Biology subjects

Glocker, B.

Publications and source records attributed to Glocker, B..

3 recordsLinked to original sources

bioRxiv2021

A U-Net model of local brain-age

AO_SCPLOWBSTRACTC_SCPLOWWe propose a new framework for estimating neuroimaging-derived "brain-age" at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n=3463 healthy people (aged 18-90 years) to produce individualised 3D maps of brain-predicted age. When testing on n=692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age "gaps". To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n=267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohens d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.

neuroscience↗

bioRxiv2020

Cellular Protrusions Engage Viral Infection Enhancing EF-C Peptide Nanofibrils

Self-assembling peptide nanofibrils (PNF) have gained increasing attention as versatile molecules in material science and biomedicine. One important application of PNF is to enhance retroviral gene transfer, a technology that has been central to the development of gene therapy. The best-investigated and commercially available PNF is derived from a 12-mer peptide termed EF-C. The mechanism of transduction enhancement depends on the polycationic surface of EF-C PNF, which binds to the negatively charged membranes of viruses and cells thereby overcoming electrostatic repulsion and increasing virion attachment and fusion. Assuming an even distribution of charges at the surfaces of virions and cells would result in an evenly distributed interaction of the virions with the cell surface. However, we here report that PNF do not randomly bind at the cell surface but are actively engaged by cellular protrusions. Chemical suppression of protrusion formation in cell lines and primary CD4+ T cells greatly reduced fibril binding and hence virion binding. Thus, the mechanism of PNF-mediated viral transduction enhancement involves active engagement of virus-loaded fibrils by cellular protrusions.

cell biology↗

bioRxiv2017

Multimodal Surface Matching with Higher-Order Smoothness Constraints

The accurate alignment of brains is fundamental to the statistical sensitivity and spatial localisation of group studies in brain imaging, and cortical surface-based alignment is generally accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of cortical areas relative to these folds, which makes aligning cortical areas based on folding alone a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that using areal features such as resting state-networks and myelin maps to drive cross-subject surface alignment improves group task fMRI statistics and map sharpness. However, the initial implementation of MSMs regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits unless the regularisation strength was increased, in which case this prevented the algorithm from fully maximizing surface alignment. Here, we propose a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multi-modal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints to enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modeling of cortical development for neonates (born between 32 and 45 weeks) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.

neuroscience↗