bioRxiv Science⌕ Search

bioRxiv · 10.1101/2020.10.05.325514

Spatial distribution of neuropathology and neuroinflammation elucidate the biomechanics of fluid percussion injury

Abstract

Diffuse brain injury is better described as multi-focal, where pathology can be found adjacent to seemingly uninjured neural tissue. In experimental diffuse brain injury, pathology and pathophysiology have been reported far more lateral than predicted by the impact site. Finite element biomechanical models of diffuse brain injury predict regions of maximum stress and strain. However, the application of a skull with uniform thickness may mask the pathophysiology due to varying thickness of human and animal skulls. Force applied to the intact skull would diffuse the forces, whereas forces applied through an open skull are distributed along paths of least resistance within and then exiting the skull. We hypothesized that the local thickening of the rodent skull at the temporal ridges serves to focus the intracranial mechanical forces experienced during brain injury and generate predictable pathology in underlying cortical tissue. We demonstrated local thickening of the skull at the temporal ridges using contour analysis of coronal skull sections and oblique sectioning on MRI. After diffuse brain injury induced by midline fluid percussion injury (mFPI), pathological foci along the anterior-posterior length of cortex under the temporal ridges were evident acutely (1, 2, 7 days) and chronically (28 days) post-injury by deposition of argyophilic reaction product. Area CA3 of the hippocampus and lateral nuclei of the thalamus showed pathological change, suggesting that mechanical forces to or from the temporal ridges shear subcortical regions. A proposed model of mFPI biomechanics suggests that injury force vectors reflect off the skull base and radiate toward the temporal ridge due to the material properties of the skull based on thickness, thereby injuring ventral thalamus, dorsolateral hippocampus, and sensorimotor cortex. Surgically thinning the temporal ridge prior to injury reduced the injury-induced inflammation in sensorimotor cortex. These data build evidence for the temporal ridges of the rodent skull to contribute to the observed pathology, whether by focusing extracranial forces to enter the cranium or intracranial forces to escape the cranium. Pre-clinical investigations can take advantage of the predicted pathology to explore injury mechanisms and treatment efficacy. HighlightsO_LIThe temporal ridge is 75% thicker than the adjacent skull of the rodent C_LIO_LIExperimental diffuse TBI neuropathology occurs beneath the length of the temporal ridge C_LIO_LINeuropathology encompasses sensorimotor cortex, somatosensory thalamus, and dorsolateral hippocampus C_LIO_LIProposed mechanism of biomechanical injury forces include the temporal ridge C_LI

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Beitchman, J. A., Lifshitz, J., Harris, N. G., Thomas, T. C., Lafrenaye, A. D., Hanell, A., Dixon, C. E., Povlishock, J. T., Rowe, R. K.. 2020-10-07. Spatial distribution of neuropathology and neuroinflammation elucidate the biomechanics of fluid percussion injury. https://doi.org/10.1101/2020.10.05.325514

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗