The cellular senescence response and neuroinflammation in juvenile mice following controlled cortical impact and repetitive mild traumatic brain injury.

Traumatic brain injury (TBI) is a leading cause of disability and increases the risk of developing neurodegenerative diseases. The mechanisms linking TBI to neurodegeneration remain to be defined. It has been proposed that the induction of cellular senescence after injury could amplify neuroinflammation and induce long-term tissue changes. The induction of a senescence response post-injury in the immature brain has yet to be characterised. We carried out two types of brain injury in juvenile CD1 mice: invasive TBI using controlled cortical impact (CCI) and repetitive mild TBI (rmTBI) using weight drop injury. The analysis of senescence-related signals showed an increase in γH2AX-53BP1 nuclear foci, p53, p19ARF, and p16INK4a expression in the CCI group, 5 days post-injury (dpi). At 35 days, the difference was no longer statistically significant. Gene expression showed the activation of different senescence pathways in the ipsilateral and contralateral hemispheres in the injured mice. CCI-injured mice showed a neuroinflammatory early phase after injury (increased Iba1 and GFAP expression), which persisted for GFAP. After CCI, there was an increase at 5 days in p16INK4, whereas in rmTBI, a significant increase was seen at 35 dpi. Both injuries caused a decrease in p21 at 35 dpi. In rmTBI, other markers showed no significant change. The PCR array data predicted the activation of pathways connected to senescence after rmTBI. These results indicate the induction of a complex cellular senescence and glial reaction in the immature mouse brain, with clear differences between an invasive brain injury and a repetitive mild injury.

Deep learning for behaviour classification in a preclinical brain injury model.

The early detection of traumatic brain injuries can directly impact the prognosis and survival of patients. Preceding attempts to automate the detection and the assessment of the severity of traumatic brain injury continue to be based on clinical diagnostic methods, with limited tools for disease outcomes in large populations. Despite advances in machine and deep learning tools, current approaches still use simple trends of statistical analysis which lack generality. The effectiveness of deep learning to extract information from large subsets of data can be further emphasised through the use of more elaborate architectures. We therefore explore the use of a multiple input, convolutional neural network and long short-term memory (LSTM) integrated architecture in the context of traumatic injury detection through predicting the presence of brain injury in a murine preclinical model dataset. We investigated the effectiveness and validity of traumatic brain injury detection in the proposed model against various other machine learning algorithms such as the support vector machine, the random forest classifier and the feedforward neural network. Our dataset was acquired using a home cage automated (HCA) system to assess the individual behaviour of mice with traumatic brain injury or non-central nervous system (non-CNS) injured controls, whilst housed in their cages. Their distance travelled, body temperature, separation from other mice and movement were recorded every 15 minutes, for 72 hours weekly, for 5 weeks following intervention. The HCA behavioural data was used to train a deep learning model, which then predicts if the animals were subjected to a brain injury or just a sham intervention without brain damage. We also explored and evaluated different ways to handle the class imbalance present in the uninjured class of our training data. We then evaluated our models with leave-one-out cross validation. Our proposed deep learning model achieved the best performance and showed promise in its capability to detect the presence of brain trauma in mice.