Robust representation learning approaches for neural population activity

Understanding communication patterns between different regions of the human brain is key to learning useful spatial representations. Once learned, these representations present a foundation on which new tasks can be learned rapidly. Moreover, the activity patterns generated by the brain are ultimately relayed to the muscles to generate behaviour. By measuring these action potentials from the relevant source regions of the brain directly, we can capture expected behaviour notwithstanding interruption in the neural pathways to downstream muscles. Spinal cord injury is an example of interruption in the case of motor control of arm or leg muscles from the motor cortex of the brain. Multiple electrodes recording action potentials from neurons in the motor cortex in conjunction with a plethora of possible modelling techniques can be used to decode this intended movement. Subsequently, soft or hard robotics can be used to bypass the damaged spinal cord in relaying intended movement behaviour to specific limbs. This thesis is comprised of two main parts. The first part addresses the question of how representation learning in neural networks can benefit the learning of goal-directed behaviour. Using the learning of spatial representations through recurrent neural networks as a model, this work showed that such a representation can be used as a foundation for rapid learning of navigational tasks using reinforcement learning. This learned representation takes the form of spatially modulated units within the neural network, similar to place cells found in the brains of mammals. Furthermore, an analysis of the simulated neurons showed that these place units within the neural network have multiple characteristics replicating those found in biological place cells, such as precursory firing behaviour. The second part tackles the issue of variability in neural representations, a phenomenon that causes significant deterioration of the decoding of behaviour from neural population activity over time. Using combined neural and behaviour recordings from monkeys performing motor tasks, this work aims to develop stable decoders that are robust to such fluctuations. Two approaches using unsupervised learning were investigated. The first is based on domain adaptation, where decoders were trained to "ignore" all aspects of the data subject to fluctuations, and to instead extract the salient, stable aspects of the neural representation of movements. This representation then allows the decoder to generalise well to a completely unseen recording session, thus accurately predicting behaviour intention withstanding significant neuron non-stationaries present between recording sessions. This generalisation to an unseen recording session without retraining or recalibration of a decoder has not been previously shown. This first approach performed well for data that was obtained close enough in time to the training data, but required a significant number of recording sessions for successful training. To address these limitations, a contrastive learning approach was used next. In this model, synthetic variations of trials from a single recording session were generated. These variations were similar in type and magnitude to the neuron non-stationaries that exist between recording sessions, and used as training data together with the original data for a model that learns to remove these non-stationaries to recover stable dynamics related to behaviour. This method produced a very stable decoder capable of accurately inferring intended behaviour for up to a week into the future. This training paradigm is an example of self-supervised learning, whereby the model is trained on perturbed versions of data. Taken together, in this thesis I explore approaches which lead to robust representations being learned within neural networks. These representations are shown to be neurally realistic and robust, allowing for a high degree of generalisation

Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation

Neural population activity relating to behaviour is assumed to be inherently low-dimensional despite the observed high dimensionality of data recorded using multi-electrode arrays. Therefore, predicting behaviour from neural population recordings has been shown to be most effective when using latent variable models. Over time however, the activity of single neurons can drift, and different neurons will be recorded due to movement of implanted neural probes. This means that a decoder trained to predict behaviour on one day performs worse when tested on a different day. On the other hand, evidence suggests that the latent dynamics underlying behaviour may be stable even over months and years. Based on this idea, we introduce a model capable of inferring behaviourally relevant latent dynamics from previously unseen data recorded from the same animal, without any need for decoder recalibration. We show that unsupervised domain adaptation combined with a sequential variational autoencoder, trained on several sessions, can achieve good generalisation to unseen data and correctly predict behaviour where conventional methods fail. Our results further support the hypothesis that behaviour-related neural dynamics are low-dimensional and stable over time, and will enable more effective and flexible use of brain computer interface technologies

Principles and Practice of Humanitarian Communication during and After Natural Disasters and Armed Conflicts

Humanitarian communications has broadly empowered human interaction and mutual understanding within circles and arenas of conflict and disasters. How information is communicated and received during crises is imperative. With peace seriously going on extinction around the world and the growth of countless humanitarian organizations, the need to explore communication is imperative going by the relevance of information, mutual understanding and its knowledge to victims of armed conflict and natural disasters, This paper explained in detail the concept of humanitarian communications, types of humanitarian communications, and how to design effective communication plan for smooth and effective operations of humanitarian actors within humanitarian circle

Targeted Neural Dynamical Modeling

Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously measured behaviour into these models to further disentangle sources of neural variability in their latent space. These approaches, however, are limited in their ability to capture the underlying neural dynamics (e.g. linear) and in their ability to relate the learned dynamics back to the observed behaviour (e.g. no time lag). To this end, we introduce Targeted Neural Dynamical Modeling (TNDM), a nonlinear state-space model that jointly models the neural activity and external behavioural variables. TNDM decomposes neural dynamics into behaviourally relevant and behaviourally irrelevant dynamics; the relevant dynamics are used to reconstruct the behaviour through a flexible linear decoder and both sets of dynamics are used to reconstruct the neural activity through a linear decoder with no time lag. We implement TNDM as a sequential variational autoencoder and validate it on simulated recordings and recordings taken from the premotor and motor cortex of a monkey performing a center-out reaching task. We show that TNDM is able to learn low-dimensional latent dynamics that are highly predictive of behaviour without sacrificing its fit to the neural data

Developing a pressure ulcer risk factor minimum data set and risk assessment framework

AIM: To agree a draft pressure ulcer risk factor Minimum Data Set to underpin the development of a new evidenced-based Risk Assessment Framework.BACKGROUND: A recent systematic review identified the need for a pressure ulcer risk factor Minimum Data Set and development and validation of an evidenced-based pressure ulcer Risk Assessment Framework. This was undertaken through the Pressure UlceR Programme Of reSEarch (RP-PG-0407-10056), funded by the National Institute for Health Research and incorporates five phases. This article reports phase two, a consensus study.DESIGN: Consensus study.METHOD: A modified nominal group technique based on the Research and Development/University of California at Los Angeles appropriateness method. This incorporated an expert group, review of the evidence and the views of a Patient and Public Involvement service user group. Data were collected December 2010-December 2011.FINDINGS: The risk factors and assessment items of the Minimum Data Set (including immobility, pressure ulcer and skin status, perfusion, diabetes, skin moisture, sensory perception and nutrition) were agreed. In addition, a draft Risk Assessment Framework incorporating all Minimum Data Set items was developed, comprising a two stage assessment process (screening and detailed full assessment) and decision pathways.CONCLUSION: The draft Risk Assessment Framework will undergo further design and pre-testing with clinical nurses to assess and improve its usability. It will then be evaluated in clinical practice to assess its validity and reliability. The Minimum Data Set could be used in future for large scale risk factor studies informing refinement of the Risk Assessment Framework

Integration of primary care and palliative care services to improve equality and equity at the end-of-life:Findings from realist stakeholder workshops

Background: Inequalities in access to palliative and end of life care are longstanding. Integration of primary and palliative care has the potential to improve equity in the community. Evidence to inform integration is scarce as research that considers integration of primary care and palliative care services is rare. Aim: To address the questions: ‘how can inequalities in access to community palliative and end of life care be improved through the integration of primary and palliative care, and what are the benefits?’ Design: A theory-driven realist inquiry with two stakeholder workshops to explore how, when and why inequalities can be improved through integration. Realist analysis leading to explanatory context(c)-mechanism(m)-outcome(o) configurations(c) (CMOCs). Findings: A total of 27 participants attended online workshops (July and September 2022): patient and public members (n=6), commissioners (n=2), primary care (n=5) and specialist palliative care professionals (n=14). Most were White British (n=22), other ethnicities were Asian (n=3), Black African (n=1) and British mixed race (n=1). Power imbalances and racism hinder people from ethnic minority backgrounds accessing current services. Shared commitment to addressing these across palliative care and primary care is required in integrated partnerships. Partnership functioning depends on trusted relationships and effective communication, enabled by co-location and record sharing. Positive patient experiences provide affirmation for the multi-disciplinary team, grow confidence and drive improvements. Conclusions: Integration to address inequalities needs recognition of current barriers. Integration grounded in trust, faith and confidence can lead to a cycle of positive patient, carer and professional experience. Prioritising inequalities as whole system concern is required for future service delivery and research. <br/

IPSE, a parasite-derived host immunomodulatory protein, is a potential therapeutic for hemorrhagic cystitis

Chemotherapy-induced hemorrhagic cystitis is characterized by bladder pain and voiding dysfunction caused by hemorrhage and inflammation. Novel therapeutic options to treat hemorrhagic cystitis are needed. We previously reported that systemic administration of the Schistosomiasis haematobium-derived protein H-IPSEH06 (IL-4-inducing principle from Schistosoma mansoni eggs), is superior to 3 doses of MESNA in alleviating hemorrhagic cystitis. Based on prior reports by others on S. mansoni IPSE (M-IPSE) and additional work by our group, we reasoned that H-IPSE mediates its effects on hemorrhagic cystitis by binding IgE on basophils and inducing IL-4 expression, promoting urothelial proliferation, and translocating to the nucleus to modulate expression of genes implicated in relieving bladder dysfunction. We speculated that local bladder injection of the S. haematobium IPSE ortholog IPSEH03, hereafter called H-IPSEH03, might be more efficacious in preventing hemorrhagic cystitis compared to systemic administration of IPSEH06. We report that H-IPSEH03, like M-IPSE and H-IPSEH06, activates IgE-bearing basophils in an NFAT reporter assay, indicating activation of the cytokine pathway. Further, H-IPSEH03 attenuates ifosfamide-induced increases in bladder wet weight in an IL-4-dependent fashion. H-IPSEH03 relieves hemorrhagic cystitis-associated allodynia and modulates voiding patterns in mice. Finally, H-IPSEH03 drives increased urothelial cell proliferation suggesting that IPSE induces bladder repair mechanisms. Taken together, H-IPSEH03 may be a potential novel therapeutic to treat hemorrhagic cystitis by basophil activation, attenuation of allodynia and promotion of urothelial cell proliferation

Export of malaria proteins requires co-translational processing of the PEXEL motif independent of phosphatidylinositol-3-phosphate binding

Acknowledgements We thank the Red Cross blood bank in Melbourne for human erythrocytes. We thank Svenja Gunther for expression of GBP130 66–196 proteins; Michelle Gazdik and Chris Burns for help in preparing lipids; Lachlan Whitehead (Centre for Dynamic Imaging, Walter and Eliza Hall Institute) for assistance with quantification of export; and David Bocher for help with generation of STEVOR constructs. This work was supported by the National Health and Medical Research Council of Australia (grants 637406, 1010326, 1049811 and 1057960), a Ramaciotti Foundation Establishment Grant (3197/2010), a Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS, and the Canadian Institutes for Health Research (MOP#130359). J.A.B is an Australian Research Council QEII Fellow, SF was supported by the Research Training Group GRK1459 of the German Research Foundation, and AFC is a Howard Hughes International Scholar.Peer reviewedPublisher PD