Consumers lived experiences and satisfaction with sub-acute mental health residential services.

PURPOSE: Sub-acute recovery-oriented facilities offer short-term residential support for people living with mental illness. They are generally highly regarded by consumers, with emerging evidence indicating that these services may support recovery. The aim of the current study was to explore the relationship between personal recovery and consumers' satisfaction with sub-acute residential services, and consumers' views about service features that aid recovery. METHODS: Consumers at 19 adult Prevention and Recovery Care Services in Victoria, Australia, were invited to complete measures containing sociodemographic information and measures on personal recovery and wellbeing. After going home, participants were invited to complete measures on service satisfaction and experience. RESULTS: Total and intrapersonal scores on the personal recovery measure increased significantly between Time 1 and Time 2, indicating marked improvement. Personal recovery and satisfaction measures were moderately to strongly correlated. Thematically analysed open-ended responses revealed themes of feeling connected, finding meaning and purpose, and self-empowerment as important aspects of these services, with some recommendations for improvements. CONCLUSION: Sub-acute residential mental health care may support individuals' personal recovery; consumer satisfaction indicates these services also offer an acceptable and supportive environment for the provision of recovery-oriented care. Further exploring consumers' experiences of sub-acute residential services is essential to understand their effectiveness, opportunities for improvement and intended impacts on personal recovery.

Discovering the pharmacodynamics of conolidine and cannabidiol using a cultured neuronal network based workflow.

Determining the mechanism of action (MOA) of novel or naturally occurring compounds mostly relies on assays tailored for individual target proteins. Here we explore an alternative approach based on pattern matching response profiles obtained using cultured neuronal networks. Conolidine and cannabidiol are plant-derivatives with known antinociceptive activity but unknown MOA. Application of conolidine/cannabidiol to cultured neuronal networks altered network firing in a highly reproducible manner and created similar impact on network properties suggesting engagement with a common biological target. We used principal component analysis (PCA) and multi-dimensional scaling (MDS) to compare network activity profiles of conolidine/cannabidiol to a series of well-studied compounds with known MOA. Network activity profiles evoked by conolidine and cannabidiol closely matched that of ω-conotoxin CVIE, a potent and selective Cav2.2 calcium channel blocker with proposed antinociceptive action suggesting that they too would block this channel. To verify this, Cav2.2 channels were heterologously expressed, recorded with whole-cell patch clamp and conolidine/cannabidiol was applied. Remarkably, conolidine and cannabidiol both inhibited Cav2.2, providing a glimpse into the MOA that could underlie their antinociceptive action. These data highlight the utility of cultured neuronal network-based workflows to efficiently identify MOA of drugs in a highly scalable assay.

Use of adaptive network burst detection methods for multielectrode array data and the generation of artificial spike patterns for method evaluation.

OBJECTIVE: Multielectrode arrays are an informative extracellular recording technology that enables the analysis of cultured neuronal networks and network bursts (NBs) are a dominant feature observed in these recordings. This paper focuses on the validation of NB detection methods on different network activity patterns and developing a detection method that performs robustly across a wide variety of activity patterns. APPROACH: A firing rate based approach was used to generate artificial spike timestamps where NBs were introduced as episodes where the probability of spiking increases. Variations in firing and bursting characteristics were also included. In addition, an improved methodology of detecting NBs is proposed, based on time-binned average firing rates and time overlaps of single channel bursts. The robustness of the proposed method was compared against three existing algorithms using simulated, publicly available and newly acquired data. MAIN RESULTS: A range of activity patterns were generated by changing simulation variables that correspond to NB duration (40-2200 ms), intervals (0.3-16 s), firing rates (0.1-1 spikes s(-1)), local burst percentage (0%-90%), number of channels in local bursts (20-40) as well as the number of tonic and frequently-bursting channels. By extracting simulation parameters directly from real data, we generated synthetic data that closely resemble activity of mouse and rat cortical cultures at native and chemically perturbed states. In 50 simulated data sets with randomly selected parameter values, the improved NB detection method performed better (ascertained by the f-measure) than three existing methods (p < 0.005). The improved method was also able to detect clustered, long-tailed and short-frequent NBs on real data. SIGNIFICANCE: This work presents an objective method of assessing the applicability of NB detection methods for different neuronal activity patterns. Furthermore, it proposes an improved NB detection method that can be used robustly across a range of data types.

Use of local field potentials of dissociated cultures grown on multi-electrode arrays for pharmacological assays.

In vitro Multi-Electrode Arrays (MEA) are an extracellular recording technology that enables the analysis of networks of neurons in vitro. Neurons in culture exhibit a range of behavioral dynamics, which can be measured in terms of individual action potentials, network-wide synchronized firing and a host of other features that characterize network activity. MEA data analysis was historically focused on high frequency spike data forgoing the low frequency content of the signal. In this study, we use local field potentials, which are low frequency components of MEA signals, to differentiate between two types of antiepileptic drugs (p<;0.0001) with different mechanisms of action.