Biology subjects

Wright, A. M.

Publications and source records attributed to Wright, A. M..

13 recordsLinked to original sources

bioRxiv2026

Physiological Oscillations of Lateral Ventricular Volume Revealed by Fast fMRI: Effects of Aging and Vigilance

Abstract Background: Cyclic ventricular deformation may contribute to CSF displacement. However, the physiological mechanisms underlying ventricular motion remain poorly understood. This study characterized physiological oscillations of lateral ventricular volume (LVV) and examined their associations with aging and vigilance. Methods: We analyzed two fast resting-state fMRI scans from 99 adults aged 35-82 years. The first scan was acquired during eyes-open wakefulness, followed by 30 min of eyes-closed rest and a second scan during eyes-closed drowsiness. LVV was estimated for each image volume using automated ventricular segmentation, yielding time-resolved volume measurements. Temporal variability was quantified using the standard deviation and coefficient of variation, and spectral power was quantified for low-frequency, respiratory, and cardiac oscillations. Age-related associations were assessed using multiple linear regression adjusted for sex. Awake-drowsy differences were assessed using paired t-tests. False discovery rate correction was applied for multiple comparisons in all analyses. Results: After quality-control exclusions, 97 participants were analyzed. LVV exhibited distinct low-frequency, respiratory, and cardiac oscillatory components. Respiratory and cardiac oscillations accounted for 37.7% and 38.4% of total physiological spectral power, respectively, compared with 23.9% for low-frequency oscillations. Mean lateral ventricular volume increased with age (FDR-adjusted p = 0.010), while respiratory and cardiac power also showed positive associations with age, although neither survived FDR correction (both FDR-adjusted p = 0.067). The transition to eyes-closed drowsiness was associated with greater LVV fluctuation magnitude, reflected by increases in standard deviation (FDR-adjusted p < 0.001) and coefficient of variation (FDR-adjusted p = 0.028) and accompanied by increased low-frequency (FDR-adjusted p < 0.001) and respiratory power (FDR-adjusted p = 0.015). Mean LVV and cardiac power did not change significantly between conditions. Conclusions: LVV exhibits prominent respiratory and cardiac oscillations in addition to low-frequency fluctuations, suggesting that ventricular dynamics reflect multiple physiological influences rather than a single dominant physiological driver. Aging was primarily associated with ventricular enlargement, whereas reduced vigilance was accompanied by increased LVV fluctuation magnitude, primarily reflected in increased low-frequency and respiratory power, without a change in mean LVV. Time-resolved LVV provides a complementary approach for investigating the physiological regulation of ventricular dynamics in health and disease. Keywords: Cerebrospinal fluid; Lateral ventricular volume; Fast fMRI; Physiological oscillations; Cardiac pulsation; Respiration; Low-frequency oscillations; Aging; Vigilance

bioengineering↗

bioRxiv2026

Deep Learning-Based Reconstruction: Model Comparison for Variable-Density GRAPPA 1H MRSI

Proton magnetic resonance spectroscopic imaging (1H MRSI) enables quantitative mapping of brain metabolites, but its clinical use remains limited by long acquisition time. The goal of this work to improve the applicability of high-resolution 1H FID-MRSI at 7T by enhancing GRAPPA-based acceleration through deep learning-driven k-space reconstruction. In particular, compared with conventional GRAPPA, MultiNet PyGRAPPA enables substantially higher in-plane acceleration while suppressing residual lipid aliasing and preserving metabolite map fidelity in non-lipid-suppressed MRSI. Building on the MultiNet PyGRAPPA framework, we introduce a comprehensive comparison of advanced machine-learning models for predicting missing k-space points. Multiple architectures--including multilayer perceptrons, convolutional neural networks, and several U-Net variants--were trained within a variable-density k-space undersampling scheme to support acceleration factors of R = 4, 6, and 7. The proposed U-Net model extends the MultiNet concept by leveraging nonlinear hierarchical feature extraction, thereby improving reconstruction fidelity while maintaining robustness to noise.The methods were evaluated in vivo using retrospectively undersampled 7T 1H FID-MRSI datasets from healthy volunteers and patients. Quantitative analyses demonstrate that the U-Net outperforms the original MultiNet approach, offering improved SNR retention rate, reduced lipid RMSE, and higher structural similarity of major metabolites. Metabolite maps reconstructed with the U-Net showed reduced lipid artifacts and improved anatomical consistency. In conclusion, integrating deep convolutional networks into GRAPPA-based k-space prediction provides a more reliable and higher-fidelity reconstruction pipeline. When combined with variable-density undersampling, this approach enables faster acquisition of high-resolution 1H MRSI without compromising spectral quality or metabolite quantification.

bioengineering↗

bioRxiv2026

High-throughput discovery of arginine-depleted peptides enables effective antisense delivery for Duchenne muscular dystrophy

Phosphorodiamidate morpholino oligomers (PMOs) are approved exon-skipping antisense therapeutics for Duchenne muscular dystrophy (DMD), but their clinical utility is limited by poor uptake in muscle tissue, necessitating frequent high-dose administration. Cell-penetrating peptides (CPPs) can enhance intracellular delivery of PMOs, yet conventional arginine-rich CPPs often cause dose-limiting toxicity, including renal damage, which hinders their clinical translation. To address this challenge, we developed a high-throughput, charge-based chromatographic enrichment platform capable of screening over 15,000 synthetic peptides, including sequences with noncanonical (abiotic) amino acids. This approach enabled de novo discovery of arginine-depleted CPPs with improved delivery profiles. Four lead candidates demonstrated efficient nuclear PMO delivery with ~10-fold lower in vitro toxicity compared to standard CPPs such as penetratin. The top-performing peptide, CXP1, showed robust splice-switching activity and favorable tolerability in both cellular and animal models. In dystrophic mdx mice, CXP1-PMO conjugates achieved greater exon skipping compared to PMOs conjugated to R6G at equivalent doses. Tissue levels of CXP1-PMO correlated with exon-skipping efficacy, establishing a clear pharmacokinetic-pharmacodynamic relationship. These findings highlight a mechanistically novel and translationally relevant discovery strategy, demonstrating the potential of high-throughput platforms to generate more effective CPP-based delivery vehicles for antisense therapeutics in DMD and related neuromuscular disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=44 SRC="FIGDIR/small/730741v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@84b7f2org.highwire.dtl.DTLVardef@1487850org.highwire.dtl.DTLVardef@d2e263org.highwire.dtl.DTLVardef@10ce371_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2024

Quantitative 2D J-resolved metabolite-cycled semiLASER spectroscopy of metabolites and macromolecules in the human brain at 9.4 T

PurposeWhile two-dimensional (2D) in vivo spectroscopy yields rich information and has been successfully used in clinical trials, it requires a localization scheme that minimizes the impact of chemical shift displacement on J-coupling evolution, a robust frequency drift correction and dedicated processing and quantification methods. Considering these needs this study demonstrates a novel data acquisition and an analysis pipeline to quantify 16 metabolites in mmol/kg in the human brain using a 2D J-resolved metabolite-cycled (MC) semiLASER localization sequence at 9.4 T in the human brain. MethodsMetabolite spectra were acquired in vivo using the newly developed J-resolved MC semiLASER localization sequence with maximum echo sampling (MES) at 9.4 T. In order to account for the underlying macromolecular (MM) spectra in the acquired metabolite spectra, J-resolved MM spectra were acquired using a double inversion recovery (DIR) J-resolved MC semiLASER. Spectral fitting was performed with ProFit 2.0 using a simulated basis set from VesPA tailored to 2D J-resolved semiLASER with MES. Finally, metabolite concentrations were calculated using internal water referencing. ResultsTissue concentrations for 16 metabolites in mmol/kg are reported after correcting for number of protons, tissue content, and relaxation effects of both water and metabolites at 9.4T. Quantification results of spectra considering 8 and 2 averages per TE did not show any significant differences. Conclusion2D spectra of metabolites acquired at 9.4T and 2D MMs acquired at any field strength are presented for the first time. Basis set simulation and quantification of metabolites for metabolite spectra acquired using maximum-echo-sampled 2D J-resolved semiLASER was performed for the first time. The sensitivity in the detection of J-coupled metabolites such as glutamine, glucose or lactate. At ultra-high field, the acquisition duration of 2D MRS can be also substantially reduced since only a very low number of averages per TE are needed.

neuroscience↗

bioRxiv2024

Robust data-driven segmentation of pulsatile cerebral vessels using functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) captures rich physiological and neuronal information that can offer insights into neurofluid dynamics, vascular health, and waste clearance function. The availability of cerebral vessel segmentation could facilitate fluid dynamics research in fMRI. However, without magnetic resonance angiography scans, cerebral vessel segmentation is challenging and time-consuming. This study leverages cardiac-induced pulsatile fMRI signal to develop a data-driven, automatic segmentation of large cerebral arteries and the superior sagittal sinus (SSS). The method was validated in a local dataset by comparing it to ground truth cerebral artery and SSS segmentations. Using the Human Connectome Project (HCP) aging dataset, the methods reproducibility was tested on 422 participants aged 36 to 100 years, each with four repeated fMRI scans. The method demonstrated high reproducibility, with an intraclass correlation coefficient > 0.7 in both cerebral artery and SSS segmentation volumes. This study demonstrates that the large cerebral arteries and SSS can be reproducibly and automatically segmented in fMRI datasets, facilitating the investigation of fluid dynamics in these regions.

bioengineering↗

bioRxiv2024

Modeling of Rate Heterogeneity in Datasets Compiled for Use With Parsimony

AO_SCPLOWBSTRACTC_SCPLOWIn recent years, there has been an increased interest in modeling morphological traits using Bayesian methods. Much of the work associated with modeling these characters has focused on the substitution or evolutionary model employed in the analysis. However, there are many other assumptions that researchers make in the modeling process that are consequential to estimated phylogenetic trees. One of these is how among-character rate variation (ACRV) is parameterized. In molecular data, a discretized gamma distribution is often used to allow different characters to have different rates of evolution. Morphological data are collected in ways that fundamentally differ from molecular data. In this paper, we appraise the use of standard parameters for ACRV and provide recommendations to researchers who work with morphological data in a Bayesian framework.

paleontology↗

bioRxiv2024

Quantitative Models for Distinguishing Punctuated and Continuous-Time Models of Character Evolution and Their Implications for Macroevolutionary Theory

The recent proliferation of quantitative models for assessing anatomical character evolution all assume that character change happens continuously through time. However, punctuated equilibrium model posits that character change should be coincide with cladogenetic events, and thus should be tied to origination rates. Rates of cladogenesis are important to quantitative phylogenetics, but typically only for establishing prior probabilities of phylogenetic topologies. Here, we modify existing character likelihood models to use the local cladogenesis rates from Bayesian analyses to generate the amounts of character change over time dependent on origination rates, as expected under the punctuated equilibrium model. In the case of strophomenoid brachiopods strop from the Ordovician, we find that Bayesian analyses strongly favor punctuated models over continuous-time models, with elevated rates of cladogenesis early in the clades history inducing frequencies of change despite constant rates of change per speciation event. This corroborates prior work proposing that the early burst in strophomenoid disparity reflects simply elevated speciation rates, which in turn has implications for seemingly unrelated macroevolutionary theory about whether early bursts reflect shifts in intrinsic constraints or empty ecospace. Future development of punctuated character evolution models should account for the full durations of species, which will provide a test of continuous change rates. Ultimately, continuous change vs. punctuated change should become part of phylogenetic paleobiology in the same way that other tests of character evolution currently are. Non-technical SummaryPunctuated Equilibrium predicts a distribution of anatomical change that is fundamentally different from the models used in studies of relationships among species. We present a model to assess relationships that assumes punctuated change. We apply this model to a dataset of strophomenoid brachiopods to demonstrate that a model of punctuated change fits better than a model of continuous-time ("phyletic gradualism") change in this group. Notably, because the punctuated model posits elevated speciation rates early in the strophomenoid history, the model also posits elevated rates of change among the early strophomenoids relative to later ones. This corroborates notions for what causes bursts of anatomical evolution rooted in ecological theory rather than evolutionary developmental theory. More basically, it emphasizes that paleontologists should consider both punctuated and continuous-time models when assessing relationships and other aspects of macroevolutionary theory.

paleontology↗

bioRxiv2024

Quantitative metabolic reference for healthy human cerebrum derived from group averaged 9.4T 1H MRSI data

Drawing inspiration from previous works using 1H FID MRSI, this study quantifies metabolite concentrations at 9.4 T in the human cerebrum of a volunteer cohort and performs a respective group analysis to derive region specific metabolite concentrations. Voxel-specific corrections were performed for both water and individual metabolites, as well as used tissue specific T1-relaxation times. Anatomical and magnetic resonance spectroscopic imaging data were collected using MP2RAGE and FID MRSI sequences, and subsequent data underwent a series of preprocessing techniques. Results showed consistent metabolite maps for key metabolites (NAA, tCr, Glu, tCho and mI), while instability in data quality was noted for lower slices. This study not only showcases the potential of metabolite quantification and mapping at 9.4 T but also underscores the necessity for meticulous data processing to ensure accurate metabolite representations. Comparisons with earlier works and single voxel results validate the methodologies adopted. HighlightsO_LIQuantitative whole cerebrum metabolite maps in the human brain acquired at 9.4 T. C_LIO_LIGroup averaged regional metabolite concentrations. C_LIO_LIGroup summed regional metabolite spectra. C_LI

neuroscience↗

bioRxiv2024

Assessing the Adequacy of Morphological Models used in Palaeobiology

Reconstructing the evolutionary history of different groups of organisms provides insight into how life originated and diversified on Earth. Phylogenetic trees are commonly used to estimate this evolutionary history, providing a hypothesis of the events. Within Bayesian phylogenetics a major step in estimating a tree is in choosing an appropriate model of character evolution. In the case of most extinct species, our only source of information to decipher their phylogenetic relationships is through the morphology of fossils. We therefore use a model of morphological character evolution, the most common of which being the Mk Lewis model. While it is frequently used in palaeobiology, it is not known whether the simple Mk substitution model, or any extensions to it, provide a sufficiently good description of the process of morphological evolution. To determine whether or not the Mk model is appropriate for fossil data we used posterior predictive simulations, a model adequacy approach, to estimate absolute fit of the model to morphological data sets. We first investigate the impact that different versions of the Mk model have on key parameter estimates using tetrapod data sets. We show that choice of substitution model has an impact on both topology and branch lengths, highlighting the importance of model choice. Next, we use simulations to investigate the power of posterior predictive simulations for morphology. Having validated this approach we show that current variations of the Mk model are in fact performing adequately in capturing the evolutionary dynamics that generated our data. We do not find any preference for a particular model extension across multiple data sets, indicating that there is no one size fits all when it comes to morphological data and that careful consideration should be given to choosing models of discrete character evolution. By using suitable models of character evolution, we can increase our confidence in our phylogenetic estimates, which should in turn allow us to gain more accurate insights into the evolutionary history of both extinct and extant taxa.

paleontology↗

bioRxiv2022

Mapping of Glutamate Metabolism using 1H FID-MRSI after oral Administration of Glc at 9.4 T

Glutamate is the major excitatory transmitter in the brain and malfunction of the related metabolism is associated with various neurological diseases and disorders. The observation of labeling changes in the spectra after the administration of a 13C labelled tracer is a common tool to gain better insights into the function of the metabolic system. But so far, only a very few studies presenting the labelling effects in more than two voxels to show the spatial dependence of metabolism. In the present work, the labeling effects were measured in a transversal plane in the human brain using ultra-short TE and TR 1H FID-MRSI. The measurement set-up was most simple: The [1-13C]Glc was administered orally instead of intravenous and the spectra were measured with a pure 1H technique without the need of a 13C channel (as Boumezbeur et al. demonstrated in 2004). Thus, metabolic maps and enrichment curves could be obtained for more metabolites and in more voxels than ever before in human brain. Labeling changes could be observed in [4-13C]glutamate, [3-13C]glutamate+glutamine, [2-13C]glutamate+glutamine, [4-13C]glutamine, [2,3-13C]aspartate and [6-13C]N-acetyl aspartate with a high temporal (3.6 min) and spatial resolution (32x32 grid with nominal voxel size of 0.33 L) in five volunteers.

neuroscience↗

bioRxiv2022

Test-retest reproducibility of human brain multi-slice 1H FID-MRSI data at 9.4 T after optimization of lipid regularization, macromolecular model and spline baseline stiffness

* PurposeThis study analyzes the effects of retrospective lipid suppression, a simulated macromolecular prior knowledge and different spline baseline stiffness values on 9.4 T multi-slice proton FID-MRSI data spanning the whole cerebrum of human brain and its reproducibility of metabolite ratio (/tCr) maps for 10 brain metabolites. * MethodsMeasurements were performed twice on five volunteers using a non-accelerated FID MRSI 2D sequence at 9.4 T. The effects of retrospective lipid L2-regularization, macromolecular spectrum and different LCModel baseline flexibilities on SNR, FWHM, fitting residual, CRLB and the concentration ratio maps were investigated. Intra-subject, inter-session coefficient of variation of the mean metabolite ratios (/tCr) of each slice was calculated. * ResultsL2-regularization provided effective suppression of lipid-artifacts, but should be avoided if no artifacts are detected. Transversal, sagittal and coronal of many metabolite ratio maps correspond to anatomically expected concentration relations in gray and white matter for the majority of the cerebrum when using a flexible baseline in LCModel fit. Additionally, results from the second measurements of the same subjects show that slice positioning and data quality correlate significantly to the first measurement. * ConclusionConcentration ratio maps (/tCr) for 4 metabolites (tCho, NAA, Glu, mI) spanning the majority and six metabolites (NAAG, GABA, GSH, Tau, Gln, Asp) covering 32 mm in the upper part of the brain were acquired at 9.4 T using multi-slice FID MRSI with retrospective lipid suppression, a macromolecular spectrum and a flexible LCModel baseline.

neuroscience↗

bioRxiv2022

Dynamic observation of 2H labeled compounds in the human brain with 1H versus 2H magnetic resonance spectroscopy at 9.4T

The metabolic pathway of [6,6-2H2]-labeled glucose was investigated with two different techniques. The first technique used direct detection of deuterium applying Deuterium Metabolic Imaging (DMI). The second technique used the indirect detection of deuterium with proton MR spectroscopy (MRS) called Quantitative Exchanged-label Turnover (QELT) MRS. For the first time, time-resolved data was acquired for both techniques in the same healthy human subjects and directly compared. The time-curves were used in a kinetic model to estimate rates of the metabolic pathway of glucose. Two different kinetic models were compared. One included only DMI data, the second one combined DMI and QELT. For the first model, a tricarboxylic acid (TCA) cycle rate of 0.69 {+/-} 0.10 mol{middle dot}min-1{middle dot}g-1 was determined. For the second model, the estimated TCA cycle rate was 0.68 {+/-} 0.12 mol{middle dot}min-1{middle dot}g-1. In addition, the rate of glutamine synthesis from glutamate could be estimated with model 2 (0.51 {+/-} 0.15 mol{middle dot}min-1{middle dot}g-1). The sensitivity of both methods was evaluated and compared to alternative techniques.

neuroscience↗

bioRxiv2021

Quantitative T1-relaxation corrected metabolite mapping of 12 metabolites in the human brain at 9.4 T

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive imaging modality that enables observation of metabolites. Applications of MRSI for neuroimaging applications has shown promise for monitoring and detecting various diseases. This study builds off previously developed techniques of short TR, 1H FID MRSI by correcting for T1-weighting of the metabolites and utilizing an internal water reference to produce quantitative (mmol kg-1) metabolite maps. This work reports and shows quantitative metabolite maps for 12 metabolites for a single slice. Voxel-specific T1-corrections for water are common in MRSI studies; however, most studies use either averaged T1-relaxation times to correct for T1-weighting of metabolites or omit this correction step entirely. This work employs the use of voxel-specific T1-corrections for metabolites in addition to water. Utilizing averaged T1-relaxation times for metabolites can bias metabolite maps for metabolites that have strong differences between T1-relaxation for GM and WM (i.e. Glu). This work systematically compares quantitative metabolite maps to single voxel quantitative results and qualitatively compares metabolite maps to previous works. HighlightsO_LIQuantitative metabolite maps of 12 metabolites human brains acquired at 9.4 T. C_LIO_LIVoxel-specific T1-weighting corrections for water and metabolites. C_LIO_LIComparisons of T1-weighted and T1-corrected metabolite maps. C_LI

neuroscience↗