Biology subjects

Grefkes, C.

Publications and source records attributed to Grefkes, C..

6 recordsLinked to original sources

bioRxiv2026

Does Pre-Sensorimotor Brain Activity Modulate TMS-Evoked Potentials?

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) provides a direct measure of cortical responses to local brain perturbations and offers a powerful approach for investigating brain-state-dependent neural dynamics. However, it remains unclear to what extent ongoing sensorimotor brain activity influences TMS-evoked potentials (TEPs). To address this question, thirty healthy participants performed kinesthetic motor imagery of left- and right-index finger tapping while single-pulse TMS was applied over the left primary motor cortex. 64-channel EEG was recorded to characterize sensorimotor oscillatory activity and TEPs. Time-frequency analyses quantified event-related desynchronization (ERD) in the mu (8-12 Hz) and beta (13-30 Hz) frequency bands, while TEP amplitudes were examined within early latency windows (10-65 ms). Motor imagery induced robust bilateral mu- and beta-band ERD, confirming successful engagement of the sensorimotor network. However, ERD patterns were highly similar between left- and right-hand motor imagery, and neither frequentist nor bayesian analyses identified differences in TMS-evoked potentials between imagery conditions. Source reconstruction demonstrated rapid propagation of TMS-evoked activity from the stimulated motor cortex to distributed bilateral motor regions. Correlation analyses revealed that greater pre-stimulus beta-band ERD was associated with larger P60 amplitudes. Because the instructed motor imagery conditions did not produce distinguishable pre-stimulus brain states, an exploratory trial-by-trial analysis was subsequently performed to account for naturally occurring variability in sensorimotor lateralization to investigate the effect of variability in ERD on TRP. This analysis revealed an overall effect of trial-by-trial brain state on TEP amplitudes

neuroscience↗

bioRxiv2026

Stroke-induced lipocalin-2-expressing red pulp macrophages reprogram peripheral immunity

Acute ischemic stroke (AIS) induces profound systemic immune alterations that contribute to infection susceptibility. Here, we identify lipocalin-2 (LCN-2) as a rapidly induced and conserved regulator of stroke-associated immunosuppression. Using 3-MACE-Seq, cytokine profiling, and immunofluorescence in C57BL/6J mice subjected to transient middle cerebral artery occlusion (tMCAO), we show that LCN-2 is strongly upregulated in splenic red pulp macrophages (RPMs) within 24 hours and again 7 days post-tMCAO. LCN-2-expressing RPMs form immunological synapses with CD3+ T cells, thereby impacting T cell trafficking. Recombinant LCN-2 directly reprogrammed T cells and monocytes toward hyporesponsive, tolerogenic phenotypes by suppressing inflammatory cytokines, impairing chemotaxis, enhancing phagocytosis, and uncoupling oxidative burst. Human spleens likewise displayed LCN-2-expressing CD68+ RPMs, and LCN-2 preconditioning of monocytes reproduced reduced HLA-DR, CD80, CD206, and ROS with increased uptake of E. coli bioparticles. These findings identify LCN-2 signaling as a central orchestrator of stroke-induced peripheral immunoreprogramming and a potential therapeutic target to mitigate post-stroke immunodepression. SummaryAcute ischemic stroke induces LCN-2 in splenic red pulp macrophages, which reprogram T cells and monocytes toward tolerogenic, hyporesponsive states. Mouse and human data identify LCN-2 as a driver of peripheral immunodepression and a potential target to reduce infection risk. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/733904v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@fb0caforg.highwire.dtl.DTLVardef@1fdcd41org.highwire.dtl.DTLVardef@1ae99b0org.highwire.dtl.DTLVardef@1ec0837_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

immunology↗

bioRxiv2025

Ischemic stroke induces persistent alteration to brain stromal progenitor cells linked to chronic vascular dysfunction

Fibrotic scar formation after stroke serves a dual role: while essential for providing structural support during post-ischemic recovery, excessive fibrosis in the chronic phase of stroke impairs regenerative processes including axonal regrowth and neovascularization. The temporal dynamics of fibrosis are critical determinants of functional outcomes, as the balance between protective scarring and regenerative capacity differs across distinct stroke phases. Consequently, strategic modulation of fibrotic processes to preserve regenerative potential represents a promising therapeutic approach in stroke recovery. To understand the cellular mechanisms underlying this fibrotic response, we investigated stromal progenitor cell composition in the post-stroke brain. The vast majority of stromal progenitor cells (SPCs) are pericytes, with minorities comprising perivascular fibroblasts (PVFs) and vascular smooth muscle cells. We demonstrate that ischemic stroke drives a long-term shift in this composition, characterized by sustained expansion of the PVF population and excessive laminin deposition in the peri-infarct region, effects that persist for at least six months post-stroke. Single-cell RNA sequencing revealed sustained transcriptional and compositional alterations in the SPC population throughout chronic post-stroke phase, driven by AP-1-mediated signaling via TNF in both PVFs and pericytes. These changes correlate with long-term vasomotor dysfunction and capillary constriction in the peri-infarct region at six weeks post-stroke. Ischemic stroke drives aberrant, persistent PVF accumulation at the capillary bed with implications for post-stroke cerebrovascular dysfunction and recurrent stroke. Taken together, these findings reveal that ischemic stroke drives an aberrant long-term mis-localization of PVFs to the capillary bed that may have clinically-relevant implications for post-stroke cerebrovascular function as well as potential ramifications for recurrent stroke.

neuroscience↗

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↗