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Prion diseases are pathogenically linked to the normal cellular prion protein (PrPC) misfolding into abnormal conformers (PrPSc), with PrPSc accumulation underpinning both transmission and neurotoxicity. Despite achieving this canonical understanding, however fundamental questions remain incompletely resolved, including the level of pathophysiological overlap between neurotoxic and transmitting species of PrPSc and the temporal profiles of their propagation. To further investigate the likely time of occurrence of significant levels of neurotoxic species during prion disease development, the well characterised in vivo M1000 murine model was employed. Following intracerebral inoculation, detailed serial cognitive and ethological testing at specified time points suggested subtle transition to early symptomatic disease from ∼50% of the overall disease course. In addition to observing a chronological order for impaired behaviours, different behavioural tests also showed distinctive profiles of evolving cognitive impairments with the Barnes maze demonstrating a relatively simple linear worsening of spatial learning and memory over an extended period while in contrast a conditioned fear memory paradigm previously untested in murine prion disease demonstrated more complex alterations during disease progression. These observations support the likely production of neurotoxic PrPSc from at least just prior to the mid-point of murine M1000 prion disease and illustrate the likely need to tailor the types of behavioural testing across the time course of disease progression for optimal detection of cognitive deficits. • Behavioural tests can detect cognitive deficit at 50% of prion incubation. • Some tests can follow disease progression better than others. • Barnes test is a useful tool for detecting cognitive deficits. • Fear conditioning response becomes polarized in late-stage of prion infection. [ABSTRACT FROM AUTHOR]

Abstract: After traumatic CNS injury, a cascade of secondary events expands the initial lesion. The gap-junction protein connexin43 (Cx43), which is transiently up-regulated, has been implicated in the spread of ‘bystander’ damage. We have used an antisense oligodeoxynucleotide (asODN) to suppress Cx43 up-regulation in two rat models of spinal cord injury. Within 24 h of compression injury, rats treated with Cx43-asODN scored higher than sense-ODN and vehicle-treated controls on behavioural tests of locomotion. Their spinal cords showed less swelling and tissue disruption, less up-regulation of astrocytic GFAP, and less extravasation of fluorescently-labelled bovine serum albumin and neutrophils. The locomotor improvement was sustained over at least 4 weeks. Following partial spinal cord transection, Cx43-asODN treatment reduced GFAP immunoreactivity, neutrophil recruitment, and the activity of OX42+ microglia in and around the lesion site. Cx43 has many potential roles in the pathophysiology of CNS injury and may be a valuable target for therapeutic intervention. [Copyright &y& Elsevier]

Testing and training animals in motor and related tasks is a cornerstone of pre-clinical behavioural and rehabilitative neuroscience. Yet manually testing and training animals in these tasks is time consuming and analyses are often subjective. Consequently, there have been many recent advances in automating both the administration and analyses of animal behavioural training and testing. This review is an in-depth appraisal of the history of, and recent developments in, the automation of animal behavioural assays used in neuroscience. We describe the use of common locomotor and non-locomotor tasks used for motor training and testing before and after nervous system injury. This includes a discussion of how these tasks help us to understand the underlying mechanisms of neurological repair and the utility of some tasks for the delivery of rehabilitative training to enhance recovery. We propose two general approaches to automation: automating the physical administration of behavioural tasks (i.e., devices used to facilitate task training, rehabilitative training, and motor testing) and leveraging the use of machine learning in behaviour analysis to generate large volumes of unbiased and comprehensive data. The advantages and disadvantages of automating various motor tasks as well as the limitations of machine learning analyses are examined. In closing, we provide a critical appraisal of the current state of automation in animal behavioural neuroscience and a prospective on some of the advances in machine learning we believe will dramatically enhance the usefulness of these approaches for behavioural neuroscientists. • Rehabilitative training in models of neurological disorders is effective but time consuming. • Problems can be mitigated by automation of tasks and analysis. • Common behavioural assays can be automated with sensors, cameras, and robots. • Machine learning in behavioural analysis exploits big data. [ABSTRACT FROM AUTHOR]