Reflex syncope : an integrative physiological approach

Síncope, a forma mais comum de perda temporária de consciência é responsável por até 5% das idas aos serviços de emergência e até 3% dos internamentos hospitalares. É um problema médico frequente, com múltiplos gatilhos, incapacitante, potencialmente perigoso e desafiante em termos diagnósticos e terapêuticos. Assim, é necessária uma anamnese detalhada para primeiro estabelecer a natureza da perda de consciência, mas, após o diagnóstico, as medidas terapêuticas existentes são pouco eficazes. Embora a fisiopatologia da síncope vasovagal ainda não tenha sido completamente esclarecida, alguns mecanismos subjacentes foram já desvendados. Em última análise, a síncope depende de uma falha transitória na perfusão cerebral pelo que qualquer factor que afecte a circulação sanguínea cerebral pode determinar a ocorrência de síncope. Assim, o objectivo do presente estudo é caracterizar o impacto hemodinâmico e autonómico nos mecanismos subjacentes à síncope reflexa, para melhorar o diagnóstico, o prognóstico e a qualidade de vida dos doentes e dos seus cuidadores. Para isso, desenhámos e implementámos novas ferramentas matemáticas e computacionais que permitem uma avaliação autonómica e hemodinâmica integrada, de forma a aprofundar a compreensão do seu envolvimento nos mecanismos de síncope reflexa. Além disso, refinando a precisão do diagnóstico, a sensibilidade e a especificidade do teste de mesa de inclinação (“tilt test”), estabelecemos uma ferramenta preditiva do episódio iminente de síncope. Isso permitiu-nos estabelecer alternativas de tratamento eficazes e personalizadas para os doentes refractários às opções convencionais, sob a forma de um programa de treino de ortostatismo (“tilt training”), contribuindo para o aumento da sua qualidade de vida e para a redução dos custos directos e indirectos da sua assistência médica. Assim, num estudo verdadeiramente multidisciplinar envolvendo doentes com síncope reflexa refractária à terapêutica, conseguimos demonstrar uma assincronia funcional das respostas reflexas autonómicas e hemodinâmicas, expressas por um desajuste temporal entre o débito cardíaco e as adaptações de resistência total periférica, uma resposta baroreflexa atrasada e um desequilíbrio incremental do tónus autonómico que, em conjunto, poderão resultar de uma disfunção do sistema nervoso autónomo que se traduz por uma reserva simpática diminuída. Igualmente, desenhámos, testámos e implementámos uma plataforma computacional e respectivo software associado - a plataforma FisioSinal –incluindo novas formas, mais dinâmicas, de avaliação integrada autonómica e hemodinâmica, que levaram ao desenvolvimento de algoritmos preditivos para a estratificação de doentes com síncope. Além disso, na aplicação dessas ferramentas, comprovámos a eficácia de um tratamento não invasivo, não disruptivo e integrado, focado na neuromodulação das variáveis autonómicas e cardiovasculares envolvidas nos mecanismos de síncope. Esta terapêutica complementar levou a um aumento substancial da qualidade de vida dos doentes e à abolição dos eventos sincopais na grande maioria dos doentes envolvidos. Em conclusão, o nosso trabalho contribuiu para preencher a lacuna entre a melhor informação científica disponível e sua aplicação na prática clínica, sustentando-se nos três pilares da medicina translacional: investigação básica, clínica e comunidade.Syncope, the most common form of transient loss of consciousness, accounts for up to 5% of emergency room visits and up to 3% of hospital admissions. It is a frequent medical problem with multiple triggers, potentially dangerous, incapacitating, and challenging to diagnose. Therefore, a detailed clinical history is needed first to establish the nature of the loss of consciousness. However, after diagnosis, the therapeutic measures available are still very poor. Although the exact pathophysiology of vasovagal syncope remains to be clarified, some underlying mechanisms have been unveiled, dependent not only on the cause of syncope but also on age and various other factors that affect clinical presentation. Ultimately, syncope depends on a failure of the circulation to perfuse the brain, so any factor affecting blood circulation may determine syncope occurrence. Thus, the purpose of the present study is to understand the impact of the hemodynamic and autonomic functions on reflex syncope mechanisms to improve patients diagnose, prognosis and general quality of life. Bearing that in mind, we designed and implemented new mathematical and computational tools for autonomic and hemodynamic evaluation, in order to deepen the understanding of their involvement in reflex syncope mechanisms. Furthermore, by refining the diagnostic accuracy, sensitivity and specificity of the head-up tilt-table test, we established a predictive tool for the impending syncopal episode. This allowed us to establish effective and personalised treatment alternatives to patient’s refractory to conventional options, contributing to their increase in the quality of life and a reduction of health care and associated costs. In accordance, in a truly multidisciplinary study involving reflex syncope patients, we were able to show an elemental functional asynchrony of hemodynamic and autonomic reflex responses, expressed through a temporal mismatch between cardiac output and total peripheral resistance adaptations, a deferred baroreflex response and an unbalanced, but incremental, autonomic tone, all contributing to autonomic dysfunction, translated into a decreased sympathetic reserve. Through the design, testing and implementation of a computational platform and the associated software - FisioSinal platform -, we developed novel and dynamic ways of autonomic and hemodynamic evaluation, whose data lead to the development of predictive algorithms for syncope patients’risk stratification. Furthermore, through the application of these tools, we showed the effectiveness of a non-invasive, non-disruptive and integrated treatment, focusing on neuromodulation of the autonomic and cardiovascular variables involved in the syncope mechanisms, leading to a substantial increase of quality of life and the abolishment of syncopal events in a vast majority of the enrolled patients. In conclusion, our work contributed to fill the gap between the best available scientific information and its application in the clinical practice by tackling the three pillars of translational medicine: bench-side, bedside and community

Distinct forms of depression and somatization following head injury: A neuropsychological framework and exploratory treatment paradigm for somatic symptoms and executive dysfunction following mild head injury

Psychiatric symptoms following traumatic brain injury (TBI) pose a significant barrier to neurorehabilitation and impact survivor’s life satisfaction following injury. Depressive and somatization symptoms are common clinical presentations postinjury; however, due to the paucity of etiological models to explain these symptoms, treatment approaches are predominately “borrowed” from non-neurally compromised populations with similar clinical presentations. The orbitofrontal cortex (OFC) is particularly vulnerable in TBI, and serves to modulate autonomic arousal states. Varying severities of TBI have been linked to autonomic underarousal as measured by electrodermal activation (EDA). In a series of studies examining persons with mild head injury (MHI), the phenomenological presentation of depressive and somatization symptoms was examined in persons with and without MHI, and the relationship between these symptoms and autonomic underarousal was explored. In study one, MHI were found to be autonomically underaroused, reporting more somatic depressive symptoms relative to their no-MHI cohort, and the relationship between their injury severity and the intensity of their somatic depressive complaints was completely mediated by underarousal. Investigating somatization revealed MHI status as a moderator of the relationship between somatization and post-concussive symptoms, with MHI having a stronger positive association. Autonomic underarousal was found to be a complete mediator between the relationship between injury severity and their somatization symptoms. In study two, we experimentally manipulated autonomic arousal through brief cardiovascular exercise and evaluated whether this concomitantly improved somatic-based psychiatric complaints and neurocognitive functioning in persons with MHI. Study two replicated the somatic depressive mediation model of study one, and revealed that the experimental manipulation was effective in increasing autonomic arousal, and improving somatic-psychiatric complaints and neurocognitive status. Collectively, these findings suggest that depressive and somatization symptoms postinjury are phenomenologically and etiologically different in persons with a history of head injury relative to their non-neurally compromised counterpart, and autonomic underarousal and OFC dysfunction is a strong candidate for continued investigation as an etiological model for psychiatric symptoms postinjury. Reversal of underarousal may serve as an important therapeutic goal. Lastly, we propose the term “somatic underarousal” to describe this symptomatology as a means to avoid confusion with the historical roots of the term somatization

Philosophical foundations of neuroeconomics: economics and the revolutionary challenge from neuroscience.

This PhD thesis focuses on the philosophical foundations of Neuroeconomics, an innovative research program which combines findings and modelling tools from economics, psychology and neuroscience to account for human choice behaviour. The proponents of Neuroeconomics often manifest the ambition to foster radical modifications in the accounts of choice behaviour developed by its parent disciplines. This enquiry provides a philosophically informed appraisal of the potential for success and the relevance of neuroeconomic research for economics. My central claim is that neuroeconomists can help other economists to build more predictive and explanatory models, yet are unlikely to foster revolutionary modifications in the economic theory of choice. The contents are organized as follows. In chapters 1-2, I present neuroeconomists’ investigative tools, distinguish the most influential approaches to neuroeconomic research and reconstruct the case in favour of a neural enrichment of economic theory. In chapters 3-7, I combine insights from neuro-psychology, economic methodology and philosophy of science to develop a systematic critique of Neuroeconomics. In particular, I articulate four lines of argument to demonstrate that economists are provisionally justified in retaining a methodologically distinctive approach to the modelling of decision making. My first argument points to several evidential and epistemological concerns which complicate the interpretation of neural data and cast doubt on the inferences neuroeconomists often make in their studies. My second argument aims to show that the trade-offs between the modelling desiderata that neuroeconomists and other economists respectively value severely constrain the incorporation of neural insights into economic models. My third argument questions neuroeconomists’ attempts to develop a unified theory of choice behaviour by identifying some central issues on which they hold contrasting positions. My fourth argument differentiates various senses of the term ‘revolution’ and illustrates that neuroeconomists are unlikely to provide revolutionary contributions to economic theory in any of these senses

Pressure support ventilation or synchronised intermittent mandatory ventilation for weaning premature babies on mechanical ventilation : a multi centre randomised controlled trial

PhD ThesisMechanical ventilation is life saving as a respiratory support for preterm infants with respiratory distress syndrome. There is good evidence now that any form of volume-targeted modality of mechanical ventilation is superior over pressure-targeted modality to reduce chronic lung disease and death. It is perceived by minimising the duration of mechanical ventilation would reduce the exposure to positive pressure breaths and thereby could reduce long term morbidities such as chronic lung disease. An area of lacunae is defining what is weaning on mechanical ventilation. Whilst most clinicians will agree when to commence mechanical ventilation there is paucity of consensus on when to commence weaning on mechanical ventilation and the best way for weaning to prevent extubation failure. Pressure support ventilation (PSV) is pressure-targeted modality of ventilation designed to support spontaneous breathing. It was designed as a weaning mode to facilitate extubation. Pure PSV has no back up rate. Currently, PSV is used in combination with other modes such as SIMV to provide some back up respiratory rate for the unreliable respiratory drive due to apnoea in preterm infants. However, there is inadequate understanding of the appropriate PSV level for weaning preterm infants on mechanical ventilation. Clinicians routinely use 50%-70% of peak inflation pressures used prior to commencing the weaning mode. Use of Pressure support ventilation (PSV) could be variable- with one extreme utilising minimal pressure to just overcome the tube resistance (PSmin) with the aim to prevent fatigue and avoid extubation failure. The other extreme is augmenting spontaneous breathing effort to provide a full tidal volume breath (PSmax). Features of flow triggering and flow cycling aid synchrony at inspiration and expiration and this allows greater autonomy to the infant to control all aspects of its breathing cycle. Addition of some PSV to aid spontaneous breaths has shown to reduce the duration of weaning. A randomised controlled study was designed to compare duration of weaning using PSmax and SIMV. Infants less than 32 weeks gestation at birth with respiratory distress syndrome from surfactant deficiency were eligible to participate. 93 infants stratified in three groups based on their gestation at birth were randomised over 30-month period. Weaning was commenced in the randomised mode when infants reached a set priori of MAP<10 cm H2O, FiO2 <40% and had a reliable respiratory drive for at least 2 consecutive hours. In the control arm (SIMV with PSmin)– clinicians reduced the back up rate to wean. In the intervention arm (PSmax with ten SIMV breaths)- clinicians reduced the PSVmax to PSVmin for weaning. A minute ventilation test was performed to assess readiness to extubation when both arms reached PSmin with ten back up SIMV breaths. Primary outcome for the study was duration of weaning on mechanical ventilation. Our study suggests there is no difference between the two groups but there is a trend towards faster extubation in the PSV arm (the median time to extubate in the SIMV arm was 42 (95%CI, 28.23 to 55.76) hours and the median time to achieve the primary outcome in the PSV arm was 31 (95% CI, 12.59 to 49.40) hours). The survival distribution between the interventions was statistically not significant, Chi-square 0.768, p 0.381. This effect was more evident in bigger infants weighing at least 1500 grams. There was no difference in the secondary outcomes between the two groups and common preterm morbidities were equally balanced. There were no adverse events during the study period to report. Contrary to the general belief, infants are not disadvantaged by weaning on PSVmax. Clinical outcomes were comparable with the traditional SIMV method of weaning on mechanical ventilation

Good things come to those who weight: evidence integration and decision termination in human choices

Perceptual decision-making describes the processes by which sensory information is recognised, evaluated and combined before making a commitment to a course of action. The goal of this thesis is to understand the neural and computational mechanisms underlying human perceptual decisions. Good decisions are made when all the available evidence is taken into account, and allowed to influence choice in proportion to its reliability. The first experimental chapter describes a categorisation task employed to investigate how information is integrated and employed according to its reliability during sequential sampling. It is observed that humans weight information approximately optimally. A subsequent experiment involving electroencephalographic (EEG) recordings elucidates a neurobiologically plausible mechanism that could give rise to this effect. However, reliability-based evidence integration may only be possible in relatively simple decisions, when task demands are lower. Previous work investigating more challenging decisions has shown that when two alternatives are viewed in series, locally preferred alternatives are processed with higher gain (“selective integration”). Experiment 2 asks (at both the behavioural and neural level) whether this selective integration happens at the level of attributes - i.e. category A versus B - or features - i.e. sub-dimensions of each of the attributes. Finding that it occurs at the level of features, we discuss the optimality of this strategy. We show, interestingly, that whilst selective integration at the feature level is not harmful to performance, only attribute-level selectivity is actively beneficial in this context. In everyday settings, the choice to stop integrating evidence and commit is often determined by the agent, rather than an external deadline. Experiment 3 uses a self-paced categorisation task to investigate what factors predict when decisions are made. The results show that decisions and their latencies are described by a quasi-optimal model, that times commitment in a way that depends on the evidence consistency. We show that an approximation based on normalisation can account for these findings at the computational level. This model predicts neural signals observed in humans

Cognitive Mechanisms and Computational Models: Explanation in Cognitive Neuroscience

Cognitive Neuroscience seeks to integrate cognitive psychology and neuroscience. I critique existing analyses of this integration project, and offer my own account of how it ought to be understood given the practices of researchers in these fields. A recent proposal suggests that integration between cognitive psychology and neuroscience can be achieved `seamlessly' via mechanistic explanation. Cognitive models are elliptical mechanism sketches, according to this proposal. This proposal glosses over several difficulties concerning the practice of cognitive psychology and the nature of cognitive models, however. Although psychology's information-processing models superficially resemble mechanism sketches, they in fact systematically include and exclude different kinds of information. I distinguish two kinds of information-processing model, neither of which specifies the entities and activities characteristic of mechanistic models, even sketchily. Furthermore, theory development in psychology does not involve the filling in of these missing details, but rather refinement of the sorts of models they start out as. I contrast the development of psychology's attention filter models with the development of neurobiology's models of sodium channel filtering. I argue that extending the account of mechanisms to include what I define as generic mechanisms provides a more promising route towards integration. Generic mechanisms are the in-the-world counterparts to abstract types. They thus have causal-explanatory powers which are shared by all the tokens that instantiate that type. This not only provides a way for generalizations to factor into mechanistic explanations, which allows for the `upward-looking' explanations needed for integrating cognitive models, but also solves some internal problems in the mechanism literature concerning schemas and explanatory relevance. I illustrate how generic mechanisms are discovered and used with examples from computational cognitive neuroscience. I argue that connectionist models can be understood as approximations to generic brain mechanisms, which resolves a longstanding philosophical puzzle as to their role. Furthermore, I argue that understanding scientific models in general in terms of generic mechanisms allows for a unified account of the types of inferences made in modeling and in experiment

Intelligent technologies for the aging brain: opportunities and challenges

Intelligent computing is rapidly reshaping healthcare. In light of the global burden of population aging and neurological disorders, dementia and elderly care are among the healthcare sectors that are most likely to benefit from this technological revolution. Trends in artificial intelligence, robotics, ubiquitous computing, neurotechnology and other branches of biomedical engineering are progressively enabling novel opportunities for technology-enhanced care. These Intelligent Assistive Technologies (IATs) open the prospects of supporting older adults with neurocognitive disabilities, maintain their independence, reduce the burden on caregivers and delay the need for long-term care (1, 2). While technology develops fast, yet little knowledge is available to patients and health professionals about the current availability, applicability, and capability of existing IATs. This thesis proposes a state-of-the-art analysis of IATs in dementia and elderly care. Our findings indicate that advances in intelligent technology are resulting in a rapidly expanding number and variety of assistive solutions for older adults and people with neurocognitive disabilities. However, our analysis identifies a number of challenges that negatively affect the optimal deployment and uptake of IATs among target users and care institutions. These include design issues, sub-optimal approaches to product development, translational barriers between lab and clinics, lack of adequate validation and implementation, as well as data security and cyber-risk weaknesses. Additionally, in virtue of their technological novelty, intelligent technologies raise a number of Ethical, Legal and Social Implications (ELSI). Therefore, a significant portion of this thesis is devoted to providing an early ethical Technology Assessment (eTA) of intelligent technology, hence contributing to preparing the terrain for its safe and ethically responsible adoption. This assessment is primarily focused on intelligent technologies at the human-machine interface, as these applications enable an unprecedented exposure of the intimate dimension of individuals to the digital infosphere. Issues of privacy, integrity, equality, and dual-use were addressed at the level of stakeholder analysis, normative ethics and human-rights law. Finally, this thesis is aimed at providing evidence-based recommendations for guiding participatory and responsible development in intelligent technology, and delineating governance strategies that maximize the clinical benefits of IATs for the aging world, while minimizing unintended risks

Artificial intelligence versus human intelligence in anaesthesia: who is winning?

Artificial intelligence (AI) plays a significant role especially in the light of the COVID-19 pandemic. The position of anaesthesiologists and their role in providing anaesthetic services initially was dominant. The AI ability to overtake the human’s capability in providing an accurate medical treatment may threaten the role of a doctor. The integration of AI in anaesthesia has been tremendous. Challenges in using this technology in anaesthesia are to determine, design, test the practicality, maintain dynamicity and market the technology. In the future, we hope AI may become the strongest weapon for anaesthesiologists to deliver the best anaesthesia services to patients and not as an enem

Dynamics of Information Distribution on Social Media Platforms during Disasters

abstract: When preparing for and responding to disasters, humanitarian organizations must run effective and efficient supply chains to deliver the resources needed by the affected population. The management of humanitarian supply chains include coordinating the flows of goods, finances, and information. This dissertation examines how humanitarian organizations can improve the distribution of information, which is critical for the planning and coordination of the other two flows. Specifically, I study the diffusion of information on social media platforms since such platforms have emerged as useful communication tools for humanitarian organizations during times of crisis. In the first chapter, I identify several factors that affect how quickly information spreads on social media platforms. I utilized Twitter data from Hurricane Sandy, and the results indicate that the timing of information release and the influence of the content’s author determine information diffusion speed. The second chapter of this dissertation builds directly on the first study by also evaluating the rate at which social media content diffuses. A piece of content does not diffuse in isolation but, rather, coexists with other content on the same social media platform. After analyzing Twitter data from four distinct crises, the results indicate that other content’s diffusion often dampens a specific post’s diffusion speed. This is important for humanitarian organizations to recognize and carries implications for how they can coordinate with other organizations to avoid inhibiting the propagation of each other’s social media content. Finally, a user’s followers on social media platforms represent the user’s direct audience. The larger the user’s follower base, the more easily the same user can extensively broadcast information. Therefore, I study what drives the growth of humanitarian organizations’ follower bases during times of normalcy and emergency using Twitter data from one week before and one week after the 2016 Ecuador earthquake.Dissertation/ThesisDoctoral Dissertation Business Administration 201

The neuro-computational role of uncertainty in anxiety

Anxiety disorders are the most common mental health disorders and comprise a large number of years lost to disability. The work in this thesis is oriented towards understanding anxiety using a computational approach, focusing on uncertainty estimation as a key process. Chapter 1 introduces the role of uncertainty within anxiety and motivates the subsequent experimental chapters. Chapter 2 is a review of the computational role of the amygdala in humans, a key area for uncertainty computation. Chapter 3 is an experimental chapter which aimed to address gaps in the literature highlighted in the preceding chapters, namely the link between sensory uncertainty processing and anxiety and the role of the amygdala in this process. This chapter focuses on the development of a novel computational hierarchical Bayesian model to quantify sensory uncertainty and its application to neuroimaging data, with intolerance of uncertainty relating to greater neural activation in the insula but not amygdala. Chapter 4 targets the computational mechanisms underlying the negative self-bias observed in subclinical social anxiety. Again, this chapter focuses on the development of novel computational belief-update models which explicitly model uncertainty. Here, we see that a reduced trait self-positivity underpins this negative social evaluation process. The final experimental chapter presented in Chapter 5 investigates the link between different computational mechanisms, such as uncertainty, and a range of mood and anxiety symptomatology. This study revealed cognitive, social and somatic computational profiles that share a threat bias mechanism but have distinct negative-self bias and aversive learning signatures. Contrary to expectations, none of the uncertainty measures showed any associations with anxiety symptom subtypes. Finally, chapter 6 brings together the work in this thesis and alongside limitations of the work, discusses how these experiments contribute to our understanding of anxiety and the role of uncertainty across the anxiety spectrum