Biology subjects

Butman, J. A.

Publications and source records attributed to Butman, J. A..

4 recordsLinked to original sources

bioRxiv2025

Characterization of mechanical tissue properties in post-mortem human brain using magnetic resonance elastography

Traumatic brain injury (TBI) is a serious health condition that can cause neurological dysfunction to varying degrees depending on the nature of the mechanical insult. In biomechanical studies of TBI under high loading conditions, post-mortem human subjects (PMHS) are often used since ethical concerns prohibit such experiments in living human subjects. Because PMHS brains undergo significant changes following death, it is important to understand the relationship between the mechanical properties of PMHS brain tissue and living tissue. In this study, we performed magnetic resonance elastography (MRE) on three PMHS specimens to estimate the material properties of the cadaveric brain, namely the storage modulus and the loss modulus, as well as the resulting shear stiffness and damping ratio. We also performed longitudinal MRE scans on one of the PMHS over the span of two months to investigate the evolution of tissue properties with post-mortem degradation. In comparison to in vivo subjects of age range 70-75 years, a substantially higher stiffness (mean: 5.96kPa) and lower damping ratio (mean: 0.09) were found in PMHS models. This study also revealed an initial increase in shear stiffness up to the seventh day post-mortem, followed by a steady decrease by the fifty-eighth day. However, the damping ratio displayed an opposite trend to that of shear stiffness. These changes were heterogeneous across brain regions. The collected measurements and analysis elucidate the changes in mechanical properties in post-mortem subjects, and can be used to build and validate computational models of TBI.

neuroscience↗

bioRxiv2021

Validation of Data Acquisition and Phase Estimation for Quantitative Susceptibility Mapping with a Rotating-Tube Phantom

PurposeQuantitative Susceptibility Mapping (QSM) is an MRI tool with the potential to reveal pathological changes from magnetic susceptibility measurements. Before phase data can be used to recover susceptibility ({Delta}{chi}), the QSM process begins with two steps: data acquisition and phase estimation. We assess the performance of these steps, when applied without user intervention, on several variations of a phantom imaging task. ApproachWe used a rotating-tube phantom with five tubes ranging from {Delta}{chi}=0.05 ppm to {Delta}{chi}=0.336 ppm. MRI data was acquired at nine angles of rotation for four different pulse sequences. The images were processed by 10 phase estimation algorithms including Laplacian, region-growing, branch-cut, temporal unwrapping and maximum-likelihood methods. We analyzed errors between measured and expected phase using the probability mass function and Cumulative Distribution Function. ResultsRepeatable acquisition and estimation methods were identified based on the probability of relative phase errors. For single-echo GRE and segmented EPI sequences, a region-growing method was most reliable with Pr(relative error<0.1)=0.95 and 0.90 respectively. For multi-echo sequences, a Maximum-Likelihood method was most reliable with Pr(relative error<0.1)=0.97. The most repeatable multi-echo methods outperformed the most repeatable single-echo methods. ConclusionsWe found a wide range of repeatability and reproducibility for off-the-shelf MRI acquisition and phase estimation approaches. The error was dominated in many cases by spatially discontinuous phase unwrapping errors. Any post-processing applied on erroneous phase estimates, such as QSMs background field removal and dipole inversion, would suffer from error propagation. Our paradigm identifies methods that yield consistent and accurate phase estimates that would ultimately yield consistent and accurate {Delta}{chi} estimates.

bioengineering↗

bioRxiv2019

A framework for spatial normalization and voxelwise analysis of diffusion propagators in multiple MAP-MRI data sets

We describe a pipeline for constructing a study-specific template of diffusion propagators measured with mean apparent propagator (MAP) MRI that supports direct voxelwise analysis of differences between propagators across multiple data sets. The pipeline leverages the fact that MAP-MRI is a generalization of diffusion tensor imaging (DTI) and combines simple and robust processing steps from existing tensor-based image registration methods. First, we compute a DTI study template which provides the reference frame and scaling parameters needed to construct a standardized set of MAP-MRI basis functions at each voxel in template space. Next, we transform each subjects diffusion data, including diffusion weighted images (DWIs) and gradient directions, from native to template space using the corresponding tensor-based deformation fields. Finally, we fit MAP coefficients in template space to the transformed DWIs of each subject using the standardized template of MAP basis functions. The consistency of MAP basis functions across all data sets in template space allows us to: 1. compute a template of propagators by directly averaging MAP coefficients and 2. quantify voxelwise differences between co-registered propagators using the angular dissimilarity, or a probability distance metric, such as the Jensen-Shannon Divergence. We illustrate the application of this method by generating a template of MAP propagators for a cohort of healthy volunteers and show a proof-of-principle example of how this pipeline may be used to detect subtle differences between propagators in a single-subject longitudinal clinical data set. The ability to standardize and analyze multiple clinical MAP-MRI data sets could improve assessments in cross-sectional and single-subject longitudinal clinical studies seeking to detect subtle microstructural changes, such as those occurring in mild traumatic brain injury (mTBI), or during the early stages of neurodegenerative diseases, or cancer.

neuroscience↗

bioRxiv2018

In vivo epigenetic editing of sema6a promoter reverses impaired transcallosal connectivity caused by C11orf46/ARL14EP neurodevelopmental risk gene

Many neuropsychiatric risk genes contribute to epigenetic regulation of gene expression but very little is known about specific chromatin-associated mechanisms governing the formation and maintenance of neuronal connectivity. Here we show that transcallosal connectivity is critically dependent on C11orf46 (also known as ARL14EP), a small nuclear protein encoded in the chromosome 11p13 Wilms Tumor, Aniridia, Genitourinary Abnormalities, intellectual disability (formerly referred to as Mental Retardation) (WAGR) risk locus. C11orf46 haploinsufficiency in WAGR microdeletion cases was associated with severe hypoplasia of the corpus callosum. In utero short hairpin RNA-mediated C11orf46 knockdown disrupted transcallosal projections of cortical pyramidal neurons, a phenotype that was rescued by wild type C11orf46 but not the C11orf46R236H mutant associated with autosomal recessive intellectual disability. Multiple genes encoding key regulators of axonal growth and differentiation, including Sema6A, were hyperexpressed in C11orf46-knockdown neurons. Importantly, RNA-guided epigenetic editing of neuronal Sema6a gene promoters via a dCas9 protein-conjugated SunTag scaffold with multimeric (10x) C11orf46 binding during early developmental periods, resulted in normalization of expression and rescue of transcallosal dysconnectivity via repressive chromatin remodeling, including up-regulated histone H3K9 methylation by the KAP1-SETDB1 repressor complex. Our study demonstrates that interhemispheric communication is highly sensitive to locus-specific remodeling of neuronal chromatin, revealing the therapeutic potential for shaping the brains connectome via gene-targeted designer activators and repressor proteins.

developmental biology↗