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Schott, B.

Publications and source records attributed to Schott, B..

3 recordsLinked to original sources

Automated quantification of white matter hyperintensity confluence: A measure of spatial organisation beyond volume and visual rating scales

White matter hyperintensities (WMH) are a highly prevalent finding on FLAIR MRI scans and a prominent feature of white matter pathology across cerebrovascular and neurodegenerative diseases. Currently, WMH are assessed with visual rating scales such as the Fazekas scale or with their volume, as calculated from automatic or manual segmentations. Both methods have limitations: Visual rating scales are rater-dependent and coarse, while WMH volume does not take the confluence of lesions into account and thus disregards their spatial organisation. As an alternative, here we propose a novel automated method for quantifying the confluence of white matter hyperintensities on a continuous standardised scale between 0 and 1. The metric is based on WMH segmentations from routine MRI and quantifies the extent to which individual WMH merge into coherent lesions, independently of total lesion volume. We apply the method to QMIN-MC, a large UK memory clinic cohort, and show associations of the confluence metric with age, cognitive performance across domains, and Fazekas ratings. Participants with vascular and mixed dementia showed higher confluence than other diagnostic groups, whereas cognitively unimpaired participants showed lower confluence. However, confluence did not explain additional cognitive variance after accounting for log-transformed WMH volume. Findings were validated in DELCODE, an independent cohort of individuals with neurodegenerative disorders, replicating our original results. In this validation cohort, periventricular WMH confluence remained associated with cognition after adjustment for WMH volume. These findings introduce WMH confluence as a reproducible, automated, and fine-grained measure of lesion spatial organisation. It provides complementary information about morphological WMH severity beyond volume and is an alternative to visual rating scales. Although related to WMH volume in memory-clinic populations, confluence captures clinically interpretable information and may complement existing WMH measures for improved lesion characterisation in studies of white matter disease, ageing, and cognitive impairment.

neuroscience↗

SMAS: Structural MRI-Based AD Score using Bayesian VAE

This study introduces the Structural MRI-based Alzheimers Disease Score (SMAS), a novel index intended to quantify Alzheimers Disease (AD)-related morphometric patterns using a deep learning Bayesian-supervised Variational Autoencoder (Bayesian-SVAE). SMAS index was constructed using baseline structural MRI data from the DELCODE study and evaluated longitudinally in two independent cohorts: DEL-CODE (n=415) and ADNI (n=190). Our findings indicate that SMAS has strong associations with cognitive performance (DELCODE: r=-0.83; ADNI: r=-0.62), age (DEL-CODE: r=0.50; ADNI: r=0.28), hippocampal volume (DEL-CODE: r=-0.44; ADNI: r=-0.66), and total grey matter volume (DELCODE: r=-0.42; ADNI: r=-0.47), suggesting its potential as a biomarker for AD-related brain atrophy. Moreover, our longitudinal studies suggest that SMAS may be useful for early identification and tracking of AD. The model demonstrated significant predictive accuracy in distinguishing cognitively healthy individuals from those with AD (DELCODE: AUC=0.971 at baseline, 0.833 at 36 months; ADNI: AUC=0.817 at baseline, improving to 0.903 at 24 months). Notably, over a 36-month period, SMAS index outperformed existing measures such as SPARE-AD and hippocampal volume. Relevance map analysis revealed significant morphological changes in key AD-related brain regions--including the hippocampus, posterior cingulate cortex, precuneus, and lateral parietal cortex--highlighting that SMAS is a sensitive and interpretable biomarker of brain atrophy, suitable for early AD detection and longitudinal monitoring of disease progression.

neuroscience↗

Creating Coarse-Grained Systems with COBY: Towards Higher Accuracy in Membrane Complexity

Current trends in molecular modelling are geared towards increasingly realistic representations of the modelled systems. This is reflected in larger, more complex systems, which are difficult to build and would ideally rely on a software that converts userprovided descriptors into system coordinates. This is not a trivial task, as the building algorithms use simplifications that can introduce inaccuracies in the system properties that do not correspond to the requested values. We created COBY, a coarse-grained system builder that can create a large variety of systems in a single command call using Martini molecule models. We improved the accuracy of the complex membrane and solvent building procedures, introduced a variety of arguments that can be used to build diverse systems, and implemented features intended for force field development. COBY can be used to build flat membranes of any degree of complexity, handle protein and solvent insertion, solute flooding, stacked membranes, membrane patches and pores, and includes advanced functionalities such as molecule import, lipid building from fragments, handling of multiple parameter libraries, and several choices of algorithms for interpreting user-provided system descriptors. COBY is an open-source software written in Python 3, and the code, documentation, and tutorials are hosted at github.com/MikkelDA/COBY. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/604601v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@15e46a4org.highwire.dtl.DTLVardef@6fd668org.highwire.dtl.DTLVardef@657541org.highwire.dtl.DTLVardef@f51840_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗