Studies on long term behavioural changes in group-housed rat models of brain and spinal cord injury using an automated home cage recording system

Yip, Ping K.; Chapman, George E.; Sillito, Rowland R.; Ip, Ting Hin Richard; Akhigbe, Georgia; Becker, Stephanie C.; Price, A. W.; Michael‐Titus, Adina T.; Armstrong, J. Douglas; Tremoleda, Jordi L.

doi:10.1016/j.jneumeth.2019.04.005

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…number of animals in the cage, healthy or injured together, both sexes together). Versatile examination of species‐specific behavioural changes and developing ethograms of animals in their home cages might be one future direction for translational research (Mogil, 2009 ; Yip et al., 2019 ). Clinically relevant behaviours related to a given aetiology of pain should also be studied.…”

Section: Translational Pain Researchmentioning

confidence: 99%

Challenges and opportunities in translational pain research – An opinion paper of the working group on translational pain research of the European pain federation (EFIC)

Mouraux¹,

Bannister

Becker

et al. 2021

European Journal of Pain

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For decades, basic research on the underlying mechanisms of nociception has held promise to translate into efficacious treatments for patients with pain. Despite great improvement in the understanding of pain physiology and pathophysiology, translation to novel, effective treatments for acute and chronic pain has however been limited, and they remain an unmet medical need. In this opinion paper bringing together pain researchers from very different disciplines, the opportunities and challenges of translational pain research are discussed. The many factors that may prevent the successful translation of bench observations into useful and effective clinical applications are reviewed, including interspecies differences, limited validity of currently available preclinical disease models of pain, and limitations of currently used methods to assess nociception and pain in non‐human and human models of pain. Many paths are explored to address these issues, including the backward translation of observations made in patients and human volunteers into new disease models that are more clinically relevant, improved generalization by taking into account age and sex differences, and the integration of psychobiology into translational pain research. Finally, it is argued that preclinical and clinical stages of developing new treatments for pain can be improved by better preclinical models of pathological pain conditions alongside revised methods to assess treatment‐induced effects on nociception in human and non‐human animals. Significance: For decades, basic research of the underlying mechanisms of nociception has held promise to translate into efficacious treatments for patients with pain. Despite great improvement in the understanding of pain physiology and pathophysiology, translation to novel, effective treatments for acute and chronic pain has however been limited, and they remain an unmet medical need.

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Section: Translational Pain Researchmentioning

confidence: 99%

Challenges and opportunities in translational pain research – An opinion paper of the working group on translational pain research of the European pain federation (EFIC)

Mouraux¹,

Bannister

Becker

et al. 2021

European Journal of Pain

View full text Add to dashboard Cite

show abstract

“…Recent developments in preclinical animal studies have suggested a more accessible alternative of investigating the impact of brain injuries using non invasive and continuous monitoring of individual behavioural changes in grouped housed animal cohorts whilst in their home cages [ 19 ]. Data obtained from such home cage automated (HCA) monitored behaviour provides large and comprehensive activity and physiological individual data from grouped housed animals, overcoming the constraints towards CNNs imposed by imaging modalities and therefore allows for more flexibility in data processing and model architecture.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for behaviour classification in a preclinical brain injury model

et al. 2022

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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.

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Innovative models for in vitro detection of seizure

Rockley¹,

Roberts

Morton³

2019

Toxicology Research

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Data show that toxicity to the central nervous system (CNS) is the most frequent cause of safety failures during the clinical phase of drug development. CNS endpoints such as seizure pose a safety risk to patients and volunteers and can lead to a loss of competitiveness, delays, and increased costs. Current methods rely on detection in the nonclinical rodent and non-rodent studies required to support clinical trials. There are two main issues with this approach; seizure may be missed in the animal studies and, even if seizure is detected, significant resource has already been invested in the project by this stage. Thus, there is a need to develop improved screening methods that can be used earlier in drug discovery to predict seizure. Advances in stem cell biology coupled with an increased understanding of the role of ion channels in seizure offer an opportunity for a new paradigm in screening. Human derived induced pluripotent stem cells (hiPSCs) representative of almost all cellular subtypes present in the brain can be incorporated into physiologically relevant in vitro models that can be used to determine seizure risk using high-throughput methods. Akin to the success of screening against a panel of ion channels such as hERG to reduce cardiovascular safety liability, the involvement of ion channels in seizure suggests that a similar approach to early seizure detection is valid. Profiling of the ion channels expressed in hiPSC models showing the seizurogenic phenotype coupled with electrophysiological assessment of ion channel function could translate into an ion channel seizure panel for rapid and reliable in vitro detection of seizure. The mechanistic information gathered would support optimal drug design early in development before resources, animals and time have been wasted.

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Studies on long term behavioural changes in group-housed rat models of brain and spinal cord injury using an automated home cage recording system

Abstract: long term behavioural changes in group-housed rat models of brain and spinal cord injury using an automated home cage recording system',

Cited by 6 publications

References 44 publications

Challenges and opportunities in translational pain research – An opinion paper of the working group on translational pain research of the European pain federation (EFIC)

Challenges and opportunities in translational pain research – An opinion paper of the working group on translational pain research of the European pain federation (EFIC)

Deep learning for behaviour classification in a preclinical brain injury model

Innovative models for in vitro detection of seizure

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