Biology subjects

Schlumm, T.

Publications and source records attributed to Schlumm, T..

3 recordsLinked to original sources

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↗

bioRxiv2022

Multi-Echo Investigations of Positive and Negative CBF and Concomitant BOLD Changes

Unlike the positive blood oxygenation level-dependent (BOLD) response (PBR), commonly taken as an indication of an activated brain region, the physiological origin of negative BOLD signal changes (i.e. a negative BOLD response, NBR), also referred to as deactivation is still being debated. In this work, an attempt was made to gain a better understanding of the underlying mechanism by obtaining a comprehensive measure of the contributing cerebral blood flow (CBF) and its relationship to the NBR in the human visual cortex, in comparison to a simultaneously induced PBR in surrounding visual regions. To overcome the low signal-to-noise ratio (SNR) of CBF measurements, a newly developed multi-echo version of a center-out echo planar-imaging (EPI) readout was employed with pseudo-continuous arterial spin labeling (pCASL). It achieved very short echo and inter-echo times and facilitated a simultaneous detection of functional CBF and BOLD changes at 3 T with improved sensitivity. Evaluations of the absolute and relative changes of CBF and the effective transverse relaxation rate, [Formula], the coupling ratios, and their dependence on CBF at rest, CBFrest, indicated differences between activated and deactivated regions. Analysis of the shape of the respective functional responses also revealed faster negative responses with more pronounced post-stimulus transients. Resulting differences in the flow-metabolism coupling ratios were further examined for potential distinctions in the underlying neuronal contributions. HighlightsO_LIIntroduction of multi-echo center-out EPI for investigating concomitant CBF and BOLD changes in regions of positive (PBR) and negative BOLD response (NBR). C_LIO_LI{Delta}CBF timecourses closely follow those of [Formula] with negative signals exhibiting faster responses and more pronounced post-stimulus transients. C_LIO_LIDecreases in CBF appear to warrant a larger change in NBR than CBF increases in PBR regions. C_LIO_LIConsideration of baseline CBF values is important in comparisons of relative coupling ratios ({delta}sBOLD/{delta}cbf) between brain regions. C_LIO_LIDiscussion of potential excitatory and inhibitory neuronal feed forward control of CBF and CMRO2 in PBR and NBR. C_LI

neuroscience↗

bioRxiv2020

Increased Sensitivity and Signal-to-Noise Ratio in Diffusion-Weighted MRI using Multi-Echo Acquisitions

Post-mortem diffusion MRI (dMRI) enables acquisitions of structural imaging data with otherwise unreachable resolutions - at the expense of longer scanning times. These data are typically acquired using highly segmented image acquisition strategies, thereby resulting in an incomplete signal decay before the MRI encoding continues. Especially in dMRI, with low signal intensities and lengthy contrast encoding, such temporal inefficiency translates into reduced image quality and longer scanning times. This study introduces Multi Echo (ME) acquisitions to dMRI on a human MRI system - a time-efficient approach, which increases SNR (Signal-to-Noise Ratio) and reduces noise bias for dMRI images. The benefit of the introduced ME dMRI method was validated using numerical Monte Carlo simulations and showcased on a post-mortem brain of a wild chimpanzee. The proposed Maximum Likelihood Estimation echo combination results in an optimal SNR without detectable signal bias. The combined strategy comes at a small price in scanning time (here 30% additional) and leads to a substantial SNR increase (here up to 1.9x which is equivalent to 3.6 averages) and a general reduction of the noise bias.

neuroscience↗