Identifying the Challenges in Reducing Latency in GSN using Predictors

Simulations based on real-time data continuously gathered from sensor networks all over the world have received growing attention due to the increasing availability of measured data. Furthermore, predictive techniques have been employed in the realm of such networks to reduce communication for energy-efficiency. However, research has focused on the high amounts of data transferred rather than latency requirements posed by the applications. We propose using predictors to supply data with low latency as required for accurate simulations. This paper investigates requirements for a successful combination of these concepts and discusses challenges that arise

Data Mining and Analysis on Multiple Time Series Object Data

Huge amount of data is available in our society and the need for turning such data into useful information and knowledge is urgent. Data mining is an important field addressing that need and significant progress has been achieved in the last decade. In several important application areas, data arises in the format of Multiple Time Series Object (MTSO) data, where each data object is an array of time series over a large set of features and each has an associated class or state. Very little research has been conducted towards this kind of data. Examples include computational toxicology, where each data object consists of a set of time series over thousands of genes, and operational stress management, where each data object consists of a set of time series over different measuring points on the human body. The purpose of this dissertation is to conduct a systematic data mining study over microarray time series data, with applications on computational toxicology. More specifically, we aim to consider several issues: feature selection algorithms for different classification cases, gene markers or feature set selection for toxic chemical exposure detection, toxic chemical exposure time prediction, wildness concept development and applications, and organizing diversified and parsimonious committee. We will formalize and analyze these research problems, design algorithms to address these problems, and perform experimental evaluations of the proposed algorithms. All these studies are based on microarray time series data set provided by Dr. McDougal

The impact of vestibular modulations on whole brain structure and function in humans

The vestibular system is a sensory system that monitors active and passive headmovements while at the same time permanently sensing gravity. Vestibular information is important for maintaining balance and stabilisation of vision and ultimately for general orientation in space. A distributed set of cortical vestibular regions process vestibular sensory information, together with other sensory and motor signals. How these brain regions are influenced by or interact with each other, and how this depends on the context in which the system is acting is not well understood. In my research I investigated the whole brain consequences of different vestibular sensory contexts by means of structural and functional magnetic resonance (MR) imaging on three different time scales (long-term, short-term, and medium-term). For the long-term time scale, I investigated functional brain connectivity in individuals experiencing a type of chronic dizziness that cannot be explained by structural damage within the nervous system. These patients exhibit chronic or long-term alterations in their processing of vestibular information, which leads to dizziness and vertigo. I found altered sensory and cerebellar network connectivity when they experience a dizziness-provoking stimulus. These two networks contain, but are not limited to, vestibular processing regions, demonstrating the importance of a whole-brain approach. The alterations correspond the notion that these patients have dysfunctional stimulus expectations. The short-term vestibular processing I investigated was the effect of artificial vestibular stimulation, which is frequently used in vestibular research and treatment. For this, I analysed functional network connectivity in healthy participants. I found that short-term vestibular stimulation does not cause a cortical functional reorganisation, although a nociceptive stimulus, which was matched for the somatosensory component of this stimulation, led to a reorganisation. The fact that cortical reorganisation does not occur during exclusively vestibular stimulation may reflect subconscious nature of vestibular processing in that it does not induce a different internal brain state. On the medium-term time scale, I investigated whole-brain structural changes as a result of gravity. Astronauts that travel to space for extended periods of time experience several physiological symptoms also affecting the fluid exchange of the brain. To characterise if these fluid exchanges also affect size of the spaces around brain blood vessels (perivascular spaces), I developed a semi-automatic detection pipeline which requires only one type of structural MR image. I found that space travellers have enlarged perivascular spaces even before their mission, when compared to a control population. These spaces were to a small extend further increased shortly after a long duration space flight of 6 months. Astronaut training thus contributes to structural changes in the whole brain in combination with long-duration space flight. This further suggests that additional contextual factors such as sleep quality should be considered in the future. Overall, in my thesis I show that investigating the whole brain during different vestibular modulations provides additional and novel insights about the underlying neural processes. I found that long-term vestibular states have an impact on functional networks, whilst short-term vestibular modulations do not seem to impact functional network organisation. In addition, I quantified the structural impact of microgravity and astronaut training in the whole brain using a new analysis pipeline. In the future, I expect that new advancements in the field of neuroimaging analysis, such as high sampling of individuals and dynamic network analysis will advance the field. This will potentially also provide new means to monitor disease progression or intervention success

Foundations for Safety-Critical on-Demand Medical Systems

In current medical practice, therapy is delivered in critical care environments (e.g., the ICU) by clinicians who manually coordinate sets of medical devices: The clinicians will monitor patient vital signs and then reconfigure devices (e.g., infusion pumps) as is needed. Unfortunately, the current state of practice is both burdensome on clinicians and error prone. Recently, clinicians have been speculating whether medical devices supporting ``plug & play interoperability\u27\u27 would make it easier to automate current medical workflows and thereby reduce medical errors, reduce costs, and reduce the burden on overworked clinicians. This type of plug & play interoperability would allow clinicians to attach devices to a local network and then run software applications to create a new medical system ``on-demand\u27\u27 which automates clinical workflows by automatically coordinating those devices via the network. Plug & play devices would let the clinicians build new medical systems compositionally. Unfortunately, safety is not considered a compositional property in general. For example, two independently ``safe\u27\u27 devices may interact in unsafe ways. Indeed, even the definition of ``safe\u27\u27 may differ between two device types. In this dissertation we propose a framework and define some conditions that permit reasoning about the safety of plug & play medical systems. The framework includes a logical formalism that permits formal reasoning about the safety of many device combinations at once, as well as a platform that actively prevents unintended timing interactions between devices or applications via a shared resource such as a network or CPU. We describe the various pieces of the framework, report some experimental results, and show how the pieces work together to enable the safety assessment of plug & play medical systems via a two case-studies

Prediction and causal inference in the transition from acute to chronic low back pain

The overarching aim of this thesis was to enhance our understanding of the neurobiological risk factors associated with the transition from acute to chronic Low back pain (LBP). To achieve this aim, the Understanding persistent Pain Where it ResiDes (UPWaRD) study was conducted. In this thesis, six chapters describe the background, methods, and results of the UPWaRD study. Chapter 2 describes the protocol, published ‘a priori’ for developing a multivariable prediction model, including candidate predictors selected from the neurobiological (e.g. sensorimotor cortical excitability assessed by sensory and motor evoked potentials, Brain Derived Neurotrophic Factor [BDNF] genotype), psychological (e.g. depression and anxiety), symptom-related (e.g. LBP history) and demographic domains. Chapter 3 builds on the study protocol in the form of a cohort profile, describing baseline characteristics of 120 people experiencing an acute LBP episode and 57 pain-free control participants that form the UPWaRD cohort. Chapter 4 reports the results of the multivariable prediction model developed in 120 people experiencing acute LBP. To further understand the importance of these prognostic factors we developed a causal model of chronic LBP using directed acyclic graphs. The methodology and statistical analysis plan for drawing causal inferences, thus transparently reporting our causal assumptions, are reported in Chapter 5. Chapter 6 then provides the first evidence that low sensory cortex excitability during an acute LBP episode is a causal mechanism underpinning the development of chronic LBP. Finally, in Chapter 7, we report the results of a proteomic analysis, using hydrophobic interaction chromatography and electrospray ionization tandem mass spectrometry. Taken together this thesis makes an extensive and original contribution to our understanding of neurobiological risk factors involved in the transition from acute to chronic LBP. Not only is the inclusion of neurobiological prognostic factors in multivariable clinical prediction models a promising direction for future research that aims to identify people at high risk of poor outcome, but low sensory cortex excitability during acute LBP may be a promising causal mechanism that future treatments could target during acute LBP in the hope of expediting recovery and preventing the development of chronic LBP. Further, this thesis provides some of the earliest evidence to suggest sex-specific differential expression of proteins, measured from human serum, contributes to recovery status at three-month follow-up. This work provides foundational evidence for future research exploring strategies targeting distinct immune system processes in males and females that may interfere with the transition from acute to chronic LBP

INDIVIDUALIZED COGNITIVE DECLINE AND THE IMPACT OF GUT MICROBIOME COMPOSITION.

The U.S. population is aging at its greatest rate in history. An older average population will increase the number of age-related cognitive issues. Elucidation of factors that contribute to decline with age and methods to prevent or decrease the incidence of cognitive dysfunction in the aging population is vital to offset the impact of the age shift. Validation of tests to identify and predict decline is the first step, but must be paired with an increased understanding of the inter- and intra-individual differences that influence cognitive decline. One difference, gut microbiome diversity, changes within the person across their lifespan and varies among individuals. An individual’s gut microflora can significantly influence gut-brain communication, brain function, and behavior. The study was focused on identification and prediction of cognitive decline using CANTAB and visual ERP as well as exploring the relation between gut-microbiome diversity and cognitive performance. Participants underwent tests to evaluate cognitive decline over time: the MoCA, a CANTAB battery for behavioral cognitive assessment, and an electrophysiological evaluation via a passive oddball paradigm and an active detection task. The role of microbiome diversity in cognitive decline was investigated, ERP measures were validated against CANTAB measures, the predictive relation between MoCA and future cognitive outcomes were characterized, and the utility of ERP PCA factors and CANTAB outcomes to predict future ERP and CANTAB performance were shown. Three CANTAB measures (RTI, SWM, and RVP) were independently confirmed to significantly relate to selected ERP measures in both the active detection and the passive oddball tasks. Baseline MoCA score and change in MoCA score significantly predicted outcomes in the CANTAB battery and ERP tasks at follow-up. The study also included the design and implementation of novel methodology with two-step temporospatial PCA to successfully predict future performance on ERP with baseline performance on the same task, which, to this author’s knowledge, is the first known use of this method for this purpose. Finally, significant relations between gut-microbiome diversity and healthy cognitive function were revealed, where lower microbial diversity significantly relates to poorer cognitive performance on both behavioral (CANTAB) and electrophysiological (ERP) measures.Doctor of Philosoph

Using EEG to investigate premature aging and cognitive decline in adults with Down's Syndrome

Down’s Syndrome (DS) is a genetic disorder associated with intellectual disability, accelerated aging and a propensity for early-onset Alzheimer’s disease (AD). Beta-amyloid plaques are one of the pathological hallmarks of AD, and also a common characteristic of the older DS brain. AD treatment trials are now moving towards administration of the intervention at preclinical stages, with the goal of preventing cognitive decline in the first place, rather than trying to halt or reverse existing pathology. Consequently, it has become essential to develop biomarkers of AD, which can: 1. Predict clinical changes and 2. Track the effectiveness of putative preventative treatments. The strong association between DS and AD means that this research is particularly important for people with DS and it presents a high-risk group for exploring predictive biomarkers. Electroencephalography (EEG) is a non-invasive and inexpensive measure of cortical activity, which is being evaluated with the typically developing (TD) population as a potential biomarker of AD. This thesis aims to evaluate EEG as a potential predictor of cognitive decline associated with DS-AD. There are several potential EEG measures that could be explored. Following a review of the literature, the predictive potential of the following event-related potentials (ERPs): mismatch negativity (MMN) and P300 (P3a and P3b), were chosen for exploration with cross-sectional and longitudinal investigations. The thesis begins by exploring how the ERPs differ for a cross-section of 36 adults with DS and 39 age- and gender-matched TD controls. As expected, the MMN waveform was smaller for adults with DS than TD controls. However, the P3b waveform was predominantly absent for adults with DS, whilst the P3a response was significantly enlarged. The P3a response was also enlarged for the adults with DS who scored lower on a neuropsychological measure. The neuropsychological measure indexes frontal functions, which are compromised early in DS-AD. This experiment also provided evidence that MMN was related to age in DS, with increasing latencies and decreasing amplitudes for older participants. The differences in MMN amplitude between the groups (DS, TD) were isolated to the older adults. These findings lend support to the premature aging hypothesis of DS. The thesis also included a longitudinal follow-up in which 34 adults with DS underwent a repeated cognitive examination one year after their EEG and initial cognitive assessment. The analyses found that adults with DS who had lower MMN amplitudes at the initial assessment were more likely to decline at the cognitive follow-up. This finding suggests that MMN may be a potentially useful clinical tool for predicting the cognitive decline associated with DS-AD.Medical Research Council Studentship (3.5 years support: £57078) The Health Foundation (University fees: £18944) Alzheimer’s Research UK Scholarship (pilot phase funding: £4570) Addenbrooke’s Charitable Trust (cross-sectional phase funding: £10000) Marmaduke Shield Fund (equipment fund: £1786) Fearnsides Fund (conference support: £670

Neurophysiological evidence of sensory and cognitive deficits in dyslexia.

For those engaged in trying to understand the cause of dyslexia, these are interesting times. There is increasing evidence that dyslexia may result from a deficit in the brain's ability to process general visual and auditory information, which may subsequently contribute to observed language difficulties. While some suggest that this processing deficit is confined to lower perceptual levels, others propose that it extends to higher cognitive levels of attention and learning. So far there is surprisingly little evidence of research wherein both modalities, both processing levels and various stimulus features have been tested in the same set of dyslexics using electrophysiological measures. This was the purpose of this research. In four studies, event related potentials were recorded from dyslexic and control brains during the non-attentive and attentive discrimination of various visual and auditory stimuli. Average dyslexic-control ERP comparisons were made for sensory N 1 and MMN waves in the passive, and cognitive P2, N2 and P3 waves in the active response conditions. Dyslexics had attenuated MMNs during the pre-attentive discrimination of changes in peripheral visual field, auditory frequency and rapid auditory sequences but not auditory duration. Moreover, dyslexics had abnormal P2 or P3 waves during the attentive discrimination of all visual and auditory stimuli. Finally, the previously attenuated MMN to frequency discrimination was enhanced after attentive practice. The feature-specific MMN abnormalities suggest a highly selective, multi-modal, perceptual dysfunction in dyslexics, as predicted by the pan-sensory deficit theory. However, the ubiquitous task-related P2 and P3 abnormalities suggest that their deficits also extend to higher cognitive domains, as predicted by the automatization/cerebellar deficit theory. The subsequent MMN enhancement suggests practice-induced improvements in their perceptual acuity. These findings suggest that dyslexia is a multilevel syndrome: the same dyslexics have problems in both domains: visual and auditory, and at both processing levels: sensory and cognitive