Biology subjects

Luders, E.

Publications and source records attributed to Luders, E..

5 recordsLinked to original sources

bioRxiv2026

Tractometry-Based Quantification of Along-Tract White-Matter Hemispheric Asymmetry in Alzheimer's Disease

White-matter hemispheric asymmetry is a fundamental property of human brain organization and is known to change in aging, neurodevelopment, and neurodegenerative disorders. Tractometry analyzes diffusion-derived microstructural measures along the full length of tracts, localizing changes to specific tract-segments rather than collapsing tracts into a single value. Yet, existing frameworks lack a principled way to quantify left-right hemispheric asymmetries along homologous tracts. Here, we introduce an asymmetry-aware tractometry framework that integrates a symmetric white-matter atlas with BUAN (Bundle Analytics) to enable anatomically consistent, along-tract comparison of homologous pathways. By defining homologous bundles with a shared template and consistent orientation, each left-hemisphere segment is directly matched to its right-hemisphere counterpart, enabling principled, segment-wise comparison and revealing spatially localized asymmetries along-tract. Applying this framework to diffusion MRI data from the Alzheimers Disease Neuroimaging Initiative (ADNI) comprising 1,215 subjects, we demonstrate how this approach reveals systematic left-right asymmetries across major white-matter pathways and show how these patterns differentiate cognitively normal (CN) individuals from those with mild cognitive impairment (MCI) and dementia. This method provides a sensitive and anatomically grounded tool for studying hemispheric specialization and its disruption in aging and disease, and establishes a general approach for asymmetry-aware tractometry in population neuroimaging studies.

neuroscience↗

bioRxiv2025

Increased Callosal Thickness in Early Trained Opera Singers

Extensive research has shown how the corpus callosum adapts to early sensorimotor training in instrumental musicians, yet less is known about these effects in professional singers. This study used high-resolution MRI to investigate variations in callosal thickness in relation to vocal training in 55 participants, including 27 professionally trained opera singers and 28 non-singers. Results indicated trend-level differences with thicker callosal regions in singers, particularly at the anterior-posterior midbody border and the isthmus, though these did not survive corrections for multiple comparisons. However, a significant negative correlation between age at first singing lesson and callosal thickness was found in the anterior third (rostrum, genu, rostral body), the anterior-posterior midbody border, and the isthmus, suggesting that early vocal training facilitates lasting neuroplastic adaptations in these regions. Additionally, a positive trend between years of professional singing and greater thickness in the midbody was observed but did not remain significant after correction for multiple comparisons. Greater callosal thickness likely enhances interhemispheric connectivity to meet the demands of operatic performance, highlighting the adaptability of the corpus callosum to early, sustained sensorimotor training. By extending evidence from instrumental musicians to singers, these results underscore developmental timing as a key factor in how sensorimotor training shapes brain structure.

neuroscience↗

bioRxiv2024

Approximation of bone mineral density and subcutaneous adiposity using T1-weighted images of the human head

Bones and brain are intricately connected and scientific interest in their interaction is growing. This has become particularly evident in the framework of clinical applications for various medical conditions, such as obesity and osteoporosis. The adverse effects of obesity on brain health have long been recognised, but few brain imaging studies provide sophisticated body composition measures. Here we propose to extract the following bone- and adiposity-related measures from T1-weighted MR images of the head: an approximation of skull bone mineral density (BMD), skull bone thickness, and two approximations of subcutaneous fat (i.e., the intensity and thickness of soft non-brain head tissue). The measures pertaining to skull BMD, skull bone thickness, and intensi-ty-based adiposity proxy proved to be reliable (r=.93/.83/.74, p<.001) and valid, with high correlations to DXA-de-rived head BMD values (rho=.70, p<.001) and MRI-derived abdominal subcutaneous adipose volume (rho=.62, p<.001). Thickness-based adiposity proxy had only a low retest reliability (r=.58, p<.001).The outcomes of this study constitute an important step towards extracting relevant non-brain features from available brain scans.

neuroscience↗

bioRxiv2022

CAT - A Computational Anatomy Toolbox for the Analysis of Structural MRI Data

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT) - a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface, but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams - illustrated on an example dataset - allow for voxel-based, surface-based, as well as region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT, while offering a citable standard for the neuroscience community.

neuroscience↗

bioRxiv2022

To smooth or not to smooth: One step closer to single-voxel accuracy without spatial smoothing

Traditionally, when conducting voxel- or vertex-wise analyses in neuroimaging studies, it seemed imperative that brain data are convoluted with a Gaussian kernel, a procedure known as "spatial smoothing". However, we suggest that - under certain conditions - smoothing may be omitted for the benefit of an improved regional specificity. We demonstrate the suitability of this omission by combining high-dimensional spatial registration and threshold-free cluster enhancement (TFCE) in a sample of 754 brains. Our findings revealed that, without smoothing, it is possible to capture brain atrophy within the hippocampal complex while dissociating neighboring areas (cornu ammonis, dentate gyrys, subiculum, and amygdala). In contrast, the traditional smoothing step would result in a single hippocampal cluster (the larger the smoothing kernel, the lower the specificity). Supplemental analyses not only varying the size of the smoothing kernel, but also the size of the sample, the signal-to-noise ratio, as well as the accuracy of the spatial registration confirm that no smoothing (or less smoothing) leads to increased specificity while maintaining sensitivity, at least for small-scale structures (e.g., hippocampus and amygdala). Nevertheless, classic analyses based on smoothed data will continue to provide important insights, especially for large-scale structures (e.g., cortical regions).

neuroscience↗