Biology subjects

Schweser, F.

Publications and source records attributed to Schweser, F..

5 recordsLinked to original sources

bioRxiv2026

Sport-Related Concussion in Adolescent Athletes is Associated with Acute White Matter Alterations, Delayed Network Changes, and Individual-Level Injury Patterns

Sport-related concussions (SRC) are heterogenous injuries that produce a variety of symptoms and recovery trajectories. This heterogenous nature and focus on group-level analyses in current literature may obscure individual level results that could better inform clinical SRC management. In a prospective case-control study, we used diffusion magnetic resonance imaging (dMRI) to quantify longitudinal, whole-brain microstructural white matter changes reflecting axonal injury and inflammation and structural network-level alterations following SRC in adolescent athletes. Differential tractography assessed individual white matter track changes from acute injury to clinical recovery on an individual level. Acutely after SRC, but not after recovery, concussed adolescents demonstrated increased (i) quantitative anisotropy, (ii) restricted diffusion imaging, and (iii) isotropy, indicating increased microstructural disruptions early after injury. At the network level, differences were seen not acutely but after clinical recovery: whole brain network structure was more similar with reduced capacity for information spread among the concussed adolescents compared with controls. At the individual level, consistent patterns of damaged white matter tracks persisted in the concussed males but not in the concussed females. These results indicate that adolescent athlete brains are impacted acutely at the microstructural level following SRC, but that macroscale network disruptions appear after microstructure damage resolution, and they can persist beyond clinical recovery. Sex differences in the brains microstructural response to SRC, highlight the need for future research to include individualized and sex-stratified analyses to guide targeted SRC management.

neuroscience↗

bioRxiv2021

Differential patterns of change in brain connectivity resulting from traumatic brain injury

BackgroundTraumatic brain injury (TBI) damages white matter tracts, disrupting brain network structure and communication. There exists a wide heterogeneity in the pattern of structural damage associated with injury, as well as a large heterogeneity in behavioral outcomes. However, little is known about the relationship between changes in network connectivity and clinical outcomes. MethodsWe utilize the rat lateral fluid-percussion injury (FPI) model of severe TBI to study differences in brain connectivity in 8 animals that received the insult and 11 animals that received only a craniectomy. Diffusion Tensor Imaging (DTI) is performed 5 weeks after the injury and network theory is used to investigate changes in white matter connectivity. ResultsWe find that 1) global network measures are not able to distinguish between healthy and injured animals; 2) injury induced alterations predominantly exist in a subset of connections (subnetworks) distributed throughout the brain; and 3) injured animals can be divided into subgroups based on changes in network motifs - measures of local structural connectivity. Additionally, alterations in predicted functional connectivity indicate that the subgroups have different propensities to synchronize brain activity, which could relate to the heterogeneity of clinical outcomes. DiscussionThese results suggest that network measures can be used to quantify progressive changes in brain connectivity due to injury and differentiate among subpopulations with similar injuries but different pathological trajectories. Impact StatementWhite matter tracts are important for efficient communication between brain regions and their connectivity pattern underlies proper brain function. Traumatic brain injury (TBI) damages white matter tracts and changes brain connectivity, but how specific changes relate to differences in clinical/behavioral outcomes is not known. Using network theory to study injury related changes in structural connectivity, we find that local measures of network structure can identify subgroups of injured rats with different types of changes in brain structure. Our results suggest that these different patterns of change could relate to differences in clinical outcomes.

neuroscience↗

bioRxiv2020

QSM Reconstruction Challenge 2.0: Design and Report of Results

PurposeThe aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data. MethodsA two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realistic in silico head phantoms using an MR simulator. At the first stage, participants optimized QSM reconstructions without ground-truths available to mimic the clinical setting. At the second stage, ground-truths were provided for parameter optimization.Submissions were evaluated using eight numerical metrics and visual ratings. ResultsA total of 98 reconstructions were submitted for stage 1 and 47 submissions for stage 2. Iterative methods had the best quantitative metric scores, followed by deep-learning and direct inversion methods. Priors derived from magnitude data improved the metric scores. Algorithms based on iterative approaches and Total Variation (and its derivatives) produced the best overall results. The reported results and analysis pipelines have been made public to allow researchers to compare new methods to the current state of the art. ConclusionThe synthetic data provides a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithmsproduced the best results along all metrics. Future QSM challenges should asses if this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.

neuroscience↗

bioRxiv2020

QSM Reconstruction Challenge 2.0 Part 1: A Realistic in silico Head Phantom for MRI data simulation and evaluation of susceptibility mapping procedures

PurposeTo create a realistic in-silico head phantom for the second Quantitative Susceptibility Mapping (QSM) Reconstruction Challenge and for future evaluations of processing algorithms for QSM. MethodsWe created a digital whole-head tissue property phantom by segmenting and post-processing high-resolution (0.64mm isotropic), multi-parametric MRI data acquired at 7T from a healthy volunteer. We simulated the steady-state magnetization at 7T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies, as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: repetition time and echo time, voxel size, background fields, and RF phase biases. Diffusion weighted imaging phantom data is provided allowing future investigations of tissue microstructure effects in phase and QSM algorithms. ResultsThe brain-part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantoms properties, including the possibility of generating phase data with non-linear evolution over echo time due to partial volume effects or complex distributions of frequency shifts within the voxel. ConclusionThe presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.

neuroscience↗

bioRxiv2018

Two-dimensional local Fourier image reconstruction via domain decomposition Fourier continuation method

The MRI image is obtained in the spatial domain from the given Fourier coefficients in the frequency domain. It is costly to obtain the high resolution image because it requires higher frequency Fourier data while the lower frequency Fourier data is less costly and effective if the image is smooth. However, the Gibbs ringing, if existent, prevails with the lower frequency Fourier data. We propose an efficient and accurate local reconstruction method with the lower frequency Fourier data that yields sharp image profile near the local edge. The proposed method utilizes only the small number of image data in the local area. Thus the method is efficient. Furthermore the method is accurate because it minimizes the global effects on the reconstruction near the weak edges shown in many other global methods for which all the image data is used for the reconstruction. To utilize the Fourier method locally based on the local non-periodic data, the proposed method is based on the Fourier continuation method. This work is an extension of our previous 1D Fourier domain decomposition method to 2D Fourier data. The proposed method first divides the MRI image in the spatial domain into many subdomains and applies the Fourier continuation method for the smooth periodic extension of the subdomain of interest. Then the proposed method reconstructs the local image based on L2 minimization regularized by the L1 norm of edge sparsity to sharpen the image near edges. Our numerical results suggest that the proposed method should be utilized in dimension-by-dimension manner instead of in a global manner for both the quality of the reconstruction and computational efficiency. The numerical results show that the proposed method is effective when the local reconstruction is sought and that the solution is free of Gibbs oscillations.

neuroscience↗