Biology subjects

Kress, S.

Publications and source records attributed to Kress, S..

4 recordsLinked to original sources

bioRxiv2025

EGFR activation correlates with intracranial pressure and outcome in a mixed intracranial bleeding porcine model

Intracranial hypertension is a major driver of secondary injury after acute subdural hematoma (ASDH), yet how mechanical stress is translated into neuroinflammatory signaling remains poorly understood. Here, we identify a mechanosensitive astrocyte signaling pathway that links elevated intracranial pressure (ICP) to inflammatory amplification in the injured brain. Using a clinically relevant porcine ASDH model combined with mechanistic studies in human iPSC-derived astrocytes, we demonstrate that sustained ICP elevation induces bilateral neuroinflammation together with coordinated upregulation of mechanosensitive ion channels and receptor tyrosine kinase (RTK) pathways. Integrative analysis of molecular and physiological datasets identified astrocytes as the principal cellular responders to ICP and revealed epidermal growth factor receptor (EGFR) as the astrocyte-associated RTK most strongly correlated with ICP dynamics, inflammatory chemokine expression, and survival. Pharmacological activation of the mechanosensitive channel Piezo1 in human astrocytes was sufficient to trigger EGFR internalization, site-specific phosphorylation, and ERK signaling, promoting structural remodeling and robust induction of pro-inflammatory mediators including CCL2, IL-6, and IL-8. Conversely, EGFR inhibition attenuated inflammatory signaling while enhancing astrocytic programs associated with water handling and edema containment. In vivo, increased expression of EGFR ligands together with elevated EGFR phosphorylation supported sustained pathway engagement following ASDH, and correlation analyses linked Piezo1 expression and EGFR activation with ICP severity and adverse outcome. Together, these findings define a mechanotransduction axis in which astrocytic Piezo1 signaling integrates mechanical stress with EGFR-dependent neuroimmune responses, positioning EGFR as a translationally accessible target to modulate inflammation-driven secondary brain injury.

neuroscience↗

bioRxiv2022

Comparing models of information transfer in the structural brain network and their relationship to functional connectivity: Diffusion versus shortest path routing

The relationship between structural and functional connectivity in the human brain is a core question in network neuroscience, and a topic of paramount importance to our ability to meaningfully describe and predict functional outcomes. Graph theory has been used to produce measures based on the structural connectivity network that are related to functional connectivity. These measures are commonly based on either the shortest path routing model or the diffusion model, which carry distinct assumptions about how information is transferred through the network. Unlike shortest path routing, which assumes the most efficient path is always known, the diffusion model makes no such assumption, and lets information diffuse in parallel based on the number of connections to other regions. Past research has also developed hybrid measures that use concepts from both models, which have better predicted the functional connectivity from structural connectivity than shortest path length alone. We examined the extent to which each of these models can account for the structure-function relationship of interest using graph theory measures that are exclusively based on each model. This analysis was performed on multiple parcellations of the Human Connectome Project using multiple approaches, which all converged on the same finding. We found that the diffusion model accounts for much more variance in functional connectivity than the shortest path routing model, suggesting that the diffusion model is better suited to describing the structure-function relationship in the human brain at the macroscale.

neuroscience↗

bioRxiv2021

Structure Can Predict Function in the Human Brain: A Graph Neural Network Deep Learning Model of Functional Connectivity and Centrality based on Structural Connectivity

Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. This model applied here to predict functional connectivity and centrality from structural connectivity accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. This model provides a new benchmark for performance and represents a novel finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the models performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for capturing connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).

neuroscience↗

bioRxiv2019

Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal

The complexity of brain activity has recently been investigated using the Hurst (H) exponent, which describes the extent to which functional magnetic resonance imaging (fMRI) blood oxygen-level dependent (BOLD) activity is self-similar vs. complex. For example, research has demonstrated that fMRI activity is more complex before than after consumption of alcohol and during task than resting state. The measurement of H in fMRI is a novel method that requires the investigation of additional factors contributing to complexity. Graph theory metrics of centrality can assess how centrally important to the brain network each region is, based on diffusion tensor imaging (DTI) counts of probabilistic white matter (WM) tracts. DTI derived centrality was hypothesized to account for the complexity of functional activity, based on the supposition that more sources of information to integrate should result in more complex activity. FMRI BOLD complexity as measured by H was associated with five brain region centrality measures: degree, eigenvector, PageRank, current flow betweenness, and current flow closeness centrality. Multiple regression analyses demonstrated that degree centrality was the most robust predictor of complexity, whereby greater centrality was associated with increased complexity (lower H). Regions known to be highly connected, including the thalamus and hippocampus, notably were among the highest in centrality and complexity. This research has led to a greater understanding of how brain region characteristics such as DTI centrality relate to the novel Hurst exponent approach for assessing brain activity complexity, and implications for future research that employ these measures are discussed.

neuroscience↗