Biology subjects

Grefkes, C.

Publications and source records attributed to Grefkes, C..

3 recordsLinked to original sources

bioRxiv2025

Predicting Brain Volumes from Anthropometric and Demographic Features: Insights from UK Biobank Neuroimaging Data

Brain size measures are well-studied and often treated as a confound in volumetric neuroimaging analyses. Yet their relationship with body anthropometric measures and demographics remains underexplored. In this study, we examined those relationships alongside age- and sex-related differences in global brain volumes. Using brain magnetic resonance imaging (MRI) of healthy participants in the UK Biobank, we derived global measures of brain morphometry, including total intracranial volume (TIV), total brain volume (TBV), gray matter volume (GMV), white matter volume (WMV), and cerebrospinal fluid (CSF). We extracted these measures using the Computational Anatomy Toolbox (CAT) and FreeSurfer. Our analyses were structured in three approaches: across-sex analysis, sex-specific analysis, and impact of age analysis. Employing machine learning (ML), we found that TIV was strongly predicted by sex (across-sex r = 0.68), reflecting sexual dimorphism. On the other hand, TBV, GMV, WMV, and CSF were more sensitive to age, with higher prediction accuracy when age was included as a feature, highlighting age-related changes in the brain structure, such as fluid expansion. Sex-specific models showed reduced TIV prediction (r {approx} 0.25) but improved TBV accuracy (r {approx} 0.44), underscoring sex-specific body-brain relationships. Anthropometrics enhanced prediction but only subsidiary to age and sex. These findings advance our understanding of brain-body scaling relationships and underscore the necessity of accounting for age and sex in neuroimaging studies of brain morphology.

neuroscience↗

bioRxiv2024

Proportional recovery in mice with cortical stroke

The proportional Recovery Rule (PRR) has been frequently used to predict recovery of lost motor function in acute stroke patients. However, it still needs to be explored whether the same concept applies to preclinical, i.e. animal models of stroke recovery. To address this question, we investigated behavioral data from 125 adult male C57Bl/6J mice with photothrombotic strokes in the sensorimotor cortex. Lesion size and location were determined in the first week using in vivo T2-weighted MRI. Motor recovery was evaluated repeatedly over four weeks using the cylinder, grid walk, and rotating beam test. Recovery trajectories were analyzed using a newly formulated Mouse Recovery Rule (MRR), comparing it against the traditional PRR. Initial findings indicated variable recovery patterns, which were separated using a stepwise linear regression approach resulting in two clusters: 47% PRR and 53% MRR. No significant correlation was found between recovery patterns and lesion size or location, suggesting that other biological factors drive individual differences in recovery. Of note, in the MRR cluster, animals recovered to 90% of their initial behavioral state within the first four weeks post-stroke, which is higher than the 70% recovery usually reported in human PRR studies. This study demonstrates the complexity of translating the PRR to stroke recovery models in mice and underscores the need for species-specific recovery models. Our findings have implications for designing and interpreting therapeutic strategies for stroke recovery in preclinical settings, with the potential to improve the predictive accuracy of stroke recovery assessments.

animal behavior and cognition↗

bioRxiv2024

AutoGaitA - Automated Gait Analysis in Python

Individual behaviours require the nervous system to execute specialised motor programs, each characterised by unique patterns of coordinated movements across body parts. Deep learning approaches for body-posture tracking have facilitated the analysis of such motor programs. However, translating the resulting time-stamped coordinate datasets into meaningful kinematic representations of motor programs remains a long-standing challenge. We developed the versatile quantitative framework AutoGaitA (Automated Gait Analysis), a Python toolbox that enables comparisons of motor programs at multiple levels of granularity and across tracking methods, species and behaviours. AutoGaitA allowed us to demonstrate that flies, mice, and humans, despite divergent biomechanics, converge on the age-dependent loss of propulsive strength, and that, in mice, locomotor programs adapt as an integrated function of both age and task difficulty. AutoGaitA represents a truly universal framework for robust analyses of motor programs and changes thereof in health and disease, and across species and behaviours.

animal behavior and cognition↗