Biology subjects

Moeller, H. E.

Publications and source records attributed to Moeller, H. E..

4 recordsLinked to original sources

bioRxiv2025

Observations of anisotropic paramagnetic and diamagnetic susceptibility in the primate brain

Bulk magnetic susceptibility in the brain white matter is known to be diamagnetic and anisotropic due to the ordered myelin lipids. While paramagnetic iron is widely present in the brain, it is typically not considered to contribute to anisotropy. Using experimental MRI and computational techniques, this study explores the competing contribution of diamagnetic and paramagnetic substances to the susceptibility anisotropy. Multi-echo gradient-echo imaging and diffusion-weighted imaging data from a paraformaldehyde-fixed post-mortem chimpanzee (Pan troglodytes verus) brain was analyzed. A computational method, DECOMPOSE-QSM, was used to separate paramagnetic susceptibility and diamagnetic susceptibility components. As expected, diamagnetic components showed significant anisotropy; unexpectedly, paramagnetic components also exhibited strong anisotropy in deep gray matter and parts of white matter. This may arise from the geometric arrangement of iron-rich cellular compartments, such as oligodendrocytes, astrocytes, and microglia, along nerve fibers. This method enables further exploration of tissue-specific contributions to susceptibility anisotropy.

neuroscience↗

bioRxiv2025

Ultra-high-field fMRI reveals layer-specific responses in the human spinal cord

Developments in functional magnetic resonance imaging (fMRI) at ultra-high field (UHF) now allow for insights into human brain function at mesoscopic scale. However, similar progress has not been achieved for the human spinal cord, despite its importance for reciprocal brain-body communication. Here, we therefore integrate substantial methodological improvements that enable previously unattainable high-resolution UHF spinal fMRI. Using a heat-pain paradigm in healthy volunteers, we investigate fMRI responses in the dorsal horn: we characterize their spatial pattern, establish their robustness and demonstrate that they can be reliably observed even for individual participants. Most importantly, we uncover two physiologically relevant spinal response components - phasic and tonic heat responses - that map differentially onto superficial and deep layers of the dorsal horn. Taken together, our novel UHF fMRI approach allows for differentiating responses across fundamental spinal processing units and will enable insights into human spinal cord function at mesoscopic scale in health and disease.

neuroscience↗

bioRxiv2022

High Angular Resolution Susceptibility Imaging and Estimation of Fiber Orientation Distribution Functions in Primate Brain

Uncovering brain-tissue microstructure including axonal characteristics is a major neuroimaging research focus. Within this scope, anisotropic properties of magnetic susceptibility in white matter have been successfully employed to estimate primary axonal trajectories using mono-tensorial models. However, anisotropic susceptibility has not yet been considered for modeling more complex fiber structures within a voxel, such as intersecting bundles, or an estimation of orientation distribution functions (ODFs). This information is routinely obtained by high angular resolution diffusion imaging (HARDI) techniques. In applications to fixed tissue, however, diffusion-weighted imaging suffers from an inherently low signal-to-noise ratio and limited spatial resolution, leading to high demands on the performance of the gradient system in order to mitigate these limitations. In the current work, high angular resolution susceptibility imaging (HARSI) is proposed as a novel, phase-based methodology to estimate ODFs. A multiple gradient-echo dataset was acquired in an entire fixed chimpanzee brain at 61 orientations by reorienting the specimen in the magnetic field. The constant solid angle method was adapted for estimating phase-based ODFs. HARDI data were also acquired for comparison. HARSI yielded information on whole-brain fiber architecture, including identification of peaks of multiple bundles that resembled features of the HARDI results. Distinct differences between both methods suggest that susceptibility properties may offer complementary microstructural information. These proof-of-concept results indicate a potential to study the axonal organization in post-mortem primate and human brain at high resolution. HighlightsO_LIIntroduction of High Angular Resolution Susceptibility Imaging (HARSI) for advancing Quantitative Susceptibility Mapping (QSM). C_LIO_LIHARSI-derived fiber orientation distributions in fixed chimpanzee brain. C_LIO_LIHARSI-based visualization of complex fiber configurations. C_LIO_LIComparisons between HARSI and High Angular Resolution Diffusion Imaging. C_LIO_LIPotential for high-resolution post-mortem imaging of fiber architecture. C_LI

neuroscience↗

bioRxiv2019

PyRates - A Python Framework for Rate-Based Neural Simulations

In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of neural models as a graph. PyRates provides intuitive access to and modification of all mathematical operators in a graph, thus allowing for a highly generic model definition. For computational efficiency and parallelization, the model graph is translated into a tensorflow-based compute graph. Using the example of two different neural models belonging to the family of rate-based population models, we explain the mathematical formalism, software structure and user interfaces of PyRates. We then show via numerical simulations that the behavior shown by the model implementations in PyRates is consistent with the literature. Finally, we demonstrate the computational capacities and scalability of PyRates via a number of benchmark simulations of neural networks differing in size and connectivity.

neuroscience↗