition, breathing‐induced fields caused severe ghosting in images with long TE, which led to a systematic underestimation of T2* in the spinal cord. The trace‐based correction reduced the ghosting and increased the estimated T2* values. Breathing‐related ghosting was also observed in the multi‐shot EPI images. The correction largely removed the ghosting, thereby improving the temporal signal‐to‐noise ratio of the time series. Conclusions Trace‐based retrospective correction of breathing‐induced field variations can reduce ghosting and improve quantitative metrics in multi‐shot structural and functional T2*‐weighted imaging of the spinal cord. The method is straightforward to implement and does not rely on sequence modifications or additional hardware beyond a respiratory bellows.

Publications1 - 10 of 15

Lars Kasper

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results